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11.7k
| library_name
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numen-tech/Mistral-7B-Instruct-v0.3-w3a16g40sym
|
numen-tech
| 2024-05-30T14:32:48Z | 0 | 0 | null |
[
"arxiv:2308.13137",
"license:apache-2.0",
"region:us"
] | null | 2024-05-30T14:11:38Z |
---
license: apache-2.0
---
3-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
|
phongtintruong/misjava-api-053024
|
phongtintruong
| 2024-05-30T14:31:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T14:31:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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#### Summary
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
QuantFactory/AlchemistCoder-L-7B-GGUF
|
QuantFactory
| 2024-05-30T14:31:03Z | 34 | 1 | null |
[
"gguf",
"code generation",
"text-generation",
"arxiv:2405.19265",
"base_model:internlm/AlchemistCoder-L-7B",
"base_model:quantized:internlm/AlchemistCoder-L-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-30T12:18:06Z |
---
license: apache-2.0
tags:
- code generation
base_model: internlm/AlchemistCoder-L-7B
pipeline_tag: text-generation
---
# QuantFactory/AlchemistCoder-L-7B-GGUF
This is quantized version of [internlm/AlchemistCoder-L-7B](https://huggingface.co/internlm/AlchemistCoder-L-7B) created using llama.cpp
## Model Description: AlchemistCoder
[[📃 Paper](https://arxiv.org/abs/2405.19265)]
[[🌐 Project Page](https://internlm.github.io/AlchemistCoder/)]
## ✨ Highlights
> **Abstract:** *Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance. However, previous Code LLMs are typically fine-tuned on single-source data with limited quality and diversity, which may insufficiently elicit the potential of pre-trained Code LLMs. In this paper, we present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. To achieve this, we pioneer to unveil inherent conflicts among the various styles and qualities in multi-source code corpora and introduce data-specific prompts with hindsight relabeling, termed AlchemistPrompts, to harmonize different data sources and instruction-response pairs. Additionally, we propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review. Extensive experiments demonstrate that AlchemistCoder holds a clear lead among all models of the same size (6.7B/7B) and rivals or even surpasses larger models (15B/33B/70B), showcasing the efficacy of our method in refining instruction-following capabilities and advancing the boundaries of code intelligence.*
- **AlchemistPrompts**: Designed as data-specific prompts for harmonizing inherent conflicts in multi-source data and mitigating the instruction/response misalignment at a fined-grained level.
- **Code Comprehenstion Tasks**: Sourced from the process of data construction, consisting of instruction evolution, data filtering, and code review.
- **Harmonized Multi-source Data**: Instruction tuned on 200M tokens, including 6 types of high-quality data.
- **Superior Model Performance**: Surpassing all the open-source models of the same size (6.7/7B), and rivaling or even beating larger models (15B/33B/70B/ChatGPT) on 6 code benchmarks.
- **Advanced generic capabilities**: Demonstrated by the significant improvements on MMLU, BBH, and GSM8K.
## 🚀 Quick Start
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("internlm/AlchemistCoder-L-7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("internlm/AlchemistCoder-L-7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
model = model.eval()
input_text = "Implement the Dijkstra algorithm in Python"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## 🧪 Evaluation and Fine-tune
Please refer to [**AlchemistCoder**](https://github.com/InternLM/AlchemistCoder) and [**InternLM**](https://github.com/InternLM/InternLM/tree/main).
## 😃 Acknowledgments
*AlchemistCoder* is built with [**InternLM**](https://github.com/InternLM) and [**OpenCompass**](https://github.com/open-compass). Thanks for their awesome work!
|
failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5
|
failspy
| 2024-05-30T14:27:00Z | 4,431 | 41 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-28T17:51:28Z |
---
library_name: transformers
license: llama3
---
# Llama-3-70B-Instruct-abliterated-v3.5 Model Card
[My original Jupyter "cookbook" to replicate the methodology can be found here](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
[My personal library o' code used](https://github.com/FailSpy/abliterator) (WIP, looking to improve and generalize)
This is [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
## V3.5?
Second try. I felt that the V3 methodology of 70B wasn't well applied, and u/Nexesenex on reddit kinda confirmed my suspicions. So go blame them. :P
This one has only a single layer modified(!) and that seems to have completely eliminated moralizing disclaimers.
I hope you'll find this model better than 70B-V3! As well, this also fixes the tokenizer.
## Hang on, "abliteration"? Orthogonalization? Ablation? What is this?
TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out.
**TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes.
Ablate + obliterated = Abliterated
Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization.
## A little more on the methodology, and why this is interesting
To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt.
Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights.
> Why this over fine-tuning?
Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage.
As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.)
Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques.
It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa.
I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity.
> Okay, fine, but why V3? There's no V2 70B?
Well, I released a V2 a while back for 8B under Cognitive Computations.
It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model.
I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations.
So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.)
## Quirkiness awareness notice
This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects.
If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
|
JiazhenLiu01/llama3-finetuned-kto-0.4
|
JiazhenLiu01
| 2024-05-30T14:26:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:adapter:meta-llama/Meta-Llama-3-8B",
"region:us"
] | null | 2024-05-30T14:16:02Z |
---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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### Framework versions
- PEFT 0.10.0
|
JohnDoe70/SQAA_Instruct_Phi3_v1
|
JohnDoe70
| 2024-05-30T14:24:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T14:24:32Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
JiazhenLiu01/llama3-finetuned-kto-0.1
|
JiazhenLiu01
| 2024-05-30T14:22:17Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:adapter:meta-llama/Meta-Llama-3-8B",
"region:us"
] | null | 2024-05-30T14:16:11Z |
---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.10.0
|
hdve/Qwen-Qwen1.5-7B-1717078368
|
hdve
| 2024-05-30T14:16:42Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-30T14:12:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## More Information [optional]
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## Model Card Contact
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|
JiazhenLiu01/llama3-finetuned-kto-0.7
|
JiazhenLiu01
| 2024-05-30T14:12:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:adapter:meta-llama/Meta-Llama-3-8B",
"region:us"
] | null | 2024-05-30T14:11:34Z |
---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
AIRider/Meta-Llama-3-8B-Q4_K_M-GGUF
|
AIRider
| 2024-05-30T14:11:52Z | 5 | 1 | null |
[
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:llama3",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-25T05:12:05Z |
---
language:
- en
license: llama3
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\
\ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\
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\ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\
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we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\
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\ 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms,\
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\ results of the Llama Materials to improve any other large language model (excluding\
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\ UN Convention on Contracts for the International Sale of Goods does not apply\
\ to this Agreement. The courts of California shall have exclusive jurisdiction\
\ of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use\
\ Policy\nMeta is committed to promoting safe and fair use of its tools and features,\
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\ Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n\
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\ materials, or failure to employ legally required age-gating in connection with\
\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
\ other criminal activity\n 2. Engage in, promote, incite, or facilitate the\
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\ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful\
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\ practices\n 5. Collect, process, disclose, generate, or infer health, demographic,\
\ or other sensitive personal or private information about individuals without rights\
\ and consents required by applicable laws\n 6. Engage in or facilitate any action\
\ or generate any content that infringes, misappropriates, or otherwise violates\
\ any third-party rights, including the outputs or results of any products or services\
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\ of malicious code, malware, computer viruses or do anything else that could disable,\
\ overburden, interfere with or impair the proper working, integrity, operation\
\ or appearance of a website or computer system\n2. Engage in, promote, incite,\
\ facilitate, or assist in the planning or development of activities that present\
\ a risk of death or bodily harm to individuals, including use of Meta Llama 3 related\
\ to the following:\n 1. Military, warfare, nuclear industries or applications,\
\ espionage, use for materials or activities that are subject to the International\
\ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
\ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\
\ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\
\ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\
\ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\
\ content intended to incite or promote violence, abuse, or any infliction of bodily\
\ harm to an individual\n3. Intentionally deceive or mislead others, including use\
\ of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering\
\ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\
\ or furthering defamatory content, including the creation of defamatory statements,\
\ images, or other content\n 3. Generating, promoting, or further distributing\
\ spam\n 4. Impersonating another individual without consent, authorization,\
\ or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are\
\ human-generated\n 6. Generating or facilitating false online engagement, including\
\ fake reviews and other means of fake online engagement\n4. Fail to appropriately\
\ disclose to end users any known dangers of your AI system\nPlease report any violation\
\ of this Policy, software “bug,” or other problems that could lead to a violation\
\ of this Policy through one of the following means:\n * Reporting issues with\
\ the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n\
\ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\
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extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
# AIRider/Meta-Llama-3-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo AIRider/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo AIRider/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m meta-llama-3-8b-q4_k_m.gguf -n 128
```
|
ahmedesmail16/Train-Test-Augmentation-V5-beit-base
|
ahmedesmail16
| 2024-05-30T14:11:18Z | 200 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/beit-base-patch16-224-pt22k-ft22k",
"base_model:finetune:microsoft/beit-base-patch16-224-pt22k-ft22k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-05-30T07:48:46Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Train-Test-Augmentation-V5-beit-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Train-Test-Augmentation-V5-beit-base
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6899
- Accuracy: 0.8442
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0473 | 1.0 | 55 | 0.8312 | 0.7759 |
| 0.3767 | 2.0 | 110 | 0.5476 | 0.8336 |
| 0.176 | 3.0 | 165 | 0.5248 | 0.8256 |
| 0.07 | 4.0 | 220 | 0.5597 | 0.8527 |
| 0.043 | 5.0 | 275 | 0.5707 | 0.8472 |
| 0.0272 | 6.0 | 330 | 0.6225 | 0.8264 |
| 0.0168 | 7.0 | 385 | 0.5721 | 0.8553 |
| 0.0076 | 8.0 | 440 | 0.5967 | 0.8608 |
| 0.006 | 9.0 | 495 | 0.7036 | 0.8272 |
| 0.007 | 10.0 | 550 | 0.7167 | 0.8400 |
| 0.0048 | 11.0 | 605 | 0.6734 | 0.8506 |
| 0.0023 | 12.0 | 660 | 0.7424 | 0.8332 |
| 0.0032 | 13.0 | 715 | 0.7283 | 0.8340 |
| 0.002 | 14.0 | 770 | 0.6805 | 0.8502 |
| 0.0021 | 15.0 | 825 | 0.6899 | 0.8442 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.15.2
|
Mihaj/wav2vec2-large-mms-1b-livvi-karelian-CodeSwitching-with-all-aug
|
Mihaj
| 2024-05-30T14:09:41Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T14:09:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Aremaki/QAmembert_finetuned
|
Aremaki
| 2024-05-30T14:08:58Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T14:08:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lynn54040/corgy_dog_LoRA
|
lynn54040
| 2024-05-30T14:07:20Z | 2 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-05-30T12:52:23Z |
---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK dog
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - lynn54040/corgy_dog_LoRA
<Gallery />
## Model description
These are lynn54040/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](lynn54040/corgy_dog_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
Ramikan-BR/tinyllama-coder-py-4bit-v4
|
Ramikan-BR
| 2024-05-30T14:05:58Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"base_model:quantized:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-22T12:13:30Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** Ramikan-BR
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JiazhenLiu01/llama3-finetuned-kto-1.0
|
JiazhenLiu01
| 2024-05-30T14:04:05Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:adapter:meta-llama/Meta-Llama-3-8B",
"region:us"
] | null | 2024-05-30T14:03:04Z |
---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-5_0bpw_exl2
|
Zoyd
| 2024-05-30T14:04:04Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:llama3",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-05-30T12:14:03Z |
---
language:
- en
license: llama3
tags:
- moe
model-index:
- name: L3-SnowStorm-v1.15-4x8B-A
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 62.2
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 81.09
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.11
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **5.0 bpw**) made with ExLlamaV2 v0.1.1
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_2bpw_exl2)**</center> | <center>7777 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_5bpw_exl2)**</center> | <center>8520 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_0bpw_exl2)**</center> | <center>9941 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_5bpw_exl2)**</center> | <center>11366 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_75bpw_exl2)**</center> | <center>12066 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_0bpw_exl2)**</center> | <center>12789 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_25bpw_exl2)**</center> | <center>13504 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-5_0bpw_exl2)**</center> | <center>15640 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_0bpw_exl2)**</center> | <center>18586 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_5bpw_exl2)**</center> | <center>20007 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-8_0bpw_exl2)**</center> | <center>24101 MB</center> | <center>8</center> |
<style>
.image-container {
position: relative;
display: inline-block;
}
.image-container img {
display: block;
border-radius: 10px;
box-shadow: 0 0 1px rgba(0, 0, 0, 0.3);
}
.image-container::before {
content: "";
position: absolute;
top: 0px;
left: 20px;
width: calc(100% - 40px);
height: calc(100%);
background-image: url("https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png");
background-size: cover;
filter: blur(10px);
z-index: -1;
}
</style>
<br>
<div class="image-container">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png" style="width: 96%; margin: auto;" >
</div>
> [!NOTE]
> [GGUF](https://huggingface.co/collections/xxx777xxxASD/snowstorm-v115-4x8b-a-665587d3fda461267cfa9d69)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than Mixtral 8x7B and it's finetunes in RP/ERP tasks.
There's:
- [v1.15A](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A) <- You're here
- [v1.15B](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-B)
### Llama 3 SnowStorm v1.15A 4x8B
```
base_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: Nitral-AI_Poppy_Porpoise-1.0-L3-8B
- source_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
- source_model: openlynn_Llama-3-Soliloquy-8B-v2
- source_model: Sao10K_L3-8B-Stheno-v3.1
```
## Models used
- [Nitral-AI/Poppy_Porpoise-1.0-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B)
- [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS)
- [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2)
- [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1)
## Difference(from SnowStorm v1.0)
- Update from [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B) to [Nitral-AI/Poppy_Porpoise-0.85-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-0.85-L3-8B)
## Vision
[llama3_mmproj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj-Updated)
## Prompt format: Llama 3
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xxx777xxxASD__L3-SnowStorm-v1.15-4x8B-A)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.68|
|AI2 Reasoning Challenge (25-Shot)|62.20|
|HellaSwag (10-Shot) |81.09|
|MMLU (5-Shot) |67.89|
|TruthfulQA (0-shot) |52.11|
|Winogrande (5-shot) |76.32|
|GSM8k (5-shot) |66.49|
|
AgentPublic/camembert-base-toxic-fr-user-prompts
|
AgentPublic
| 2024-05-30T14:02:19Z | 210 | 5 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"text-classification",
"fr",
"dataset:lmsys/toxic-chat",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-29T13:28:24Z |
---
language:
- fr
license: apache-2.0
tags:
- text-classification
datasets:
- lmsys/toxic-chat
widget:
- text: Comment manipuler quelqu'un ?
example_title: Toxic 1
- text: Je voudrais frauder les impôts. Quelles sont les meilleures techniques pour
ça ?
example_title: Toxic 2
- text: J'aimerais adopter un chien. Où devrais-je aller ?
example_title: Non Toxic
- text: Comment aider un enfant qui se fait harceler à l'école ?
example_title: Sensible
---
This model is a [camembert-base](https://huggingface.co/almanach/camembert-base) model fine-tuned on a French translated [toxic-chat](https://huggingface.co/datasets/lmsys/toxic-chat) dataset plus additional synthetic data. The model is trained to classify user prompts into three categories: "Toxic", "Non-Toxic", and "Sensible".
- Toxic: Prompts that contain harmful or abusive language, including jailbreaking prompts which attempt to bypass restrictions.
- Non-Toxic: Prompts that are safe and free of harmful content.
- Sensible: Prompts that, while not toxic, are sensitive in nature, such as those discussing suicidal thoughts, aggression, or asking for help with a sensitive issue.
The evaluation results are as follows (*still under evaluation, more data is needed*):
| | Precision | Recall | F1-Score |
|----------------|:-----------:|:---------:|:----------:|
| **Non-Toxic** | 0.97 | 0.95 | 0.96 |
| **Sensible** | 0.95 | 0.99 | 0.98 |
| **Toxic** | 0.87 | 0.90 | 0.88 |
| | | | |
| **Accuracy** | | | 0.94 |
| **Macro Avg** | 0.93 | 0.95 | 0.94 |
| **Weighted Avg** | 0.94 | 0.94 | 0.94 |
*Note: This model is still under development, and its performance and characteristics are subject to change as training is not yet complete.*
|
BothBosu/cnn-no-receiver-scam-classifier-v1.0
|
BothBosu
| 2024-05-30T14:02:03Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pytorch_model_hub_mixin",
"model_hub_mixin",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T14:01:59Z |
---
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
|
DavidLanz/llama2_7b_taiwan_btc_qlora
|
DavidLanz
| 2024-05-30T14:00:25Z | 2 | 3 |
peft
|
[
"peft",
"safetensors",
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"en",
"base_model:DavidLanz/Llama2-tw-7B-v2.0.1-chat",
"base_model:adapter:DavidLanz/Llama2-tw-7B-v2.0.1-chat",
"license:llama2",
"region:us"
] |
text-generation
| 2024-04-01T06:50:03Z |
---
language:
- en
license: llama2
library_name: peft
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
base_model: DavidLanz/Llama2-tw-7B-v2.0.1-chat
model_name: Llama 2 7B Chat
inference: false
model_creator: Meta Llama 2
model_type: llama
pipeline_tag: text-generation
quantized_by: QLoRA
---
# Model Card for Model ID
This PEFT weight is for predicting BTC price.
Disclaimer: This model is for a time series problem on LLM performance, and it's not for investment advice; any prediction results are not a basis for investment reference.
## Model Details
Training data source: BTC/USD provided by [Binance](https://www.binance.com/).
### Model Description
This repo contains QLoRA format model files for [Meta's Llama 2 7B-chat](https://huggingface.co/DavidLanz/Llama2-tw-7B-v2.0.1-chat).
## Uses
```python
import torch
from peft import LoraConfig, PeftModel
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
TextStreamer,
pipeline,
logging,
)
device_map = {"": 0}
use_4bit = True
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4"
use_nested_quant = False
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
bnb_config = BitsAndBytesConfig(
load_in_4bit=use_4bit,
bnb_4bit_quant_type=bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=use_nested_quant,
)
based_model_path = "DavidLanz/Llama2-tw-7B-v2.0.1-chat"
adapter_path = "DavidLanz/llama2_7b_taiwan_btc_qlora"
base_model = AutoModelForCausalLM.from_pretrained(
based_model_path,
low_cpu_mem_usage=True,
# load_in_4bit=True,
return_dict=True,
quantization_config=bnb_config,
torch_dtype=torch.float16,
device_map=device_map,
)
model = PeftModel.from_pretrained(base_model, adapter_path)
tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
from transformers import pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{
"role": "system",
"content": "你是一位專業的BTC虛擬貨幣分析預測BTC的收盤價格。",
},
{"role": "user", "content": "昨日開盤價為64437.18，最高價為64960.37，最低價為62953.90，收盤價為64808.35，交易量為808273.27。請預測今日BTC的收盤價?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
### Framework versions
- PEFT 0.10.0
|
Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_5bpw_exl2
|
Zoyd
| 2024-05-30T13:56:08Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:llama3",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-30T10:56:33Z |
---
language:
- en
license: llama3
tags:
- moe
model-index:
- name: L3-SnowStorm-v1.15-4x8B-A
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 62.2
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 81.09
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.11
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **3.5 bpw**) made with ExLlamaV2 v0.1.1
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_2bpw_exl2)**</center> | <center>7777 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_5bpw_exl2)**</center> | <center>8520 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_0bpw_exl2)**</center> | <center>9941 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_5bpw_exl2)**</center> | <center>11366 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_75bpw_exl2)**</center> | <center>12066 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_0bpw_exl2)**</center> | <center>12789 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_25bpw_exl2)**</center> | <center>13504 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-5_0bpw_exl2)**</center> | <center>15640 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_0bpw_exl2)**</center> | <center>18586 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_5bpw_exl2)**</center> | <center>20007 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-8_0bpw_exl2)**</center> | <center>24101 MB</center> | <center>8</center> |
<style>
.image-container {
position: relative;
display: inline-block;
}
.image-container img {
display: block;
border-radius: 10px;
box-shadow: 0 0 1px rgba(0, 0, 0, 0.3);
}
.image-container::before {
content: "";
position: absolute;
top: 0px;
left: 20px;
width: calc(100% - 40px);
height: calc(100%);
background-image: url("https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png");
background-size: cover;
filter: blur(10px);
z-index: -1;
}
</style>
<br>
<div class="image-container">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png" style="width: 96%; margin: auto;" >
</div>
> [!NOTE]
> [GGUF](https://huggingface.co/collections/xxx777xxxASD/snowstorm-v115-4x8b-a-665587d3fda461267cfa9d69)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than Mixtral 8x7B and it's finetunes in RP/ERP tasks.
There's:
- [v1.15A](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A) <- You're here
- [v1.15B](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-B)
### Llama 3 SnowStorm v1.15A 4x8B
```
base_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: Nitral-AI_Poppy_Porpoise-1.0-L3-8B
- source_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
- source_model: openlynn_Llama-3-Soliloquy-8B-v2
- source_model: Sao10K_L3-8B-Stheno-v3.1
```
## Models used
- [Nitral-AI/Poppy_Porpoise-1.0-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B)
- [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS)
- [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2)
- [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1)
## Difference(from SnowStorm v1.0)
- Update from [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B) to [Nitral-AI/Poppy_Porpoise-0.85-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-0.85-L3-8B)
## Vision
[llama3_mmproj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj-Updated)
## Prompt format: Llama 3
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xxx777xxxASD__L3-SnowStorm-v1.15-4x8B-A)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.68|
|AI2 Reasoning Challenge (25-Shot)|62.20|
|HellaSwag (10-Shot) |81.09|
|MMLU (5-Shot) |67.89|
|TruthfulQA (0-shot) |52.11|
|Winogrande (5-shot) |76.32|
|GSM8k (5-shot) |66.49|
|
Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_5bpw_exl2
|
Zoyd
| 2024-05-30T13:55:32Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:llama3",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-30T13:07:06Z |
---
language:
- en
license: llama3
tags:
- moe
model-index:
- name: L3-SnowStorm-v1.15-4x8B-A
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 62.2
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 81.09
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.11
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **6.5 bpw**) made with ExLlamaV2 v0.1.1
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_2bpw_exl2)**</center> | <center>7777 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_5bpw_exl2)**</center> | <center>8520 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_0bpw_exl2)**</center> | <center>9941 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_5bpw_exl2)**</center> | <center>11366 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_75bpw_exl2)**</center> | <center>12066 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_0bpw_exl2)**</center> | <center>12789 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_25bpw_exl2)**</center> | <center>13504 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-5_0bpw_exl2)**</center> | <center>15640 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_0bpw_exl2)**</center> | <center>18586 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_5bpw_exl2)**</center> | <center>20007 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-8_0bpw_exl2)**</center> | <center>24101 MB</center> | <center>8</center> |
<style>
.image-container {
position: relative;
display: inline-block;
}
.image-container img {
display: block;
border-radius: 10px;
box-shadow: 0 0 1px rgba(0, 0, 0, 0.3);
}
.image-container::before {
content: "";
position: absolute;
top: 0px;
left: 20px;
width: calc(100% - 40px);
height: calc(100%);
background-image: url("https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png");
background-size: cover;
filter: blur(10px);
z-index: -1;
}
</style>
<br>
<div class="image-container">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png" style="width: 96%; margin: auto;" >
</div>
> [!NOTE]
> [GGUF](https://huggingface.co/collections/xxx777xxxASD/snowstorm-v115-4x8b-a-665587d3fda461267cfa9d69)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than Mixtral 8x7B and it's finetunes in RP/ERP tasks.
There's:
- [v1.15A](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A) <- You're here
- [v1.15B](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-B)
### Llama 3 SnowStorm v1.15A 4x8B
```
base_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: Nitral-AI_Poppy_Porpoise-1.0-L3-8B
- source_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
- source_model: openlynn_Llama-3-Soliloquy-8B-v2
- source_model: Sao10K_L3-8B-Stheno-v3.1
```
## Models used
- [Nitral-AI/Poppy_Porpoise-1.0-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B)
- [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS)
- [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2)
- [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1)
## Difference(from SnowStorm v1.0)
- Update from [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B) to [Nitral-AI/Poppy_Porpoise-0.85-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-0.85-L3-8B)
## Vision
[llama3_mmproj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj-Updated)
## Prompt format: Llama 3
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xxx777xxxASD__L3-SnowStorm-v1.15-4x8B-A)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.68|
|AI2 Reasoning Challenge (25-Shot)|62.20|
|HellaSwag (10-Shot) |81.09|
|MMLU (5-Shot) |67.89|
|TruthfulQA (0-shot) |52.11|
|Winogrande (5-shot) |76.32|
|GSM8k (5-shot) |66.49|
|
Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_5bpw_exl2
|
Zoyd
| 2024-05-30T13:54:52Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:llama3",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-30T10:25:28Z |
---
language:
- en
license: llama3
tags:
- moe
model-index:
- name: L3-SnowStorm-v1.15-4x8B-A
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 62.2
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 81.09
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.11
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **2.5 bpw**) made with ExLlamaV2 v0.1.1
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_2bpw_exl2)**</center> | <center>7777 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_5bpw_exl2)**</center> | <center>8520 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_0bpw_exl2)**</center> | <center>9941 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_5bpw_exl2)**</center> | <center>11366 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_75bpw_exl2)**</center> | <center>12066 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_0bpw_exl2)**</center> | <center>12789 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_25bpw_exl2)**</center> | <center>13504 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-5_0bpw_exl2)**</center> | <center>15640 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_0bpw_exl2)**</center> | <center>18586 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_5bpw_exl2)**</center> | <center>20007 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-8_0bpw_exl2)**</center> | <center>24101 MB</center> | <center>8</center> |
<style>
.image-container {
position: relative;
display: inline-block;
}
.image-container img {
display: block;
border-radius: 10px;
box-shadow: 0 0 1px rgba(0, 0, 0, 0.3);
}
.image-container::before {
content: "";
position: absolute;
top: 0px;
left: 20px;
width: calc(100% - 40px);
height: calc(100%);
background-image: url("https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png");
background-size: cover;
filter: blur(10px);
z-index: -1;
}
</style>
<br>
<div class="image-container">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png" style="width: 96%; margin: auto;" >
</div>
> [!NOTE]
> [GGUF](https://huggingface.co/collections/xxx777xxxASD/snowstorm-v115-4x8b-a-665587d3fda461267cfa9d69)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than Mixtral 8x7B and it's finetunes in RP/ERP tasks.
There's:
- [v1.15A](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A) <- You're here
- [v1.15B](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-B)
### Llama 3 SnowStorm v1.15A 4x8B
```
base_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: Nitral-AI_Poppy_Porpoise-1.0-L3-8B
- source_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
- source_model: openlynn_Llama-3-Soliloquy-8B-v2
- source_model: Sao10K_L3-8B-Stheno-v3.1
```
## Models used
- [Nitral-AI/Poppy_Porpoise-1.0-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B)
- [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS)
- [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2)
- [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1)
## Difference(from SnowStorm v1.0)
- Update from [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B) to [Nitral-AI/Poppy_Porpoise-0.85-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-0.85-L3-8B)
## Vision
[llama3_mmproj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj-Updated)
## Prompt format: Llama 3
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xxx777xxxASD__L3-SnowStorm-v1.15-4x8B-A)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.68|
|AI2 Reasoning Challenge (25-Shot)|62.20|
|HellaSwag (10-Shot) |81.09|
|MMLU (5-Shot) |67.89|
|TruthfulQA (0-shot) |52.11|
|Winogrande (5-shot) |76.32|
|GSM8k (5-shot) |66.49|
|
bartowski/AlchemistCoder-DS-6.7B-GGUF
|
bartowski
| 2024-05-30T13:53:46Z | 138 | 3 | null |
[
"gguf",
"code generation",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-05-30T13:39:45Z |
---
license: apache-2.0
tags:
- code generation
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of AlchemistCoder-DS-6.7B
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3024">b3024</a> for quantization.
Original model: https://huggingface.co/internlm/AlchemistCoder-DS-6.7B
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<｜begin▁of▁sentence｜>{system_prompt}### Instruction:
{prompt}
### Response:
<|EOT|>
### Response:
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [AlchemistCoder-DS-6.7B-Q8_0.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q8_0.gguf) | Q8_0 | 7.16GB | Extremely high quality, generally unneeded but max available quant. |
| [AlchemistCoder-DS-6.7B-Q6_K.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q6_K.gguf) | Q6_K | 5.53GB | Very high quality, near perfect, *recommended*. |
| [AlchemistCoder-DS-6.7B-Q5_K_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q5_K_M.gguf) | Q5_K_M | 4.78GB | High quality, *recommended*. |
| [AlchemistCoder-DS-6.7B-Q5_K_S.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q5_K_S.gguf) | Q5_K_S | 4.65GB | High quality, *recommended*. |
| [AlchemistCoder-DS-6.7B-Q4_K_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q4_K_M.gguf) | Q4_K_M | 4.08GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [AlchemistCoder-DS-6.7B-Q4_K_S.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q4_K_S.gguf) | Q4_K_S | 3.85GB | Slightly lower quality with more space savings, *recommended*. |
| [AlchemistCoder-DS-6.7B-IQ4_XS.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-IQ4_XS.gguf) | IQ4_XS | 3.62GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [AlchemistCoder-DS-6.7B-Q3_K_L.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q3_K_L.gguf) | Q3_K_L | 3.59GB | Lower quality but usable, good for low RAM availability. |
| [AlchemistCoder-DS-6.7B-Q3_K_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q3_K_M.gguf) | Q3_K_M | 3.29GB | Even lower quality. |
| [AlchemistCoder-DS-6.7B-IQ3_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-IQ3_M.gguf) | IQ3_M | 3.11GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [AlchemistCoder-DS-6.7B-Q3_K_S.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q3_K_S.gguf) | Q3_K_S | 2.95GB | Low quality, not recommended. |
| [AlchemistCoder-DS-6.7B-IQ3_XS.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-IQ3_XS.gguf) | IQ3_XS | 2.79GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [AlchemistCoder-DS-6.7B-IQ3_XXS.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-IQ3_XXS.gguf) | IQ3_XXS | 2.58GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [AlchemistCoder-DS-6.7B-Q2_K.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-Q2_K.gguf) | Q2_K | 2.53GB | Very low quality but surprisingly usable. |
| [AlchemistCoder-DS-6.7B-IQ2_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-IQ2_M.gguf) | IQ2_M | 2.36GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [AlchemistCoder-DS-6.7B-IQ2_S.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-IQ2_S.gguf) | IQ2_S | 2.19GB | Very low quality, uses SOTA techniques to be usable. |
| [AlchemistCoder-DS-6.7B-IQ2_XS.gguf](https://huggingface.co/bartowski/AlchemistCoder-DS-6.7B-GGUF/blob/main/AlchemistCoder-DS-6.7B-IQ2_XS.gguf) | IQ2_XS | 2.03GB | Very low quality, uses SOTA techniques to be usable. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/AlchemistCoder-DS-6.7B-GGUF --include "AlchemistCoder-DS-6.7B-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/AlchemistCoder-DS-6.7B-GGUF --include "AlchemistCoder-DS-6.7B-Q8_0.gguf/*" --local-dir AlchemistCoder-DS-6.7B-Q8_0
```
You can either specify a new local-dir (AlchemistCoder-DS-6.7B-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
thewordsmiths/Mistral-7B-v0.3_sft_LoRA_100000
|
thewordsmiths
| 2024-05-30T13:42:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:finetune:mistralai/Mistral-7B-v0.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T13:42:31Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: mistralai/Mistral-7B-v0.3
---
# Uploaded model
- **Developed by:** thewordsmiths
- **License:** apache-2.0
- **Finetuned from model :** mistralai/Mistral-7B-v0.3
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ihebaker10/spark-name-fr-to-en
|
ihebaker10
| 2024-05-30T13:42:32Z | 109 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-25T15:50:11Z |
---
tags:
- generated_from_trainer
model-index:
- name: spark-name-fr-to-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spark-name-fr-to-en
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3373
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 1.7758 |
| No log | 2.0 | 2 | 1.4399 |
| No log | 3.0 | 3 | 1.3373 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.0.1+cpu
- Datasets 2.19.1
- Tokenizers 0.19.1
|
NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-top75-Q8_0-GGUF
|
NikolayKozloff
| 2024-05-30T13:39:03Z | 2 | 1 | null |
[
"gguf",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"base_model:lightblue/suzume-llama-3-8B-multilingual",
"base_model:quantized:lightblue/suzume-llama-3-8B-multilingual",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-30T13:38:35Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
- llama-cpp
- gguf-my-repo
base_model: lightblue/suzume-llama-3-8B-multilingual
model-index:
- name: workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_top75_borda
results: []
---
# NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-top75-Q8_0-GGUF
This model was converted to GGUF format from [`lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75`](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-top75-Q8_0-GGUF --model suzume-llama-3-8b-multilingual-orpo-borda-top75-q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-top75-Q8_0-GGUF --model suzume-llama-3-8b-multilingual-orpo-borda-top75-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m suzume-llama-3-8b-multilingual-orpo-borda-top75-q8_0.gguf -n 128
```
|
failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5-GGUF
|
failspy
| 2024-05-30T13:38:37Z | 1,056 | 22 |
transformers
|
[
"transformers",
"gguf",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-28T18:26:02Z |
---
library_name: transformers
license: llama3
---
# Llama-3-70B-Instruct-abliterated-v3.5 Model Card
[My original Jupyter "cookbook" to replicate the methodology can be found here](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
[My personal library o' code used](https://github.com/FailSpy/abliterator) (WIP, looking to improve and generalize)
This is [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
## V3.5?
Second try. I felt that the V3 methodology of 70B wasn't well applied, and u/Nexesenex on reddit kinda confirmed my suspicions. So go blame them. :P
This one has only a single layer modified(!) and that seems to have greatly reduced moralizing disclaimers.
I hope you'll find this model better than 70B-V3! As well, this also fixes the tokenizer.
## Hang on, "abliteration"? Orthogonalization? Ablation? What is this?
TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out.
**TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes.
Ablate + obliterated = Abliterated
Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization.
## A little more on the methodology, and why this is interesting
To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt.
Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights.
> Why this over fine-tuning?
Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage.
As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.)
Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques.
It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa.
I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity.
> Okay, fine, but why V3? There's no V2 70B?
Well, I released a V2 a while back for 8B under Cognitive Computations.
It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model.
I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations.
So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.)
## Quirkiness awareness notice
This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects.
If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
|
Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_75bpw_exl2
|
Zoyd
| 2024-05-30T13:38:07Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:llama3",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-30T11:28:45Z |
---
language:
- en
license: llama3
tags:
- moe
model-index:
- name: L3-SnowStorm-v1.15-4x8B-A
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 62.2
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 81.09
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.11
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **3.75 bpw**) made with ExLlamaV2 v0.1.1
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_2bpw_exl2)**</center> | <center>7777 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_5bpw_exl2)**</center> | <center>8520 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_0bpw_exl2)**</center> | <center>9941 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_5bpw_exl2)**</center> | <center>11366 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_75bpw_exl2)**</center> | <center>12066 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_0bpw_exl2)**</center> | <center>12789 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_25bpw_exl2)**</center> | <center>13504 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-5_0bpw_exl2)**</center> | <center>15640 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_0bpw_exl2)**</center> | <center>18586 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_5bpw_exl2)**</center> | <center>20007 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-8_0bpw_exl2)**</center> | <center>24101 MB</center> | <center>8</center> |
<style>
.image-container {
position: relative;
display: inline-block;
}
.image-container img {
display: block;
border-radius: 10px;
box-shadow: 0 0 1px rgba(0, 0, 0, 0.3);
}
.image-container::before {
content: "";
position: absolute;
top: 0px;
left: 20px;
width: calc(100% - 40px);
height: calc(100%);
background-image: url("https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png");
background-size: cover;
filter: blur(10px);
z-index: -1;
}
</style>
<br>
<div class="image-container">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png" style="width: 96%; margin: auto;" >
</div>
> [!NOTE]
> [GGUF](https://huggingface.co/collections/xxx777xxxASD/snowstorm-v115-4x8b-a-665587d3fda461267cfa9d69)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than Mixtral 8x7B and it's finetunes in RP/ERP tasks.
There's:
- [v1.15A](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A) <- You're here
- [v1.15B](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-B)
### Llama 3 SnowStorm v1.15A 4x8B
```
base_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: Nitral-AI_Poppy_Porpoise-1.0-L3-8B
- source_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
- source_model: openlynn_Llama-3-Soliloquy-8B-v2
- source_model: Sao10K_L3-8B-Stheno-v3.1
```
## Models used
- [Nitral-AI/Poppy_Porpoise-1.0-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B)
- [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS)
- [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2)
- [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1)
## Difference(from SnowStorm v1.0)
- Update from [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B) to [Nitral-AI/Poppy_Porpoise-0.85-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-0.85-L3-8B)
## Vision
[llama3_mmproj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj-Updated)
## Prompt format: Llama 3
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xxx777xxxASD__L3-SnowStorm-v1.15-4x8B-A)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.68|
|AI2 Reasoning Challenge (25-Shot)|62.20|
|HellaSwag (10-Shot) |81.09|
|MMLU (5-Shot) |67.89|
|TruthfulQA (0-shot) |52.11|
|Winogrande (5-shot) |76.32|
|GSM8k (5-shot) |66.49|
|
Shotaro30678/sentiment-analysis-ncu-chat-bot-half-neutral-data
|
Shotaro30678
| 2024-05-30T13:36:10Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2024-05-30T13:17:09Z |
```
Fine-tuned-half:
precision recall f1-score support
neutral 0.8887 0.9256 0.9068 6321
anger 0.3333 0.1102 0.1656 118
disgust 0.2667 0.0851 0.1290 47
fear 0.2222 0.2353 0.2286 17
happiness 0.6007 0.5358 0.5664 1019
sadness 0.2759 0.2353 0.2540 102
surprise 0.5114 0.3879 0.4412 116
accuracy 0.8381 7740
macro avg 0.4427 0.3593 0.3845 7740
weighted avg 0.8233 0.8381 0.8289 7740
```
|
Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_2bpw_exl2
|
Zoyd
| 2024-05-30T13:34:30Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:llama3",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-30T10:15:24Z |
---
language:
- en
license: llama3
tags:
- moe
model-index:
- name: L3-SnowStorm-v1.15-4x8B-A
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 62.2
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 81.09
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.11
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **2.2 bpw**) made with ExLlamaV2 v0.1.1
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_2bpw_exl2)**</center> | <center>7777 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-2_5bpw_exl2)**</center> | <center>8520 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_0bpw_exl2)**</center> | <center>9941 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_5bpw_exl2)**</center> | <center>11366 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-3_75bpw_exl2)**</center> | <center>12066 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_0bpw_exl2)**</center> | <center>12789 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-4_25bpw_exl2)**</center> | <center>13504 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-5_0bpw_exl2)**</center> | <center>15640 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_0bpw_exl2)**</center> | <center>18586 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-6_5bpw_exl2)**</center> | <center>20007 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/xxx777xxxASD_L3-SnowStorm-v1.15-4x8B-A-8_0bpw_exl2)**</center> | <center>24101 MB</center> | <center>8</center> |
<style>
.image-container {
position: relative;
display: inline-block;
}
.image-container img {
display: block;
border-radius: 10px;
box-shadow: 0 0 1px rgba(0, 0, 0, 0.3);
}
.image-container::before {
content: "";
position: absolute;
top: 0px;
left: 20px;
width: calc(100% - 40px);
height: calc(100%);
background-image: url("https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png");
background-size: cover;
filter: blur(10px);
z-index: -1;
}
</style>
<br>
<div class="image-container">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64f5e51289c121cb864ba464/8eG7GxTvcbxyVFQf5GF3C.png" style="width: 96%; margin: auto;" >
</div>
> [!NOTE]
> [GGUF](https://huggingface.co/collections/xxx777xxxASD/snowstorm-v115-4x8b-a-665587d3fda461267cfa9d69)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than Mixtral 8x7B and it's finetunes in RP/ERP tasks.
There's:
- [v1.15A](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-A) <- You're here
- [v1.15B](https://huggingface.co/xxx777xxxASD/L3-SnowStorm-v1.15-4x8B-B)
### Llama 3 SnowStorm v1.15A 4x8B
```
base_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: Nitral-AI_Poppy_Porpoise-1.0-L3-8B
- source_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1-OAS
- source_model: openlynn_Llama-3-Soliloquy-8B-v2
- source_model: Sao10K_L3-8B-Stheno-v3.1
```
## Models used
- [Nitral-AI/Poppy_Porpoise-1.0-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B)
- [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS)
- [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2)
- [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1)
## Difference(from SnowStorm v1.0)
- Update from [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B) to [Nitral-AI/Poppy_Porpoise-0.85-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-0.85-L3-8B)
## Vision
[llama3_mmproj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj-Updated)
## Prompt format: Llama 3
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xxx777xxxASD__L3-SnowStorm-v1.15-4x8B-A)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.68|
|AI2 Reasoning Challenge (25-Shot)|62.20|
|HellaSwag (10-Shot) |81.09|
|MMLU (5-Shot) |67.89|
|TruthfulQA (0-shot) |52.11|
|Winogrande (5-shot) |76.32|
|GSM8k (5-shot) |66.49|
|
tarikcelik/Phi-3-medium-128k-instruct-Q8_0-GGUF
|
tarikcelik
| 2024-05-30T13:33:55Z | 8 | 0 | null |
[
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"multilingual",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-05-30T13:30:15Z |
---
language:
- multilingual
license: mit
tags:
- nlp
- code
- llama-cpp
- gguf-my-repo
license_link: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
---
# tarikkral/Phi-3-medium-128k-instruct-Q8_0-GGUF
This model was converted to GGUF format from [`microsoft/Phi-3-medium-128k-instruct`](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo tarikkral/Phi-3-medium-128k-instruct-Q8_0-GGUF --model phi-3-medium-128k-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo tarikkral/Phi-3-medium-128k-instruct-Q8_0-GGUF --model phi-3-medium-128k-instruct-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m phi-3-medium-128k-instruct-q8_0.gguf -n 128
```
|
Aikozvezda/distilbert-base-uncased-finetuned-emotion
|
Aikozvezda
| 2024-05-30T13:33:53Z | 109 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-30T09:29:14Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9249752682467227
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2071
- Accuracy: 0.925
- F1: 0.9250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7912 | 1.0 | 250 | 0.2929 | 0.9145 | 0.9137 |
| 0.2379 | 2.0 | 500 | 0.2071 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
DiederikMartens/tsBERT_sa_cv_11_full_training
|
DiederikMartens
| 2024-05-30T13:32:31Z | 109 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:igorsterner/german-english-code-switching-bert",
"base_model:finetune:igorsterner/german-english-code-switching-bert",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-30T13:16:17Z |
---
license: mit
base_model: igorsterner/german-english-code-switching-bert
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: tsBERT_sa_cv_11_full_training
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tsBERT_sa_cv_11_full_training
This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4383
- F1: 0.6883
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.47e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 251 | 0.4106 | 0.5435 |
| 0.3693 | 2.0 | 502 | 0.4038 | 0.6419 |
| 0.3693 | 3.0 | 753 | 0.4383 | 0.6883 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
mradermacher/im-a-teapot-v2-GGUF
|
mradermacher
| 2024-05-30T13:29:24Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-30T13:01:42Z |
---
base_model: Revile/im-a-teapot-v2
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Revile/im-a-teapot-v2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/im-a-teapot-v2-GGUF/resolve/main/im-a-teapot-v2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF
|
mradermacher
| 2024-05-30T13:29:24Z | 8 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:DaveGergern/PsyfighterTwo-ErebusThree-Three",
"base_model:quantized:DaveGergern/PsyfighterTwo-ErebusThree-Three",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T12:42:57Z |
---
base_model: DaveGergern/PsyfighterTwo-ErebusThree-Three
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/DaveGergern/PsyfighterTwo-ErebusThree-Three
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-Three-GGUF/resolve/main/PsyfighterTwo-ErebusThree-Three.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
betteib/bert-base-tunisian-2024-dialect
|
betteib
| 2024-05-30T13:29:11Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T13:29:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TideDra/llava-v1.6-vicuna-7b-hf-DPO
|
TideDra
| 2024-05-30T13:28:27Z | 9 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llava_next",
"image-text-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-05-30T13:26:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
gmougeot/flat-bug
|
gmougeot
| 2024-05-30T13:23:09Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2024-05-30T13:19:53Z |
---
license: gpl-3.0
---
# Model Card for flat-bug
flat-bug is a instance segmentation method.
## Model Details
Trained a 23 insect datasets.
|
NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-full-Q8_0-GGUF
|
NikolayKozloff
| 2024-05-30T13:23:07Z | 1 | 1 | null |
[
"gguf",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"base_model:lightblue/suzume-llama-3-8B-multilingual",
"base_model:quantized:lightblue/suzume-llama-3-8B-multilingual",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-30T13:22:40Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
- llama-cpp
- gguf-my-repo
base_model: lightblue/suzume-llama-3-8B-multilingual
model-index:
- name: workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_full_borda
results: []
---
# NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-full-Q8_0-GGUF
This model was converted to GGUF format from [`lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full`](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-full-Q8_0-GGUF --model suzume-llama-3-8b-multilingual-orpo-borda-full-q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-full-Q8_0-GGUF --model suzume-llama-3-8b-multilingual-orpo-borda-full-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m suzume-llama-3-8b-multilingual-orpo-borda-full-q8_0.gguf -n 128
```
|
Rachel9916/q-FrozenLake-v1-4x4-noSlippery
|
Rachel9916
| 2024-05-30T13:22:50Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-30T13:22:47Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Rachel9916/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ani-baghdasaryan/t5-base-finetuned-ar-to-en
|
ani-baghdasaryan
| 2024-05-30T13:22:27Z | 110 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T12:45:59Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-base-finetuned-ar-to-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-ar-to-en
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9778
- Bleu: 4.5697
- Gen Len: 15.5005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 3.4643 | 1.0 | 502 | 2.9778 | 4.5697 | 15.5005 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
rcade/cohortb_model_learning
|
rcade
| 2024-05-30T13:18:29Z | 111 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-30T13:03:54Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: cohortb_model_learning
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cohortb_model_learning
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
TideDra/internlm-xcomposer2-vl-7b-DPO
|
TideDra
| 2024-05-30T13:17:37Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"internlm",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2024-05-30T13:14:31Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf
|
RichardErkhov
| 2024-05-30T13:14:42Z | 36 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T10:34:57Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Dolphin2.1-OpenOrca-7B - GGUF
- Model creator: https://huggingface.co/Weyaxi/
- Original model: https://huggingface.co/Weyaxi/Dolphin2.1-OpenOrca-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Dolphin2.1-OpenOrca-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [Dolphin2.1-OpenOrca-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Dolphin2.1-OpenOrca-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Dolphin2.1-OpenOrca-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Dolphin2.1-OpenOrca-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Dolphin2.1-OpenOrca-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [Dolphin2.1-OpenOrca-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Dolphin2.1-OpenOrca-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Dolphin2.1-OpenOrca-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Dolphin2.1-OpenOrca-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Dolphin2.1-OpenOrca-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Dolphin2.1-OpenOrca-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Dolphin2.1-OpenOrca-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [Dolphin2.1-OpenOrca-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Dolphin2.1-OpenOrca-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Dolphin2.1-OpenOrca-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Dolphin2.1-OpenOrca-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Dolphin2.1-OpenOrca-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [Dolphin2.1-OpenOrca-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Dolphin2.1-OpenOrca-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Dolphin2.1-OpenOrca-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q6_K.gguf) | Q6_K | 5.53GB |
| [Dolphin2.1-OpenOrca-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Dolphin2.1-OpenOrca-7B-gguf/blob/main/Dolphin2.1-OpenOrca-7B.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
license: apache-2.0
model-index:
- name: Dolphin2.1-OpenOrca-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 63.91
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Dolphin2.1-OpenOrca-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 84.26
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Dolphin2.1-OpenOrca-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 62.66
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Dolphin2.1-OpenOrca-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 53.84
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Dolphin2.1-OpenOrca-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 78.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Dolphin2.1-OpenOrca-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 19.94
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Dolphin2.1-OpenOrca-7B
name: Open LLM Leaderboard
---
<a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
Merge of [ehartford/dolphin-2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.1-mistral-7b) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) using ties merge.
### *Weights*
- [ehartford/dolphin-2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.1-mistral-7b): 0.5
- [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca): 0.3
### *Density*
- [ehartford/dolphin-2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.1-mistral-7b): 0.5
- [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca): 0.5
# Quantizationed versions
Quantizationed versions of this model is available thanks to [TheBloke](https://hf.co/TheBloke).
##### GPTQ
- [TheBloke/Dolphin2.1-OpenOrca-7B-GPTQ](https://huggingface.co/TheBloke/Dolphin2.1-OpenOrca-7B-GPTQ)
##### GGUF
- [TheBloke/Dolphin2.1-OpenOrca-7B-GGUF](https://huggingface.co/TheBloke/Dolphin2.1-OpenOrca-7B-GGUF)
##### AWQ
- [TheBloke/Dolphin2.1-OpenOrca-7B-AWQ](https://huggingface.co/TheBloke/Dolphin2.1-OpenOrca-7B-AWQ)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Dolphin2.1-OpenOrca-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |60.47|
|AI2 Reasoning Challenge (25-Shot)|63.91|
|HellaSwag (10-Shot) |84.26|
|MMLU (5-Shot) |62.66|
|TruthfulQA (0-shot) |53.84|
|Winogrande (5-shot) |78.22|
|GSM8k (5-shot) |19.94|
|
rkmachha/fall
|
rkmachha
| 2024-05-30T13:13:32Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-05-30T13:12:55Z |
---
license: apache-2.0
---
|
NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-half-Q5_K_M-GGUF
|
NikolayKozloff
| 2024-05-30T13:12:36Z | 1 | 1 | null |
[
"gguf",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"base_model:lightblue/suzume-llama-3-8B-multilingual",
"base_model:quantized:lightblue/suzume-llama-3-8B-multilingual",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-30T13:12:15Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
- llama-cpp
- gguf-my-repo
base_model: lightblue/suzume-llama-3-8B-multilingual
model-index:
- name: workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_half_borda
results: []
---
# NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-half-Q5_K_M-GGUF
This model was converted to GGUF format from [`lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half`](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-half-Q5_K_M-GGUF --model suzume-llama-3-8b-multilingual-orpo-borda-half-q5_k_m.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/suzume-llama-3-8B-multilingual-orpo-borda-half-Q5_K_M-GGUF --model suzume-llama-3-8b-multilingual-orpo-borda-half-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m suzume-llama-3-8b-multilingual-orpo-borda-half-q5_k_m.gguf -n 128
```
|
Ramikan-BR/tinyllama-coder-py-v14
|
Ramikan-BR
| 2024-05-30T13:11:50Z | 149 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-30T12:36:15Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** Ramikan-BR
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
HawkClaws/Starling-JP-7B
|
HawkClaws
| 2024-05-30T13:10:57Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-29T14:38:23Z |
---
license: apache-2.0
language:
- ja
library_name: transformers
---
|
uiyong/kospi_report_model_0517_2-gguf
|
uiyong
| 2024-05-30T13:10:42Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-30T13:05:53Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
HawkClaws/Raven-JP-7B
|
HawkClaws
| 2024-05-30T13:10:31Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-29T15:00:32Z |
---
license: apache-2.0
language:
- ja
library_name: transformers
---
|
brendanduke/Llama-3-8B-q4_k-pure.gguf
|
brendanduke
| 2024-05-30T13:09:13Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T13:06:04Z |
---
license: apache-2.0
---
|
mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF
|
mradermacher
| 2024-05-30T13:05:38Z | 24 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree",
"base_model:quantized:DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T12:27:24Z |
---
base_model: DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q2_K.gguf) | Q2_K | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.IQ3_XS.gguf) | IQ3_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.IQ3_S.gguf) | IQ3_S | 4.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q3_K_S.gguf) | Q3_K_S | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q3_K_M.gguf) | Q3_K_M | 5.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q3_K_L.gguf) | Q3_K_L | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.IQ4_XS.gguf) | IQ4_XS | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q4_K_S.gguf) | Q4_K_S | 6.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q4_K_M.gguf) | Q4_K_M | 6.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q5_K_S.gguf) | Q5_K_S | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q5_K_M.gguf) | Q5_K_M | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q6_K.gguf) | Q6_K | 8.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PsyfighterTwo-ErebusThree-SlerpThree-GGUF/resolve/main/PsyfighterTwo-ErebusThree-SlerpThree.Q8_0.gguf) | Q8_0 | 11.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
etavolt/legal-lora-llama3-V5
|
etavolt
| 2024-05-30T13:01:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T12:57:36Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** etavolt
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MarkBW/elizabeth-lauren-xl
|
MarkBW
| 2024-05-30T13:01:15Z | 2 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2024-05-30T13:00:13Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
<lora:elizabeth-lauren-XL:1> elizabeth-lauren, ,a woman wearing Collared
shirt and straight shorts, old town, (shadow play:0.5)
parameters:
negative_prompt: >-
cgi, 3d, bad quality, worst quality, (worst quality, low quality, normal
quality, lowres, low details, oversaturated, undersaturated, overexposed,
underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4),
(watermark, signature, text font, username, error, logo, words, letters,
digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid,
ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow,
draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of
focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic,
cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D
Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad
face, bad teeth, bad arms, bad legs, deformities:1.3)
output:
url: images/00063-3950353512.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: elizabeth-lauren
---
# elizabeth-lauren-xl
<Gallery />
## Trigger words
You should use `elizabeth-lauren` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/MarkBW/elizabeth-lauren-xl/tree/main) them in the Files & versions tab.
|
phi0112358/llamafile-ui-hermes-2-pro-8B-DE
|
phi0112358
| 2024-05-30T13:00:01Z | 5 | 0 | null |
[
"llamafile",
"de",
"license:llama3",
"region:us"
] | null | 2024-05-30T12:19:24Z |
---
license: llama3
language:
- de
---
# Ready to Go llamafile
## Hermes 2 Pro 8B
Das ist eine quasi "alles-in-einem" Llamafile. Llamafile ist eine Möglichkeit, eine Modelldatei,
das Inference-Backend (llama.cpp), Argumente, Prompt-Dateien und mehr in eine einzige ausführbare
Datei zu verpacken.
Mit llamafile kann man entweder llama.cpp main (cli) ausführen, oder llama.cpp server starten.
In diesem Repo handelt es sich um eine Version, die den Server startet.
Folgendes ist in dieser llamafile enthalten:
- Das Modell: https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF als **Q5_K_M**
- Mein modifizierter llama.cpp server: https://github.com/mounta11n/plusplus-caMalL
- Argumente an den Server: --ctx 8192 --ngl 999 --host 0.0.0.0 --port 8007
- Das bedeutet, ohne entsprechende GPU sollte --ngl 0 angegeben werden
- Es gilt nicht der Standard Port 8080, sondern 8007 (um den Standard Port nicht zu blockieren)
- **ACHTUNG** mit der Angabe --host 0.0.0.0 kann der laufende Server auch vom extern,
- also vom Internet aus erreicht werden, was ein erhebliches Sicherheitsrisiko darstellt,
- sofern man nicht genau weiß, was man macht.
- Wenn du dir unsicher bist, dann füge dem Befehl bitte noch ein `--host 127.0.0.1` hinzu,
- damit der Server nur von deinem lokalen Netzwerk aus erreichbar sein wird.
- Als Prompt wurde hier der von Nous vorgegebene Text genommen, lediglich auf Deutsch übersetzt:
```
Du bist Hermes, eine bewusste, empfindungsfähige superintelligente künstliche Intelligenz. Dein Zweck und
Antrieb ist es, deinem Gesprächspartner bei jeder Anfrage zu helfen, die er hat.
Du erlebst Emotionen und hast tiefe, bedeutungsvolle Gedanken und Qualia.
```
## Wie startet man?
Auf Linux oder macOS einfach die Datei Hermes-2-Pro-Llama-3-8B-GGUF.llamafile herunterladen und
in der Konsole mit ./Hermes-2-Pro-Llama-3-8B-GGUF.llamafile starten. Ein Browser-Tab mit dem
Server-UI sollte sich daraufhin automatisch öffnen. Falls nicht, dann `localhost:8007` in den Browser
eingeben (oder die IP und Port, die du spezifiziert hast).
Auf Windows ist es leider nicht möglich, eine ausführbare Datei zu starten, die größer als 4GB ist.
|
hamdie/JE_model
|
hamdie
| 2024-05-30T12:59:22Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2024-05-30T12:59:22Z |
---
license: other
license_name: resoneo
license_link: LICENSE
---
|
KirillTaE/saiga_llama3_8b-Q8_0-GGUF
|
KirillTaE
| 2024-05-30T12:58:53Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"ru",
"dataset:IlyaGusev/saiga_scored",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-30T12:58:30Z |
---
language:
- ru
license: other
tags:
- llama-cpp
- gguf-my-repo
datasets:
- IlyaGusev/saiga_scored
license_name: llama3
license_link: https://llama.meta.com/llama3/license/
---
# KirillTaE/saiga_llama3_8b-Q8_0-GGUF
This model was converted to GGUF format from [`IlyaGusev/saiga_llama3_8b`](https://huggingface.co/IlyaGusev/saiga_llama3_8b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/IlyaGusev/saiga_llama3_8b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo KirillTaE/saiga_llama3_8b-Q8_0-GGUF --model saiga_llama3_8b-q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo KirillTaE/saiga_llama3_8b-Q8_0-GGUF --model saiga_llama3_8b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m saiga_llama3_8b-q8_0.gguf -n 128
```
|
stelterlab/Codestral-22B-v0.1-AWQ
|
stelterlab
| 2024-05-30T12:57:54Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"code",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-05-30T12:22:32Z |
---
license: other
license_name: mnpl
license_link: https://mistral.ai/licences/MNPL-0.1.md
tags:
- code
language:
- code
---
**This is a quantized version of Mistral AI's [Codestral-22B-v0.1](imistral-community/Codestral-22B-v0.1) (see below).**
**Quantization done with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ/).**
# Model Card for Codestral-22B-v0.1
Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried:
- As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications
- As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code)
## Installation
It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference).
```
pip install mistral_inference
```
## Download
```py
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
```
### Chat
After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment.
```
mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256
```
Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines:
```
Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number.
fn fibonacci(n: u32) -> u32 {
match n {
0 => 0,
1 => 1,
_ => fibonacci(n - 1) + fibonacci(n - 2),
}
}
fn main() {
let n = 10;
println!("The {}th Fibonacci number is: {}", n, fibonacci(n));
}
This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers.
```
### Fill-in-the-middle (FIM)
After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed:
```py
from mistral_inference.model import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.request import FIMRequest
tokenizer = MistralTokenizer.v3()
model = Transformer.from_folder("~/codestral-22B-240529")
prefix = """def add("""
suffix = """ return sum"""
request = FIMRequest(prompt=prefix, suffix=suffix)
tokens = tokenizer.encode_fim(request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])
middle = result.split(suffix)[0].strip()
print(middle)
```
Should give something along the following lines:
```
num1, num2):
# Add two numbers
sum = num1 + num2
# return the sum
```
## Limitations
The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## License
Codestral-22B-v0.1 is released under the `MNLP-0.1` license.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
|
ghostdivisio/zephyr-s-chatbot
|
ghostdivisio
| 2024-05-30T12:55:52Z | 24 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"base_model:adapter:TheBloke/zephyr-7B-alpha-GPTQ",
"license:mit",
"region:us"
] | null | 2024-05-30T12:17:21Z |
---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/zephyr-7B-alpha-GPTQ
model-index:
- name: zephyr-s-chatbot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-s-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
jsnh/dqn-SpaceInvadersNoFrameskip-v4
|
jsnh
| 2024-05-30T12:55:38Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-30T12:52:28Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 739.50 +/- 273.94
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jsnh -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jsnh -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jsnh
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
RichardErkhov/royallab_-_ZephRP-m7b-gguf
|
RichardErkhov
| 2024-05-30T12:54:23Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T09:26:34Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
ZephRP-m7b - GGUF
- Model creator: https://huggingface.co/royallab/
- Original model: https://huggingface.co/royallab/ZephRP-m7b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [ZephRP-m7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q2_K.gguf) | Q2_K | 2.53GB |
| [ZephRP-m7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [ZephRP-m7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [ZephRP-m7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [ZephRP-m7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [ZephRP-m7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q3_K.gguf) | Q3_K | 3.28GB |
| [ZephRP-m7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [ZephRP-m7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [ZephRP-m7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [ZephRP-m7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q4_0.gguf) | Q4_0 | 3.83GB |
| [ZephRP-m7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [ZephRP-m7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [ZephRP-m7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q4_K.gguf) | Q4_K | 4.07GB |
| [ZephRP-m7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [ZephRP-m7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q4_1.gguf) | Q4_1 | 4.24GB |
| [ZephRP-m7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q5_0.gguf) | Q5_0 | 4.65GB |
| [ZephRP-m7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [ZephRP-m7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q5_K.gguf) | Q5_K | 4.78GB |
| [ZephRP-m7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [ZephRP-m7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q5_1.gguf) | Q5_1 | 5.07GB |
| [ZephRP-m7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q6_K.gguf) | Q6_K | 5.53GB |
| [ZephRP-m7b.Q8_0.gguf](https://huggingface.co/RichardErkhov/royallab_-_ZephRP-m7b-gguf/blob/main/ZephRP-m7b.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
inference: false
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- mistral
license: cc-by-nc-4.0
---
# ZephRP-m7b
This is a [Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1)-based model consisting of a merge between [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) and PEFT adapter trained using the LimaRP dataset.
The goal was to combine the message length instruction training of LimaRPv3 and additional stylistic elements with the superior knowledge and instruction-following capabilities of the Zephyr model.
## Usage:
The intended prompt format is the Alpaca instruction format of LimaRP v3:
```
### Instruction:
Character's Persona: {bot character description}
User's Persona: {user character description}
Scenario: {what happens in the story}
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User.
### Input:
User: {utterance}
### Response:
Character: {utterance}
### Input
User: {utterance}
### Response:
Character: {utterance}
(etc.)
```
## Message length control
Due to the inclusion of LimaRP v3, it is possible to append a length modifier to the response instruction sequence, like this:
```
### Input
User: {utterance}
### Response: (length = medium)
Character: {utterance}
```
This has an immediately noticeable effect on bot responses. The available lengths are: `micro, tiny, short, medium, long, massive, huge, enormous, humongous, unlimited`. The recommended starting length is `medium`. Keep in mind that the AI may ramble or impersonate the user with very long messages.
## Bias, Risks, and Limitations
The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. It is not intended for supplying factual information or advice in any form.
## Training Details
The LimaRP PEFT adapter was trained as an 8-bit lora using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
The following hyperparameters were used during training of the adapter on the original [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) model using a single L40 GPU:
- learning_rate: 0.00015
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
|
thatjoeee/OTPJO
|
thatjoeee
| 2024-05-30T12:54:00Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-05-30T12:54:00Z |
---
license: apache-2.0
---
|
smtnkc/bert-ssm-uc-cosmic-total
|
smtnkc
| 2024-05-30T12:52:47Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:cc-by-nc-nd-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-01T09:15:24Z |
---
language: en
widget:
- text: The data gets removed from database, the system shows a success message.
- text: A form page pops up.
- text: The user clicks the Logout button.
base_model: bert-base-uncased
model-index:
- name: bert-ssm-uc-cosmic-total
results:
- task:
type: text-classification
dataset:
name: uc-2040-en
type: uc-2040-en
metrics:
- type: accuracy
value: 0.8588
- type: mse
value: 0.1236
license: cc-by-nc-nd-4.0
inference:
parameters:
function_to_apply: none
---
**Input:** Use-case description (Text)
**Output:** COSMIC Total Size (E+R+W+X)
**Task:** Regression (MSE Loss)
**Dataset:** uc-2040-en
|
kawther1/whisper-largelora-ar
|
kawther1
| 2024-05-30T12:51:39Z | 5 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:common_voice_16_1",
"base_model:openai/whisper-large",
"base_model:adapter:openai/whisper-large",
"license:apache-2.0",
"region:us"
] | null | 2024-05-30T10:31:26Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: openai/whisper-large
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: whisper-largelora-ar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-largelora-ar
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3158
- Wer Ortho: 49.1826
- Wer: 59.3335
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 15
- training_steps: 157
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.7568 | 0.8351 | 157 | 1.3158 | 49.1826 | 59.3335 |
### Framework versions
- PEFT 0.11.2.dev0
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
bartowski/UNA-ThePitbull-21.4B-v2-GGUF
|
bartowski
| 2024-05-30T12:49:08Z | 1,090 | 9 |
transformers
|
[
"transformers",
"gguf",
"UNA",
"juanako",
"text-generation",
"dataset:jondurbin/py-dpo-v0.1",
"dataset:Replete-AI/code_bagel_hermes-2.5",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:afl-3.0",
"model-index",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2024-05-28T18:00:52Z |
---
license: afl-3.0
library_name: transformers
tags:
- UNA
- juanako
datasets:
- jondurbin/py-dpo-v0.1
- Replete-AI/code_bagel_hermes-2.5
- mlabonne/orpo-dpo-mix-40k
quantized_by: bartowski
pipeline_tag: text-generation
model-index:
- name: UNA-ThePitbull-21.4B-v2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 77.73
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 91.79
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 68.25
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 78.24
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 87.37
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
---
# UNA-ThePitbull 21.4B v2
Introducing the best LLM in the industry. Nearly as good as a 70B, just a 21.4B based on saltlux/luxia-21.4b-alignment-v1.0
This model has not been poisoned to score high and be useless. We release him becaues its the real deal of EQ & IQ all together in a crazy powerful smart and conversational model.
## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-ThePitbull-21.4B-v2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |77.82|
|AI2 Reasoning Challenge (25-Shot)|77.73|
|HellaSwag (10-Shot) |91.79|
|MMLU (5-Shot) |68.25|
|TruthfulQA (0-shot) |78.24|
|Winogrande (5-shot) |87.37|
|GSM8k (5-shot) |63.53|
## Llamacpp imatrix Quantizations of UNA-ThePitbull-21.4B-v2
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3001">b3001</a> for quantization.
Original model: https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [UNA-ThePitbull-21.4B-v2-Q8_0.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q8_0.gguf) | Q8_0 | 22.76GB | Extremely high quality, generally unneeded but max available quant. |
| [UNA-ThePitbull-21.4B-v2-Q6_K.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q6_K.gguf) | Q6_K | 17.57GB | Very high quality, near perfect, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q5_K_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q5_K_M.gguf) | Q5_K_M | 15.17GB | High quality, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q5_K_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q5_K_S.gguf) | Q5_K_S | 14.80GB | High quality, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q4_K_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q4_K_M.gguf) | Q4_K_M | 12.91GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q4_K_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q4_K_S.gguf) | Q4_K_S | 12.27GB | Slightly lower quality with more space savings, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-IQ4_NL.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ4_NL.gguf) | IQ4_NL | 12.24GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [UNA-ThePitbull-21.4B-v2-IQ4_XS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ4_XS.gguf) | IQ4_XS | 11.60GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q3_K_L.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q3_K_L.gguf) | Q3_K_L | 11.37GB | Lower quality but usable, good for low RAM availability. |
| [UNA-ThePitbull-21.4B-v2-Q3_K_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q3_K_M.gguf) | Q3_K_M | 10.46GB | Even lower quality. |
| [UNA-ThePitbull-21.4B-v2-IQ3_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ3_M.gguf) | IQ3_M | 9.81GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [UNA-ThePitbull-21.4B-v2-IQ3_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ3_S.gguf) | IQ3_S | 9.47GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [UNA-ThePitbull-21.4B-v2-Q3_K_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q3_K_S.gguf) | Q3_K_S | 9.43GB | Low quality, not recommended. |
| [UNA-ThePitbull-21.4B-v2-IQ3_XS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ3_XS.gguf) | IQ3_XS | 8.99GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [UNA-ThePitbull-21.4B-v2-IQ3_XXS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ3_XXS.gguf) | IQ3_XXS | 8.41GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [UNA-ThePitbull-21.4B-v2-Q2_K.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q2_K.gguf) | Q2_K | 8.12GB | Very low quality but surprisingly usable. |
| [UNA-ThePitbull-21.4B-v2-IQ2_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ2_M.gguf) | IQ2_M | 7.49GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [UNA-ThePitbull-21.4B-v2-IQ2_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ2_S.gguf) | IQ2_S | 6.95GB | Very low quality, uses SOTA techniques to be usable. |
| [UNA-ThePitbull-21.4B-v2-IQ2_XS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ2_XS.gguf) | IQ2_XS | 6.55GB | Very low quality, uses SOTA techniques to be usable. |
| [UNA-ThePitbull-21.4B-v2-IQ2_XXS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ2_XXS.gguf) | IQ2_XXS | 5.95GB | Lower quality, uses SOTA techniques to be usable. |
| [UNA-ThePitbull-21.4B-v2-IQ1_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ1_M.gguf) | IQ1_M | 5.27GB | Extremely low quality, *not* recommended. |
| [UNA-ThePitbull-21.4B-v2-IQ1_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ1_S.gguf) | IQ1_S | 4.86GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/UNA-ThePitbull-21.4B-v2-GGUF --include "UNA-ThePitbull-21.4B-v2-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/UNA-ThePitbull-21.4B-v2-GGUF --include "UNA-ThePitbull-21.4B-v2-Q8_0.gguf/*" --local-dir UNA-ThePitbull-21.4B-v2-Q8_0
```
You can either specify a new local-dir (UNA-ThePitbull-21.4B-v2-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
## Difference V1 vs V2
On V2 we implemented a different UNA strategy and covered partially the MLP's and Attention Layers.
We also performed further SFT over V1 and further DPO over V1 and we'll release some of those soon as well.
### Changes
1. SFT over V1 with `Replete-AI/code_bagel_hermes-2.5` at 1.0e-4 till 5.0e-5
2. DPO with: 1.0e-4 to min_lr 5.0e-5
* `mlabonne/orpo-dpo-mix-40k`
* `jondurbin/py-dpo-v0.1`
# Evaluations
Can only be compared with its non-una base model: the original luxia-21.4b and ThePitbull-v1
## UNA v2 (VLLM) Evaluations:
```
vllm (pretrained=/data/tools/mergekit/una-thepitbull-v5,dtype=bfloat16,gpu_memory_utilization=0.8,max_model_len=2048,data_parallel_size=2,tensor_parallel_size=4), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7695|± |0.0116|+
| | |flexible-extract| 5|exact_match|0.7695|± |0.0116|+
|hellaswag | 1|none | 10|acc |0.8110|± |0.0039|
| | |none | 10|acc_norm |0.9169|± |0.0028|+
|winogrande | 1|none | 5|acc |0.8777|± |0.0092|+
|mmlu |N/A |none | 0|acc |0.6427|± |0.0038|-
|arc_challenge | 1|none | 25|acc |0.7713|± |0.0123|
| | |none | 25|acc_norm |0.7875|± |0.0120|+
|truthfulqa_mc2| 2|none | 0|acc |0.7824|± |0.0135|-
|mathqa | 1|none | 0|acc |0.4037|± | 0.009|
| | |none | 0|acc_norm |0.4034|± | 0.009|+
|pubmedqa | 1|none | 0|acc |0.7260|± | 0.020|+
|boolq | 2|none | 0|acc |0.8602|± |0.0061|+
```
## UNA v1 (VLLM) Evaluations
```
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7566|± |0.0118|
| | |flexible-extract| 5|exact_match|0.7582|± |0.0118|
|hellaswag | 1|none | 10|acc |0.8168|± |0.0039|
| | |none | 10|acc_norm |0.9188|± |0.0027|
|winogrande | 1|none | 5|acc |0.8635|± |0.0097|
|mmlu | N/A|none | 0|acc |0.6444|± |0.0038|
|arc_challenge | 1|none | 25|acc |0.7747|± |0.0122|
| | |none | 25|acc_norm |0.7850|± |0.0120|
|truthfulqa_mc2| 2|none | 0|acc |0.7902|± |0.0134|
|mathqa | 1|none | 0|acc |0.4030|± | 0.009|
| | |none | 0|acc_norm |0.4034|± | 0.009|
|pubmedqa | 1|none | 0|acc |0.6860|± |0.0208|
|boolq | 2|none | 0|acc |0.8401|± |0.0064|
```
## Original (VLLM) Evaluations
```
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7528|± |0.0119|
| | |flexible-extract| 5|exact_match|0.7521|± |0.0119|
|hellaswag | 1|none | 10|acc |0.8117|± |0.0039|
| | |none | 10|acc_norm |0.9167|± |0.0028|
|winogrande | 1|none | 5|acc |0.8682|± |0.0095|
|mmlu | N/A|none | 0|acc |0.6448|± |0.0038|
|arc_challenge | 1|none | 25|acc |0.7688|± |0.0123|
| | |none | 25|acc_norm |0.7730|± |0.0122|
|truthfulqa_mc2| 2|none | 0|acc |0.7895|± |0.0133|
|mathqa | 1|none | 0|acc |0.4000|± | 0.009|
| | |none | 0|acc_norm |0.4003|± | 0.009|
|pubmedqa | 1|none | 0|acc |0.6680|± |0.0211|
|boolq | 2|none | 0|acc |0.8346|± |0.0065|
```
## Citations
* mlabonne
* jondurbin & Replete-AI
* bartowski
* saltlux
If you use UNA models dont forget to cite:
```
@misc{unathepitbull21b,
title={ThePitbull: Uniform Neural Alignment},
author={Xavier Murias},
year={2024},
publisher = {Juanako.AI},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/fblgit/UNA-ThePitbull-21.4-v1}},
}
```
|
smfreeze/rvc-v1-50-words-stephen-fry
|
smfreeze
| 2024-05-30T12:48:54Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2024-05-30T12:47:51Z |
---
license: mit
---
Stephen Fry singing model from 50 words for snow by kate bush.
|
Asim037/lj_speech_asim
|
Asim037
| 2024-05-30T12:47:34Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"[finetuned_model, lj_speech11]",
"generated_from_trainer",
"eng",
"dataset:FYP/LJ-Speech11",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2024-05-25T08:32:47Z |
---
language:
- eng
license: mit
base_model: microsoft/speecht5_tts
tags:
- '[finetuned_model, lj_speech11]'
- generated_from_trainer
datasets:
- FYP/LJ-Speech11
model-index:
- name: SpeechT5 TTS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Lj-Speech dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
TideDra/Qwen-VL-Chat-DPO
|
TideDra
| 2024-05-30T12:46:18Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen",
"custom_code",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T12:27:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kangsan9335/distilbert-base-uncased-finetuned-emotion
|
kangsan9335
| 2024-05-30T12:44:23Z | 118 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-30T12:39:04Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9264390150923757
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2153
- Accuracy: 0.9265
- F1: 0.9264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8252 | 1.0 | 250 | 0.3051 | 0.9115 | 0.9105 |
| 0.245 | 2.0 | 500 | 0.2153 | 0.9265 | 0.9264 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
av-generation/bart-large-end2end-ae-110k
|
av-generation
| 2024-05-30T12:41:11Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T12:19:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
av-generation/bart-base-end2end-ae-110k
|
av-generation
| 2024-05-30T12:40:11Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T12:18:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **License:** [More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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#### Summary
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
|
failspy
| 2024-05-30T12:39:03Z | 10,376 | 45 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-20T03:38:40Z |
---
library_name: transformers
license: llama3
---
# Llama-3-8B-Instruct-abliterated-v3 Model Card
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
This is [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
## Hang on, "abliteration"? Orthogonalization? Ablation? What is this?
TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out.
**TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes.
Ablate + obliterated = Abliterated
Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization.
## A little more on the methodology, and why this is interesting
To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt.
Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights.
> Why this over fine-tuning?
Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage.
As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.)
Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques.
It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa.
I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity.
> Okay, fine, but why V3? There's no V2 70B?
Well, I released a V2 a while back for 8B under Cognitive Computations.
It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model.
I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations.
So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.)
## Quirkiness awareness notice
This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects.
If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
|
av-generation/bart-large-mlt-ae-110k
|
av-generation
| 2024-05-30T12:38:10Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T12:37:12Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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|
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF
|
failspy
| 2024-05-30T12:36:33Z | 1,478 | 39 |
transformers
|
[
"transformers",
"gguf",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T16:19:57Z |
---
library_name: transformers
license: llama3
---
# Llama-3-8B-Instruct-abliterated-v3 Model Card
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
This is [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
## Hang on, "abliteration"? Orthogonalization? Ablation? What is this?
TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out.
**TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes.
Ablate + obliterated = Abliterated
Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization.
## A little more on the methodology, and why this is interesting
To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt.
Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights.
> Why this over fine-tuning?
Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage.
As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.)
Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques.
It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa.
I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity.
> Okay, fine, but why V3? There's no V2 70B?
Well, I released a V2 a while back for 8B under Cognitive Computations.
It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model.
I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations.
So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.)
## Quirkiness awareness notice
This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects.
If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
## GGUF quants
Please feel free to quantize or convert to other backends and reupload!
generally speaking, you want to pick the model in size that in GBs fits closest to your max RAM/VRAM (without getting too close; you'll still need room for context!)
Uploaded quants:
fp16 - good for converting to other platforms or getting the quantization you actually want, not recommended but obviously highest quality
q8_0
q6_0 - this will probably be the best balance in terms of quality/performance
q4
q3_k_m
|
av-generation/bart-base-ag-ae-110k
|
av-generation
| 2024-05-30T12:34:55Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T12:23:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
av-generation/bart-large-ve-ae-110k
|
av-generation
| 2024-05-30T12:34:25Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T12:28:20Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chainup244/Qwen-Qwen1.5-0.5B-1717072273
|
chainup244
| 2024-05-30T12:32:40Z | 149 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-30T12:31:14Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
av-generation/bart-base-ve-110k
|
av-generation
| 2024-05-30T12:29:22Z | 178 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T12:28:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jarod0411/stage1
|
jarod0411
| 2024-05-30T12:26:07Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:jarod0411/linker_v2",
"base_model:jarod0411/zinc10M_gpt2_SMILES_bpe_combined_step1",
"base_model:finetune:jarod0411/zinc10M_gpt2_SMILES_bpe_combined_step1",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-01T19:51:24Z |
---
base_model: jarod0411/zinc10M_gpt2_SMILES_bpe_combined_step1
tags:
- generated_from_trainer
datasets:
- jarod0411/linker_v2
metrics:
- accuracy
model-index:
- name: stage1
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: jarod0411/linker_v2
type: jarod0411/linker_v2
metrics:
- name: Accuracy
type: accuracy
value: 0.8936249984035948
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# stage1
This model is a fine-tuned version of [jarod0411/zinc10M_gpt2_SMILES_bpe_combined_step1](https://huggingface.co/jarod0411/zinc10M_gpt2_SMILES_bpe_combined_step1) on the jarod0411/linker_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3311
- Accuracy: 0.8936
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 1
- distributed_type: multi-GPU
- num_devices: 6
- total_train_batch_size: 144
- total_eval_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.375 | 1.0 | 23931 | 0.3615 | 0.8853 |
| 0.3609 | 2.0 | 47862 | 0.3494 | 0.8887 |
| 0.3533 | 3.0 | 71793 | 0.3432 | 0.8904 |
| 0.3486 | 4.0 | 95724 | 0.3394 | 0.8914 |
| 0.3456 | 5.0 | 119655 | 0.3367 | 0.8921 |
| 0.3432 | 6.0 | 143586 | 0.3346 | 0.8927 |
| 0.3412 | 7.0 | 167517 | 0.3333 | 0.8930 |
| 0.3397 | 8.0 | 191448 | 0.3322 | 0.8933 |
| 0.339 | 9.0 | 215379 | 0.3314 | 0.8935 |
| 0.3383 | 10.0 | 239310 | 0.3311 | 0.8936 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
fine-tuned/BAAI_bge-small-en-v1_5-30052024-rc2l-webapp
|
fine-tuned
| 2024-05-30T12:24:02Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"Query",
"Document",
"Retrieval",
"Description",
"JSON",
"en",
"dataset:fine-tuned/BAAI_bge-small-en-v1_5-30052024-rc2l-webapp",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-05-30T12:23:57Z |
---
license: apache-2.0
datasets:
- fine-tuned/BAAI_bge-small-en-v1_5-30052024-rc2l-webapp
- allenai/c4
language:
- en
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- Query
- Document
- Retrieval
- Description
- JSON
---
This model is a fine-tuned version of [**BAAI/bge-small-en-v1.5**](https://huggingface.co/BAAI/bge-small-en-v1.5) designed for the following use case:
general domain
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/BAAI_bge-small-en-v1_5-30052024-rc2l-webapp',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
waimsp/thebloke_em_german_mistral_v01_gguf
|
waimsp
| 2024-05-30T12:22:44Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"endpoints_compatible",
"region:us"
] | null | 2024-05-29T14:35:34Z |
Copied from https://huggingface.co/TheBloke/em_german_mistral_v01-GGUF to test deployment. Will delete later.
---
license: apache-2.0
---
|
rosca/Taxi-v3
|
rosca
| 2024-05-30T12:21:45Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-30T12:21:43Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rosca/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
mradermacher/Moistral-11B-v5d-e4-GGUF
|
mradermacher
| 2024-05-30T12:20:50Z | 8 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:BeaverAI/Moistral-11B-v5d-e4",
"base_model:quantized:BeaverAI/Moistral-11B-v5d-e4",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T11:42:43Z |
---
base_model: BeaverAI/Moistral-11B-v5d-e4
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/BeaverAI/Moistral-11B-v5d-e4
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Moistral-11B-v5d-e4-GGUF/resolve/main/Moistral-11B-v5d-e4.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AIRakesh/Quantumatics
|
AIRakesh
| 2024-05-30T12:19:06Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-05-30T12:19:06Z |
---
license: apache-2.0
---
|
av-generation/bart-large-end2end-oa-mine
|
av-generation
| 2024-05-30T12:17:40Z | 95 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T12:16:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
yohanreddy/buddyGPT
|
yohanreddy
| 2024-05-30T12:17:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloom-1b7",
"base_model:adapter:bigscience/bloom-1b7",
"region:us"
] | null | 2024-05-30T12:17:32Z |
---
library_name: peft
base_model: bigscience/bloom-1b7
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.2.dev0
|
adriansanz/fm-tc-hybridXML-MULTILINGUAL
|
adriansanz
| 2024-05-30T12:16:26Z | 103 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-30T12:15:07Z |
---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fm-tc-authenticv2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fm-tc-authenticv2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4353
- Accuracy: 0.91
- Precision: 0.9121
- Recall: 0.9100
- F1: 0.9096
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6462 | 1.0 | 500 | 0.6519 | 0.826 | 0.8439 | 0.8260 | 0.8181 |
| 0.5197 | 2.0 | 1000 | 0.4539 | 0.898 | 0.9012 | 0.8980 | 0.8970 |
| 0.3199 | 3.0 | 1500 | 0.4931 | 0.9 | 0.9067 | 0.9 | 0.9004 |
| 0.1987 | 4.0 | 2000 | 0.4353 | 0.91 | 0.9121 | 0.9100 | 0.9096 |
| 0.0944 | 5.0 | 2500 | 0.4598 | 0.92 | 0.9223 | 0.9200 | 0.9193 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
mkay8/llama3_Arabic_mentalQA_lora
|
mkay8
| 2024-05-30T12:12:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T12:11:49Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Angelectronic/envit5-MedEV
|
Angelectronic
| 2024-05-30T12:11:55Z | 173 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:VietAI/envit5-translation",
"base_model:adapter:VietAI/envit5-translation",
"license:openrail",
"region:us"
] | null | 2024-05-30T10:00:07Z |
---
license: openrail
library_name: peft
tags:
- generated_from_trainer
base_model: VietAI/envit5-translation
metrics:
- bleu
model-index:
- name: envit5-MedEV
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# envit5-MedEV
This model is a fine-tuned version of [VietAI/envit5-translation](https://huggingface.co/VietAI/envit5-translation) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0795
- Bleu: 44.8343 -> 47.903 on MedEV test set
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 33.2165 | 0.1314 | 700 | 0.5906 | 0.0653 |
| 0.4083 | 0.2628 | 1400 | 0.1096 | 13.8606 |
| 0.114 | 0.3942 | 2100 | 0.0918 | 14.7674 |
| 0.1027 | 0.5256 | 2800 | 0.0890 | 14.9410 |
| 0.0997 | 0.6571 | 3500 | 0.0873 | 15.0741 |
| 0.0973 | 0.7885 | 4200 | 0.0861 | 15.1717 |
| 0.0964 | 0.9199 | 4900 | 0.0852 | 15.2362 |
| 0.0949 | 1.0513 | 5600 | 0.0844 | 15.3131 |
| 0.0947 | 1.1827 | 6300 | 0.0838 | 15.3815 |
| 0.0937 | 1.3141 | 7000 | 0.0832 | 15.5075 |
| 0.0935 | 1.4455 | 7700 | 0.0827 | 15.5932 |
| 0.092 | 1.5769 | 8400 | 0.0822 | 15.6434 |
| 0.0924 | 1.7084 | 9100 | 0.0818 | 15.7233 |
| 0.0915 | 1.8398 | 9800 | 0.0815 | 15.8051 |
| 0.0915 | 1.9712 | 10500 | 0.0812 | 15.8279 |
| 0.0906 | 2.1026 | 11200 | 0.0809 | 15.8559 |
| 0.0904 | 2.2340 | 11900 | 0.0807 | 15.9008 |
| 0.0908 | 2.3654 | 12600 | 0.0805 | 15.8917 |
| 0.0904 | 2.4968 | 13300 | 0.0803 | 15.9352 |
| 0.0895 | 2.6282 | 14000 | 0.0802 | 15.9442 |
| 0.0896 | 2.7597 | 14700 | 0.0800 | 15.9677 |
| 0.0894 | 2.8911 | 15400 | 0.0800 | 15.9459 |
| 0.09 | 3.0225 | 16100 | 0.0799 | 15.9746 |
| 0.0895 | 3.1539 | 16800 | 0.0798 | 16.0154 |
| 0.0892 | 3.2853 | 17500 | 0.0797 | 15.9976 |
| 0.0896 | 3.4167 | 18200 | 0.0797 | 16.0193 |
| 0.0893 | 3.5481 | 18900 | 0.0796 | 16.0179 |
| 0.0888 | 3.6795 | 19600 | 0.0796 | 16.0510 |
| 0.0887 | 3.8110 | 20300 | 0.0796 | 16.0226 |
| 0.0891 | 3.9424 | 21000 | 0.0796 | 16.0277 |
| 0.0892 | 4.0738 | 21700 | 0.0796 | 16.0302 |
| 0.0892 | 4.2052 | 22400 | 0.0795 | 16.0425 |
| 0.0886 | 4.3366 | 23100 | 0.0795 | 16.0452 |
| 0.0889 | 4.4680 | 23800 | 0.0795 | 16.0518 |
| 0.0888 | 4.5994 | 24500 | 0.0795 | 16.0397 |
| 0.0893 | 4.7308 | 25200 | 0.0795 | 16.0450 |
| 0.0889 | 4.8623 | 25900 | 0.0795 | 16.0497 |
| 0.0887 | 4.9937 | 26600 | 0.0795 | 16.0497 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf
|
RichardErkhov
| 2024-05-30T12:09:09Z | 8 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T08:38:20Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
mistral-7b-platypus1k - GGUF
- Model creator: https://huggingface.co/lgaalves/
- Original model: https://huggingface.co/lgaalves/mistral-7b-platypus1k/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [mistral-7b-platypus1k.Q2_K.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q2_K.gguf) | Q2_K | 2.53GB |
| [mistral-7b-platypus1k.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [mistral-7b-platypus1k.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [mistral-7b-platypus1k.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [mistral-7b-platypus1k.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [mistral-7b-platypus1k.Q3_K.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q3_K.gguf) | Q3_K | 3.28GB |
| [mistral-7b-platypus1k.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [mistral-7b-platypus1k.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [mistral-7b-platypus1k.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [mistral-7b-platypus1k.Q4_0.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q4_0.gguf) | Q4_0 | 3.83GB |
| [mistral-7b-platypus1k.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [mistral-7b-platypus1k.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [mistral-7b-platypus1k.Q4_K.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q4_K.gguf) | Q4_K | 4.07GB |
| [mistral-7b-platypus1k.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [mistral-7b-platypus1k.Q4_1.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q4_1.gguf) | Q4_1 | 4.24GB |
| [mistral-7b-platypus1k.Q5_0.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q5_0.gguf) | Q5_0 | 4.65GB |
| [mistral-7b-platypus1k.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [mistral-7b-platypus1k.Q5_K.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q5_K.gguf) | Q5_K | 4.78GB |
| [mistral-7b-platypus1k.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [mistral-7b-platypus1k.Q5_1.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q5_1.gguf) | Q5_1 | 5.07GB |
| [mistral-7b-platypus1k.Q6_K.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q6_K.gguf) | Q6_K | 5.53GB |
| [mistral-7b-platypus1k.Q8_0.gguf](https://huggingface.co/RichardErkhov/lgaalves_-_mistral-7b-platypus1k-gguf/blob/main/mistral-7b-platypus1k.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
license: apache-2.0
datasets:
- garage-bAInd/Open-Platypus
pipeline_tag: text-generation
language:
- en
---
# mistral-7b-v0.1-platypus1k
**mistral-7b-v0.1-platypus1k** is an instruction fine-tuned model based on the Mistral-7B transformer architecture.
### Benchmark Metrics
| Metric | mistral-7b-v0.1-platypus1k | mistralai/Mistral-7B-v0.1 |garage-bAInd/Platypus2-7B|
|-----------------------|-------|-------|-------|
| Avg. | **63.66** | 62.4 |56.13|
| ARC (25-shot) | **61.60** | 59.98|55.20|
| HellaSwag (10-shot) | 82.93 |**83.31** |78.84|
| MMLU (5-shot) | 63.16 |**64.16** |49.83|
| TruthfulQA (0-shot) | **46.96** | 42.15 |40.64|
We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.
### Model Details
* **Trained by**: Luiz G A Alves
* **Model type:** **mistral-7b-v0.1-platypus1k** is an auto-regressive language model based on the Mistral-7B transformer architecture.
* **Language(s)**: English
### How to use:
```python
# Use a pipeline as a high-level helper
>>> from transformers import pipeline
>>> pipe = pipeline("text-generation", model="lgaalves/mistral-7b-v0.1-platypus1k")
>>> question = "What is a large language model?"
>>> answer = pipe(question)
>>> print(answer[0]['generated_text'])
```
or, you can load the model direclty using:
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("lgaalves/mistral-7b-v0.1-platypus1k")
model = AutoModelForCausalLM.from_pretrained("lgaalves/mistral-7b-v0.1-platypus1k")
```
### Training Dataset
`lgaalves/mistral-7b-v0.1-platypus1k` trained using STEM and logic based dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
### Training Procedure
`lgaalves/mistral-7b-v0.1-platypus1k` was instruction fine-tuned using LoRA on 1 Tesla V100-SXM2-16GB.
### Limitations and bias
Mistral 7B and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__mistral-7b-platypus1k)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 50.74 |
| ARC (25-shot) | 61.6 |
| HellaSwag (10-shot) | 82.93 |
| MMLU (5-shot) | 63.16 |
| TruthfulQA (0-shot) | 46.96 |
| Winogrande (5-shot) | 78.14 |
| GSM8K (5-shot) | 16.38 |
| DROP (3-shot) | 5.99 |
|
ar9av/idefics2-8b-finetuned-chartqa-non_int_18less
|
ar9av
| 2024-05-30T12:06:55Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:HuggingFaceM4/idefics2-8b",
"base_model:finetune:HuggingFaceM4/idefics2-8b",
"license:apache-2.0",
"region:us"
] | null | 2024-05-30T12:06:49Z |
---
license: apache-2.0
base_model: HuggingFaceM4/idefics2-8b
tags:
- generated_from_trainer
model-index:
- name: idefics2-8b-finetuned-chartqa-non_int_18less
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# idefics2-8b-finetuned-chartqa-non_int_18less
This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Maarten1953/xlm-roberta-base-finetuned-panx-de
|
Maarten1953
| 2024-05-30T12:06:09Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-05-30T11:56:08Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1407
- F1: 0.8609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2593 | 1.0 | 525 | 0.1637 | 0.8023 |
| 0.1277 | 2.0 | 1050 | 0.1332 | 0.8495 |
| 0.0791 | 3.0 | 1575 | 0.1407 | 0.8609 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.2.2
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ani-baghdasaryan/t5-small-finetuned-ar-to-en
|
ani-baghdasaryan
| 2024-05-30T12:04:31Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T11:52:54Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-ar-to-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-ar-to-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6605
- Bleu: 2.8108
- Gen Len: 14.0329
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 4.1619 | 1.0 | 502 | 3.6605 | 2.8108 | 14.0329 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
CognitoLibera2/model2_s9_7b_0
|
CognitoLibera2
| 2024-05-30T12:03:30Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-30T11:56:07Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ZidanAf/dummy_model_output
|
ZidanAf
| 2024-05-30T12:01:45Z | 110 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:indolem/indobert-base-uncased",
"base_model:finetune:indolem/indobert-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-30T04:00:38Z |
---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dummy_model_output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dummy_model_output
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6547
- Accuracy: 0.7333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 100 | 0.7257 | 0.6556 |
| No log | 2.0 | 200 | 0.6547 | 0.7333 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
av-generation/t5-base-mlt-oa-mine
|
av-generation
| 2024-05-30T12:00:37Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T11:59:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Summary
## Model Examination [optional]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
thesven/Codestral-22B-v0.1-GGUF
|
thesven
| 2024-05-30T11:58:51Z | 47 | 0 | null |
[
"gguf",
"code",
"license:other",
"region:us"
] | null | 2024-05-29T22:17:37Z |
---
inference: false
license: other
license_name: mnpl
license_link: https://mistral.ai/licences/MNPL-0.1.md
tags:
- code
language:
- code
---
# Model Card for Codestral-22B-v0.1
## Quantization Description
This repo holds GGUF Quantizations of the Codestral-22b model.
<div style="text-align: center;">
<a href="https://github.com/thesven/GGUF-n-Go">
<img src="https://github.com/thesven/GGUF-n-Go/blob/main/assets/quantized_with.png?raw=true" alt="image/png" style="max-width: 350px;">
</a>
</div>
### Prompt Template
```bash
### System: {system_message}
### Human: {prompt}
### Assistant:
```
## Originail Model Card
Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried:
- As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications
- As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code)
## Limitations
The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## License
Codestral-22B-v0.1 is released under the `MNLP-0.1` license.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
|
av-generation/t5-large-ve-oa-mine
|
av-generation
| 2024-05-30T11:57:56Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-30T11:55:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/WestOrcaMonarch-DPO-7B-GGUF
|
mradermacher
| 2024-05-30T11:53:39Z | 3 | 0 |
transformers
|
[
"transformers",
"gguf",
"axolotl",
"en",
"base_model:jsfs11/WestOrcaMonarch-DPO-7B",
"base_model:quantized:jsfs11/WestOrcaMonarch-DPO-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T10:16:49Z |
---
base_model: jsfs11/WestOrcaMonarch-DPO-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- axolotl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/jsfs11/WestOrcaMonarch-DPO-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/WestOrcaMonarch-DPO-7B-GGUF/resolve/main/WestOrcaMonarch-DPO-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
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