modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
yuanmo/ai-hosanna-rvc
|
yuanmo
| 2024-01-23T12:03:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-01-23T11:49:11Z |
---
license: apache-2.0
---
友人 Hosanna 的模型 XD
以下模型均由 [RVC](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/) 训练生成，可用于 RVC webUI 推理训练
|
7flash/jan23-first
|
7flash
| 2024-01-23T11:55:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T11:39:22Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: out
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.3.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
# out
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8633
## 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-06
- 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9984 | 0.17 | 1 | 1.0934 |
| 0.9864 | 0.35 | 2 | 1.0603 |
| 0.9181 | 0.7 | 4 | 0.9132 |
| 0.8843 | 1.04 | 6 | 0.8623 |
| 0.8513 | 1.3 | 8 | 0.8310 |
| 0.7957 | 1.65 | 10 | 0.8248 |
| 0.7823 | 2.0 | 12 | 0.8221 |
| 0.5977 | 2.26 | 14 | 0.8459 |
| 0.5766 | 2.61 | 16 | 0.8648 |
| 0.546 | 2.96 | 18 | 0.8637 |
| 0.5024 | 3.22 | 20 | 0.8633 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
alitolga/opt-350m_IA3_lr5e-05_bs4_epoch20_wd0.01
|
alitolga
| 2024-01-23T11:55:16Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:finetune:facebook/opt-350m",
"license:other",
"region:us"
] | null | 2024-01-23T11:50:55Z |
---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
model-index:
- name: opt-350m_IA3_lr5e-05_bs4_epoch20_wd0.01
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. -->
# opt-350m_IA3_lr5e-05_bs4_epoch20_wd0.01
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6235
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0247 | 1.0 | 157 | 3.8361 |
| 4.0183 | 2.0 | 314 | 3.8183 |
| 4.0005 | 3.0 | 471 | 3.7924 |
| 3.9764 | 4.0 | 628 | 3.7634 |
| 3.8952 | 5.0 | 785 | 3.7394 |
| 3.9008 | 6.0 | 942 | 3.7191 |
| 3.9332 | 7.0 | 1099 | 3.7025 |
| 3.8841 | 8.0 | 1256 | 3.6877 |
| 3.8515 | 9.0 | 1413 | 3.6754 |
| 3.8297 | 10.0 | 1570 | 3.6650 |
| 3.785 | 11.0 | 1727 | 3.6560 |
| 3.832 | 12.0 | 1884 | 3.6485 |
| 3.7683 | 13.0 | 2041 | 3.6422 |
| 3.8607 | 14.0 | 2198 | 3.6369 |
| 3.7712 | 15.0 | 2355 | 3.6327 |
| 3.7966 | 16.0 | 2512 | 3.6293 |
| 3.7841 | 17.0 | 2669 | 3.6266 |
| 3.8344 | 18.0 | 2826 | 3.6248 |
| 3.7461 | 19.0 | 2983 | 3.6238 |
| 3.8097 | 20.0 | 3140 | 3.6235 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
chanwit/flux-2b-v0.1
|
chanwit
| 2024-01-23T11:51:17Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-22T06:39:11Z |
---
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]
|
Technoculture/Medorca-2x7b
|
Technoculture
| 2024-01-23T11:42:25Z | 1,380 | 2 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"merge",
"epfl-llm/meditron-7b",
"microsoft/Orca-2-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-10T20:09:34Z |
---
license: apache-2.0
tags:
- moe
- merge
- epfl-llm/meditron-7b
- microsoft/Orca-2-7b
---
# Medorca-2x7b
Medorca-2x7b is a Mixure of Experts (MoE) made with the following models:
* [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b)
* [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b)
## Evaluations
| Benchmark | Medorca-2x7b | Orca-2-7b | llama-2-7b | meditron-7b | meditron-70b |
| --- | --- | --- | --- | --- | --- |
| MedMCQA | | | | | |
| ClosedPubMedQA | | | | | |
| PubMedQA | | | | | |
| MedQA | | | | | |
| MedQA4 | | | | | |
| MedicationQA | | | | | |
| MMLU Medical | | | | | |
| MMLU | 53.3 | **56.37** | | | |
| TruthfulQA | 48.04 | **52.45** | | | |
| GSM8K | 20.64 | **47.2** | | | |
| ARC | 54.1 | 54.1 | | | |
| HellaSwag | 76.04 | **76.19** | | | |
| Winogrande | **74.51** | 73.48 | | | |
More details on the Open LLM Leaderboard evaluation results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__Medorca-2x7b.)
## 🧩 Configuration
```yaml
base_model: microsoft/Orca-2-7b
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: epfl-llm/meditron-7b
positive_prompts:
- "How does sleep affect cardiovascular health?"
- "Could a plant-based diet improve arthritis symptoms?"
- "A patient comes in with symptoms of dizziness and nausea..."
- "When discussing diabetes management, the key factors to consider are..."
- "The differential diagnosis for a headache with visual aura could include..."
negative_prompts:
- "Recommend a good recipe for a vegetarian lasagna."
- "Give an overview of the French Revolution."
- "Explain how a digital camera captures an image."
- "What are the environmental impacts of deforestation?"
- "The recent advancements in artificial intelligence have led to developments in..."
- "The fundamental concepts in economics include ideas like supply and demand, which explain..."
- source_model: microsoft/Orca-2-7b
positive_prompts:
- "Here is a funny joke for you -"
- "When considering the ethical implications of artificial intelligence, one must take into account..."
- "In strategic planning, a company must analyze its strengths and weaknesses, which involves..."
- "Understanding consumer behavior in marketing requires considering factors like..."
- "The debate on climate change solutions hinges on arguments that..."
negative_prompts:
- "In discussing dietary adjustments for managing hypertension, it's crucial to emphasize..."
- "For early detection of melanoma, dermatologists recommend that patients regularly check their skin for..."
- "Explaining the importance of vaccination, a healthcare professional should highlight..."
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Technoculture/Medorca-2x7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16},
)
messages = [{"role": "user", "content": "Why am i feeling so tired this month?"}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
xformAI/facebook-opt-125m-qcqa-ub-6-best-for-KV-cache
|
xformAI
| 2024-01-23T11:40:03Z | 1,362 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T11:37:28Z |
---
license: mit
language:
- en
library_name: transformers
---
This is a QCQA version of the original model facebook/opt-125m. In this version, the original MHA architecture is preserved but instead of having a single K/V head, different K/V heads corresponding to the same group have the same mean-pooled K or V values. It has upto 6 groups of KV heads per layer instead of original 12 KV heads in the MHA implementation. This implementation is supposed to more efficient than corresponding GQA one.
|
xformAI/opt-125m-gqa-ub-6-best-for-KV-cache
|
xformAI
| 2024-01-23T11:34:57Z | 1,359 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T08:34:46Z |
---
license: mit
language:
- en
library_name: transformers
---
This is a GQA version of the original model facebook/opt-125m. In this version, the original MHA architecture is preserved but instead of having a single K/V head, different K/V heads corresponding to the same group have the same mean-pooled K or V values. It has 6 groups of KV heads per layer instead of original 12 KV heads in the MHA implementation.
|
harshit03/new_model
|
harshit03
| 2024-01-23T11:33:38Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T07:19:40Z |
---
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]
[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]
|
mansee/distilbert-base-uncased-org-address-question-answering
|
mansee
| 2024-01-23T11:24:29Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-14T17:30:20Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-org-address
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. -->
# distilbert-base-uncased-finetuned-org-address
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7948
## Model description
This is a question answering model to find out the organisation name and address from the given context.
## 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 | 2 | 5.8774 |
| No log | 2.0 | 4 | 5.8199 |
| No log | 3.0 | 6 | 5.7948 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Floyd93/T5_Grammer_Jan_2024
|
Floyd93
| 2024-01-23T11:18:41Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-22T03:10:06Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
model-index:
- name: T5_Grammer_Jan_2024
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_Grammer_Jan_2024
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 384
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 1 | 24.5602 | 0.339 | 0.1724 | 0.339 | 0.339 | 3.8 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
khoantap/Alpaca-Deep-34b
|
khoantap
| 2024-01-23T11:16:53Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T10:54:35Z |
---
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]
|
Pankaj001/ImageClassification-BrainTumor_data
|
Pankaj001
| 2024-01-23T11:04:53Z | 0 | 1 | null |
[
"image-classification",
"dataset:benschill/brain-tumor-collection",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2024-01-23T10:52:47Z |
---
license: apache-2.0
datasets:
- benschill/brain-tumor-collection
metrics:
- accuracy
pipeline_tag: image-classification
---
# Brain Tumor Classification Model
## Overview
This repository contains a deep learning model for brain tumor classification using Hugging Face Transformers. The model has been trained on a brain tumor dataset consisting of 5712 training samples and validated on 1311 samples. It is designed to classify brain tumor images into four classes: 'glioma', 'meningioma', 'notumor', and 'pituitary'.
## Model Details
- **Framework**: Hugging Face Transformers
- **Dataset**: Brain Tumor Dataset
- **Training Data**: 5712 samples
- **Validation Data**: 1311 samples
- **Input Shape**: 130x130 pixels with 3 channels (RGB)
- **Data Preprocessing**: Data is normalized
- **Validation Accuracy**: 72%
## Classes
The model classifies brain tumor images into the following classes:
- 'glioma' (Class 0)
- 'meningioma' (Class 1)
- 'notumor' (Class 2)
- 'pituitary' (Class 3)
## Usage
You can use this model for brain tumor classification tasks. Here's an example of how to load and use the model for predictions in Python:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import tensorflow as tf
import numpy as np
# Load the pre-trained model
model_name = "model/brain_tumor_model.h5" # Replace with the actual model name
model = tf.keras.models.load_model(model_name)
# to get prediction
x = numpy array image
pred = np.argmax(model.predict(x),axis=-1)
# class label
class_labels = {0: 'glioma', 1: 'meningioma', 2: 'notumor', 3: 'pituitary'}
|
julien-cpsn/BERT-GSPR-text-classification
|
julien-cpsn
| 2024-01-23T11:00:34Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain",
"dataset:julien-cpsn/autotrain-data-BERT-GSPR-text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-23T11:00:20Z |
---
tags:
- autotrain
- text-classification
widget:
- text: "I love AutoTrain"
datasets:
- julien-cpsn/autotrain-data-BERT-GSPR-text-classification
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.5412108898162842
f1_macro: 0.2333333333333333
f1_micro: 0.4000000000000001
f1_weighted: 0.2333333333333333
precision_macro: 0.16666666666666666
precision_micro: 0.4
precision_weighted: 0.16666666666666666
recall_macro: 0.4
recall_micro: 0.4
recall_weighted: 0.4
accuracy: 0.4
|
vadhri/Taxi-v3-1000000-lr04
|
vadhri
| 2024-01-23T10:56:13Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-23T10:56:10Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-1000000-lr04
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
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="vadhri/Taxi-v3-1000000-lr04", 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"])
```
|
aishutin/stable-diffusion-2-out
|
aishutin
| 2024-01-23T10:47:15Z | 26 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2",
"base_model:finetune:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-23T09:55:11Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - aishutin/stable-diffusion-2-out
This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
bharti-jha/distilbert-base-uncased-finetuned-squad
|
bharti-jha
| 2024-01-23T10:44:41Z | 46 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-23T07:01:47Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: bharti-jha/distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bharti-jha/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9650
- Train End Logits Accuracy: 0.7323
- Train Start Logits Accuracy: 0.6942
- Validation Loss: 1.1226
- Validation End Logits Accuracy: 0.7004
- Validation Start Logits Accuracy: 0.6637
- Epoch: 1
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11066, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.5012 | 0.6070 | 0.5693 | 1.1429 | 0.6904 | 0.6539 | 0 |
| 0.9650 | 0.7323 | 0.6942 | 1.1226 | 0.7004 | 0.6637 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Tokenizers 0.15.0
|
refarde/OPEN-SOLAR-KO-10.7B-S-Core
|
refarde
| 2024-01-23T10:44:39Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"finetuned",
"en",
"ko",
"dataset:royboy0416/ko-alpaca",
"base_model:beomi/OPEN-SOLAR-KO-10.7B",
"base_model:finetune:beomi/OPEN-SOLAR-KO-10.7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-01-19T07:02:38Z |
---
base_model: beomi/OPEN-SOLAR-KO-10.7B
license: apache-2.0
pipeline_tag: text-generation
language:
- en
- ko
tags:
- finetuned
- text-generation
datasets:
- royboy0416/ko-alpaca
inference: false
model_type: mixtral
---
# Model Card for OPEN-SOLAR-KO-10.7B-S-Core
## Model Details
* **Base Model**: [beomi/OPEN-SOLAR-KO-10.7B](https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B)
## Dataset Details
### Used Datasets
- royboy0416/ko-alpaca
|
Mihaiii/TinyLlama-1.1B-Chat-v1.0-optimum-intel
|
Mihaiii
| 2024-01-23T10:44:38Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-01-23T09:46:41Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
inference: false
---
Optimum quantization using the command:
```bash
optimum-cli inc quantize --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 --output ./TinyLlama
```
Usage example:
```python
from optimum.intel import INCModelForCausalLM
from transformers import AutoTokenizer, pipeline, AutoModelForCausalLM
import torch
model_id = "Mihaiii/TinyLlama-1.1B-Chat-v1.0-optimum-intel"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = INCModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.0001, repetition_penalty=1.2)
print(outputs[0]["generated_text"])
```
|
Saugatkafley/bloom-7b1-alpaca-sft
|
Saugatkafley
| 2024-01-23T10:43:20Z | 0 | 0 | null |
[
"safetensors",
"ne",
"dataset:diyoai/alpaca-nepali",
"license:mit",
"region:us"
] | null | 2024-01-23T05:06:02Z |
---
license: mit
datasets:
- diyoai/alpaca-nepali
language:
- ne
metrics:
- accuracy
---
|
shinerkim/koalpaca-polyglot-12.8b-bill
|
shinerkim
| 2024-01-23T10:34:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-23T10:34:15Z |
---
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]
|
HariniMaha/my-pet-dog-xzg
|
HariniMaha
| 2024-01-23T10:27:59Z | 3 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-23T10:23:35Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-xzg Dreambooth model trained by HariniMaha following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 820320105005
Sample pictures of this concept:
.jpg)
.jpeg)
.jpg)
.jpeg)
.jpeg)
|
aotraz/aotrazRVCModels
|
aotraz
| 2024-01-23T10:27:18Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-07-27T18:59:33Z |
---
license: openrail
---
# Credit
Credit would be appricated.
If you would like to credit, do either\
[@aotraz](https://www.youtube.com/channel/UCJ7J4qfB2ih3hJLGU2QfINg) on YouTube\
or\
talktalkrouter on Discord
|
teddythinh/phobert-base-v2-finetuned-uit-viquad
|
teddythinh
| 2024-01-23T10:18:25Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:vinai/phobert-base-v2",
"base_model:finetune:vinai/phobert-base-v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-23T09:37:53Z |
---
base_model: vinai/phobert-base-v2
tags:
- generated_from_keras_callback
model-index:
- name: teddythinh/phobert-base-v2-finetuned-uit-viquad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# teddythinh/phobert-base-v2-finetuned-uit-viquad
This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8423
- Train End Logits Accuracy: 0.4973
- Train Start Logits Accuracy: 0.4201
- Validation Loss: 1.9312
- Validation End Logits Accuracy: 0.4842
- Validation Start Logits Accuracy: 0.4203
- Epoch: 1
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2690, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 2.7462 | 0.3481 | 0.2765 | 2.0324 | 0.4644 | 0.4040 | 0 |
| 1.8423 | 0.4973 | 0.4201 | 1.9312 | 0.4842 | 0.4203 | 1 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.13.3
|
SaranKelha/my-pet-cat-xzg
|
SaranKelha
| 2024-01-23T10:15:19Z | 0 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-23T10:11:01Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Cat-xzg Dreambooth model trained by SaranKelha following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
simonycl/data-selection-Llama-2-7b-p3-uniform-upsample-epoch-24
|
simonycl
| 2024-01-23T10:11:04Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-01-23T10:10:47Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# 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]
- **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 Data 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 Data 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 0.7.1
|
julien-cpsn/BERT-llm-generic
|
julien-cpsn
| 2024-01-23T10:04:17Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain",
"dataset:julien-cpsn/autotrain-data-BERT-llm-generic",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-23T10:04:02Z |
---
tags:
- autotrain
- text-classification
widget:
- text: "I love AutoTrain"
datasets:
- julien-cpsn/autotrain-data-BERT-llm-generic
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.09664379805326462
f1_macro: 0.9714796738949586
f1_micro: 0.9743589743589743
f1_weighted: 0.974192594235853
precision_macro: 0.9726759726759727
precision_micro: 0.9743589743589743
precision_weighted: 0.9742847242847243
recall_macro: 0.9706012378426171
recall_micro: 0.9743589743589743
recall_weighted: 0.9743589743589743
accuracy: 0.9743589743589743
|
mucai/vip-llava-7b-pretrain
|
mucai
| 2024-01-23T10:02:18Z | 4 | 1 |
transformers
|
[
"transformers",
"llava",
"text-generation",
"arxiv:2312.00784",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-23T09:39:25Z |
---
inference: false
---
<br>
<br>
# ViP-LLaVA Model Card
## Model details
**Model type:**
ViP-LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on both image level instruction data and region-level instruction data annotated with visual prompts.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
ViP-LLaVA-7B-Pretrain was trained in November 2023. [Paper](https://arxiv.org/abs/2312.00784)
**Paper or resources for more information:**
https://vip-llava.github.io/
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
**Where to send questions or comments about the model:**
https://github.com/mu-cai/ViP-LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of ViP-LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
## Evaluation dataset
ViP-LLaVA achieves state-of-the-art performance in 4 academic region-level benchmarks and our newly proposed RegionBench.
|
CBrann/vae_KL_land_cover
|
CBrann
| 2024-01-23T09:59:13Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"license:mit",
"region:us"
] | null | 2024-01-23T09:55:38Z |
---
license: mit
---
A VAE model is fine tuned on discrete ESA land cover data. Not perfect but an improvement. Fine tuned using MSE loss.
|
stablediffusionapi/aipaint
|
stablediffusionapi
| 2024-01-23T09:53:53Z | 25 | 1 |
diffusers
|
[
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-23T09:51:51Z |
---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# AIPaint API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "aipaint"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/aipaint)
Model link: [View model](https://modelslab.com/models/aipaint)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "aipaint",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
signon-project/slr-poseformer-bsl
|
signon-project
| 2024-01-23T09:48:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-23T09:48:06Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for SLR PoseFormer BSL
<!-- Provide a quick summary of what the model is/does. -->
This model enables sign language recognition for British Sign Language using keypoint estimation.
Please refer to [this code base](https://github.com/signon-project/wp3-slr-pipeline) for more information.
|
signon-project/slr-poseformer-ngt
|
signon-project
| 2024-01-23T09:46:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-23T09:43:16Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for SLR PoseFormer NGT
<!-- Provide a quick summary of what the model is/does. -->
This model enables sign language recognition for Sign Language of the Netherlands using keypoint estimation.
Please refer to [this code base](https://github.com/signon-project/wp3-slr-pipeline) for more information.
|
ClovenDoug/tiny_64_all-MiniLM-L6-v2
|
ClovenDoug
| 2024-01-23T09:41:57Z | 6 | 1 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-01-22T07:09:19Z |
# Run Model this way:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ClovenDoug/tiny_64_all-MiniLM-L6-v2")
sentence_one = "I like cats"
embedding = model.encode(sentence_one)
print(embedding)
```
# tiny_64_all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model:
It maps sentences & paragraphs to a 64 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This gives it faster similarity comparison time although inference time will remain about the same.
This model was made using knowledge distillation techniques on the original 384 dimensional all-MiniLM-L6-v2 model.
The script for distilling this model into various sizes can be found here:
https://github.com/dorenwick/sentence_encoder_distillation
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
shibing624/vicuna-baichuan-13b-chat
|
shibing624
| 2024-01-23T09:25:56Z | 210 | 19 |
transformers
|
[
"transformers",
"pytorch",
"baichuan",
"text-generation",
"chatglm",
"zh",
"en",
"Text2Text-Generation",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-07T14:44:17Z |
---
language:
- zh
- en
tags:
- chatglm
- pytorch
- zh
- en
- Text2Text-Generation
license: "apache-2.0"
widget:
- text: "一岁宝宝发烧能吃啥药?"
---
# Vicuna Baichuan-13B Model
基于Baichaun-13B-chat的Vicuna微调对话模型
[shibing624/vicuna-baichuan-13b-chat](https://huggingface.co/shibing624/vicuna-baichuan-13b-chat) evaluate test data：
The overall performance of model on QA **test**:
|input_text|predict|
|:-- |:--- |
|一岁宝宝发烧能吃啥药?|1岁宝宝发烧时，可以考虑以下几种药物：<br/>1. 退烧药：如布洛芬(Ibuprofen)或对乙酰氨基酚(Paracetamol)。这些药物可以帮助降低体温，缓解发烧症状。<br/>2. 抗生素：如果发烧是由细菌感染引起的，可以考虑使用抗生素。但请务必在医生的建议下使用。<br/>3. 抗病毒药物：如果发烧是由病毒感染引起的，可以考虑使用抗病毒药物。但请务必在医生的建议下使用。<br/>4. 解热镇痛药：如阿司匹林(Aspirin)或对乙酰氨基酚(Paracetamol)。这些药物可以帮助缓解发烧症状。<br/>5. 抗组胺药：如苯海拉明(Diphenhydramine)或非索非那定(Naproxen)。这些药物可以帮助缓解发烧症状。<br/>请务必在医生的建议下使用这些药物，并遵循药物说明书上的用量和用法。同时，保持宝宝充足的水分摄入，并确保宝宝有足够的休息。|
在中文开放测试集中的表现优异，继承了两方面的优势：
1）微调训练的底座是 [baichuan-inc/Baichuan-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan-13B-Chat) 模型，是较强的中英文底座模型，
2）微调使用的是高质量10万条多语言GPT4对话数据集，微调后的模型在通用对话，包括医疗行业答复能力达到领先水平，在通用问题上的答复能力不弱于Baichuan-13B-Chat。
## Training details
training args:
```
{"per_device_train_batch_size": 2,
"per_device_eval_batch_size": 2,
"gradient_accumulation_steps": 1,
"learning_rate": 2e-05,
"num_train_epochs": 3.0,
"max_steps": -1,
"lr_scheduler_type": "linear",
"warmup_ratio": 0.0,
"warmup_steps": 50,
"save_strategy": "steps",
"save_steps": 500,
"save_total_limit": 13,
"seed": 42,
"bf16": true,
"fp16": false,
"eval_steps": 50,
"remove_unused_columns": false,
"load_best_model_at_end": true,
"metric_for_best_model": "loss",
"optim": "paged_adamw_32bit",
"report_to": ["tensorboard"],
}
```
- train loss: 1.3209
- evaluate loss: 1.4953
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
## Usage
本项目开源在 github repo:
- [shibing624/textgen](https://github.com/shibing624/textgen)
- [shibing624/MedicalGPT](https://github.com/shibing624/MedicalGPT)
使用textgen库：[textgen](https://github.com/shibing624/textgen)，可调用Baichuan/LLaMA模型：
Install package:
```shell
pip install -U textgen
```
```python
from textgen import GptModel
def generate_prompt(instruction):
return f"""A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.USER: {instruction} ASSISTANT: """
model = GptModel("baichuan", "shibing624/vicuna-baichuan-13b-chat")
predict_sentence = generate_prompt("一岁宝宝发烧能吃啥药?")
r = model.predict([predict_sentence])
print(r) # ["1、首先大多数小儿退热药中含有解热镇痛成分阿司匹林或布洛芬等，这类药品虽然副作用较少..."]
```
## Usage (HuggingFace Transformers)
Without [textgen](https://github.com/shibing624/textgen), you can use the model like this:
First, you pass your input through the transformer model, then you get the generated sentence.
Install package:
```
pip install transformers
```
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model = AutoModelForCausalLM.from_pretrained("shibing624/vicuna-baichuan-13b-chat", device_map='auto', torch_dtype=torch.float16, trust_remote_code=True)
model.generation_config = GenerationConfig.from_pretrained("shibing624/vicuna-baichuan-13b-chat", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("shibing624/vicuna-baichuan-13b-chat", trust_remote_code=True)
device = torch.device(0) if torch.cuda.is_available() else torch.device("cpu")
def generate_prompt(instruction):
return f"""A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.USER: {instruction} ASSISTANT: """
sents = ['一岁宝宝发烧能吃啥药', "who are you?"]
for s in sents:
q = generate_prompt(s)
inputs = tokenizer(q, return_tensors="pt")
inputs = inputs.to(device)
generate_ids = model.generate(
**inputs,
max_new_tokens=512,
)
output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True)[0]
print(output)
print()
```
output:
```shell
一岁宝宝发烧能吃啥药
孩子的身体是很脆弱的，如果不注意保护好自己就会出现各种疾病。有些家长在看到孩子生病后都非常着急，想要立刻给他们用药治疗，但其实并没有那么简单，因为每个人对于药物的敏感度和耐受性都是不同的，所以我们需要根据具体情况来选择合适的方法进行治疗才可以达到最佳效果。1、首先大多数小儿退热药中含有解热镇痛成分阿司匹林或布洛芬等，这类药品虽然副作用较少，但也存在过量使用引起胃肠道损伤甚至消化道溃疡出血的风险；2、还有部分小儿退热药中含有扑尔敏等抗组胺药，它们本身属于处方药，应该严格按照医嘱服用，切勿擅自加减剂量或者延长用药时间;3、此外，某些小儿退热药中含有维生素C及葡萄糖酸钙等营养补充剂，只有当患儿伴随脱水症状（如口渴、尿色深）时才考虑添加，否则容易造成电解质紊乱。
```
模型文件组成：
```
vicuna-baichuan-13b-chat
├── config.json
├── configuration_baichuan.py
├── generation_config.json
├── modeling_baichuan.py
├── pytorch_model-00001-of-00003.bin
├── pytorch_model-00002-of-00003.bin
├── pytorch_model-00003-of-00003.bin
├── pytorch_model.bin.index.json
├── quantizer.py
├── special_tokens_map.json
├── tokenization_baichuan.py
├── tokenizer_config.json
└── tokenizer.model
```
- Inference GPU: 27G
### Inference Examples
| Human | Assitant |
|------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 小孩发烧怎么办 | 发烧是身体对感染或其他疾病的自然反应。以下是一些可以帮助缓解发烧症状的建议：<br/>1. 保持身体水分充足：发烧时，身体会失去水分，因此需要保持水分充足。<br/>2. 使用冰袋：将冰袋放在额头或颈部可以帮助降低体温。<br/>3. 使用退烧药：退烧药可以帮助降低体温。<br/>4. 保持身体温度：保持身体温度可以帮助降低体温。<br/>5. 使用冷水或温水擦拭身体：使用冷水或温水擦拭身体可以帮助降低体温。<br/>6. 使用退烧药：退烧药可以帮助降低体温。<br/>7. 保持身体温度：保持身体温度可以帮助降低体温。<br/>8. 使用冷水或温水擦拭身体：使用冷水或温水擦拭身体可以帮助降低体温。<br/>9. 使用退烧药：退烧药可以帮助降低体温。<br/>10. 保持身体温度：保持身体温度可以帮助降低体温。 |
| 一岁宝宝发烧能吃啥药？ | 1岁宝宝发烧时，可以考虑以下几种药物：<br/>1. 退烧药：如布洛芬(Ibuprofen)或对乙酰氨基酚(Paracetamol)。这些药物可以帮助降低体温，缓解发烧症状。<br/>2. 抗生素：如果发烧是由细菌感染引起的，可以考虑使用抗生素。但请务必在医生的建议下使用。<br/>3. 抗病毒药物：如果发烧是由病毒感染引起的，可以考虑使用抗病毒药物。但请务必在医生的建议下使用。<br/>4. 解热镇痛药：如阿司匹林(Aspirin)或对乙酰氨基酚(Paracetamol)。这些药物可以帮助缓解发烧症状。<br/>5. 抗组胺药：如苯海拉明(Diphenhydramine)或非索非那定(Naproxen)。这些药物可以帮助缓解发烧症状。<br/>请务必在医生的建议下使用这些药物，并遵循药物说明书上的用量和用法。同时，保持宝宝充足的水分摄入，并确保宝宝有足够的休息。 |
| 肛门病变可能是什么疾病的症状 | 肛门病变可能由多种原因引起，包括感染、炎症、肿瘤或其他疾病。一些常见的肛门病变包括：<br/>1. 肛裂：这是一种肛门皮肤撕裂，通常由便秘或其他压力因素引起。<br/>2. 肛瘘：这是一种肛门感染，导致肛门皮肤和直肠之间形成瘘管。<br/>3. 肛门疣：这是一种肛门皮肤上形成的疣状病变，通常由人类乳头状瘤病毒(HPV)引起。<br/>4. 肛门脓肿：这是一种肛门感染，导致肛门皮肤下形成脓肿。<br/>5. 肛门肿瘤：这是一种肛门皮肤或直肠内形成的肿瘤。<br/>6. 肛门溃疡：这是一种肛门皮肤上形成的溃疡，通常由感染或炎症引起。<br/>7. 肛门瘙痒：这是一种肛门皮肤瘙痒的症状，可能由感染、炎症或其他疾病引起。<br/>如果您有肛门病变的症状，请咨询医疗保健专业人员以获得适当的诊断和治疗。 |
| 医生，您好白带增多泛黄怎么回事？ | 您好，白带增多和发黄可能是由多种原因引起的，包括感染、荷尔蒙变化、阴道干燥或其他妇科问题。以下是一些可能的原因：<br/>1. 感染：白带增多和发黄可能是由阴道或尿道感染引起的。感染可能由细菌、病毒或真菌引起。<br/>2. 荷尔蒙变化：荷尔蒙变化，如月经周期或更年期，可能会导致白带增多和发黄。<br/>3. 阴道干燥：阴道干燥可能会导致白带增多和发黄。<br/>4. 其他妇科问题：其他妇科问题，如子宫颈炎、子宫内膜炎或子宫内膜异位症，也可能导致白带增多和发黄。<br/>如果您担心自己的白带，建议您咨询妇科医生进行检查和诊断。医生可以根据您的症状和检查结果确定原因并提供适当的治疗。 |
| 药流后有巧克力白带是没流干净吗 | 药流后出现巧克力白带并不一定意味着没有流干净。巧克力白带是由于子宫内膜脱落和出血引起的，通常在药流后的一段时间内会出现。<br/><br/>药流后出现巧克力白带的原因可能包括：<br/><br/>1. 子宫内膜脱落：药流后，子宫内膜会脱落，导致出血。这种出血可能表现为巧克力白带。<br/>2. 感染：药流后，子宫内膜和宫颈口可能存在创伤，容易受到细菌感染。感染可能导致巧克力白带。<br/>3. 激素水平变化：药流后，激素水平可能发生变化，导致巧克力白带。<br/><br/>如果巧克力白带持续时间较长，或者伴有其他症状，如腹痛、发热、分泌物异味等，建议及时就诊，进行相关检查，如妇科检查、B超等，以确定是否存在感染或其他问题。在医生的指导下进行相应的治疗。 |
| 维胺酯维E乳膏能治理什么疾病 | 维胺酯维E乳膏是一种外用药物，主要用于治疗痤疮、脂溢性皮炎等皮肤病。它可以帮助减少皮肤油脂分泌，减轻炎症，促进皮肤修复。<br/><br/>需要注意的是，维胺酯维E乳膏并非万能药，对于某些皮肤病可能并不适用。在使用前，建议咨询医生或药师，了解药物的适应症、禁忌症、用法用量等信息。同时，使用药物时应遵循医嘱，避免自行滥用。 |
| 痤疮是什么原因导致的? | 痤疮，也称为粉刺，是由皮肤毛孔堵塞引起的。堵塞毛孔的原因有很多，包括：<br/>1. 皮脂腺过度活跃：皮脂腺产生过多油脂，导致毛孔堵塞。<br/>2. 毛孔堵塞：皮肤表面的死皮细胞、油脂和细菌堵塞毛孔。<br/>3. 炎症：痤疮通常伴随着炎症，这可能是由于皮肤感染或免疫系统对痤疮的反应引起的。<br/>4. 遗传因素：某些人可能更容易患痤疮，这可能是由于遗传因素引起的。<br/>5. 荷尔蒙变化：痤疮在青春期和月经期间更为常见，这可能是由于荷尔蒙变化引起的。<br/>6. 生活方式因素：某些生活方式因素，如压力、不良饮食和缺乏运动，也可能导致痤疮。<br/>7. 化妆品和护肤品：某些化妆品和护肤品可能导致痤疮，特别是那些含有刺激性成分的产品。<br/>8. 药物：某些药物，如口服避孕药，也可能导致痤疮。<br/>要预防痤疮，可以采取一些措施，如保持皮肤清洁、使用温和的护肤品、避免过度使用化妆品和护肤品、保持健康的生活方式和饮食习惯、避免压力和荷尔蒙变化。如果痤疮严重，可能需要寻求皮肤科医生的帮助。 |
| 脸上长青春痘怎么办,能吃啥药,需要注意啥 | 青春痘，又称痤疮，是一种常见的皮肤病。青春痘的治疗需要综合考虑饮食、生活习惯、药物等多个方面。以下是一些建议：<br/><br/>1. 饮食：<br/>* 避免高糖、高脂、高油的食物，如快餐、甜点、油炸食品等。<br/>* 多吃新鲜蔬菜、水果、全谷类食物，以增加纤维摄入，促进肠道蠕动。<br/>* 避免摄入过多的咖啡因、酒精、辛辣食物等刺激性食物。<br/><br/>2. 生活习惯：<br/>* 保持良好的作息时间，保证充足的睡眠。<br/>* 避免长时间使用电脑、手机等电子产品，以免辐射对皮肤造成刺激。<br/>* 保持皮肤清洁，每天早晚使用温和的洁面产品清洁皮肤。<br/>* 避免挤压青春痘，以免感染和留下疤痕。<br/><br/>3. 药物：<br/>* 外用药物：可以使用含有水杨酸、苯甲酸、壬二酸等成分的护肤品，帮助去除皮肤表面的角质，减少痘痘的形成。<br/>* 口服药物：如果青春痘严重，可以考虑口服抗生素、维生素B族、锌等药物，但需在医生指导下使用。<br/><br/>4. 注意：<br/>* 不要随意使用化妆品、护肤品，以免刺激皮肤。<br/>* 不要过度清洁皮肤，以免破坏皮肤屏障。<br/>* 保持心情舒畅，避免焦虑、抑郁等负面情绪。<br/><br/>总之，青春痘的治疗需要综合考虑饮食、生活习惯、药物等多个方面。在治疗过程中，要保持耐心和信心，遵循医生的建议，逐步改善皮肤状况。 |
| 由于输精管堵塞会不会导致男性不育？，医生您好，我同事这几天不开心，我问她怎么了，她说她老公输精管堵塞，所以一直没孩子，不是不想要，请问输精管堵塞会不会导致男性不育？ | 输精管堵塞可能会导致男性不育。输精管是连接睾丸和尿道的一条管道，负责将精子从睾丸输送到尿道。如果输精管堵塞，精子就无法通过输精管到达尿道，从而导致不育。<br/><br/>输精管堵塞的原因有很多，包括先天性发育异常、炎症、肿瘤、外伤等。输精管堵塞的治疗方法包括手术疏通、药物治疗等。如果输精管堵塞严重，可能需要进行手术疏通。<br/><br/>需要注意的是，输精管堵塞并不一定导致不育，因为还有其他途径可以将精子输送到女性体内。例如，精子可以通过附睾、前列腺等组织到达女性体内。因此，如果输精管堵塞，建议及时就医，寻求专业医生的帮助。 |
| 心肌梗塞能治疗吗？ | 心肌梗死是一种严重的疾病，如果及时治疗，可以挽救生命并防止长期残疾。治疗心肌梗死的关键是尽早识别和治疗。<br/><br/>心肌梗死的治疗通常包括以下步骤：<br/><br/>1. 立即就医：如果出现胸痛、呼吸急促或晕厥等症状，立即就医。<br/><br/>2. 药物治疗：医生可能会开处方药物，如硝酸甘油或阿司匹林，以帮助缓解症状并防止进一步的损伤。<br/><br/>3. 溶栓治疗：如果患者在到达医院前出现心肌梗死，医生可能会进行溶栓治疗，通过注射溶解血栓的药物来帮助恢复血流。<br/><br/>4. 手术治疗：如果药物治疗或溶栓治疗无效，医生可能会建议进行手术，如冠状动脉搭桥手术或心脏支架植入术。<br/><br/>5. 康复和预防：心肌梗死的治疗通常包括康复计划，以帮助患者恢复身体功能并预防未来的心脏事件。此外，患者还应遵循医生的建议，以降低患心脏病的风险。<br/><br/>需要注意的是，心肌梗死的治疗取决于患者的具体情况和症状。因此，如果您或您认识的人出现心肌梗死症状，请立即就医。 |
| 怎么让自己精力充沛，列5点建议 | 1. 保持充足的睡眠：睡眠对于保持精力充沛至关重要。确保每晚获得7-9小时的高质量睡眠。<br/>2. 均衡饮食：均衡饮食对于保持能量水平至关重要。确保摄入足够的蛋白质、碳水化合物和健康脂肪。<br/>3. 定期锻炼：定期锻炼可以帮助提高能量水平。尝试每天进行至少30分钟的有氧运动，如散步、跑步或骑自行车。<br/>4. 管理压力：压力会消耗能量，因此管理压力对于保持精力充沛至关重要。尝试找到放松和减压的方法，如冥想、瑜伽或深呼吸练习。<br/>5. 保持水分：脱水会降低能量水平。确保每天喝足够的水，并根据需要补充水分。 |
| 写个春天的诗 | 春天，<br/>是大地苏醒的季节，<br/>是万物复苏的季节，<br/>是生命绽放的季节。<br/>春天，<br/>是阳光明媚的季节，<br/>是鸟语花香的季节，<br/>是绿意盎然的季节，<br/>是希望无限的季节。<br/>春天，<br/>是人们欢笑的季节，<br/>是孩子们嬉戏的季节，<br/>是恋人们相拥的季节，<br/>是人们追求梦想的季节。 |
| 南极洲为啥没有北极熊? | 南极洲没有北极熊，因为北极熊生活在北极地区，而南极洲位于南半球，气候和环境与北极地区有很大不同。南极洲的气候非常寒冷，常年被冰雪覆盖，而北极熊则生活在北极地区，那里有丰富的海洋生物和陆地哺乳动物。此外，南极洲的生态系统也与北极地区不同，没有北极熊所需的生存条件。 |
### 训练数据集
- 10万条多语言ShareGPT GPT4多轮对话数据集：[shibing624/sharegpt_gpt4](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
如果需要训练ChatGLM/LLAMA/BLOOM模型，请参考[shibing624/MedicalGPT](https://github.com/shibing624/MedicalGPT)
- training script:
```shell
CUDA_VISIBLE_DEVICES=0,1,2 torchrun --nproc_per_node 3 supervised_finetuning.py \
--model_type baichuan \
--model_name_or_path baichuan-inc/Baichuan-13B-Chat \
--train_file_dir path_of_sharegpt_gpt4 \
--validation_file_dir data/finetune/ \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--template_name vicuna \
--do_train \
--do_eval \
--use_peft True --bf16 \
--max_train_samples -1 \
--max_source_length 1024 --max_target_length 1024 \
--max_eval_samples 50 \
--num_train_epochs 3 \
--learning_rate 2e-5 \
--warmup_ratio 0.05 \
--weight_decay 0.05 \
--logging_strategy steps \
--logging_steps 10 \
--eval_steps 50 \
--evaluation_strategy steps \
--save_steps 1000 \
--save_strategy steps \
--save_total_limit 13 \
--preprocessing_num_workers 10 \
--output_dir outputs-sft--baichuan-v3-production \
--overwrite_output_dir \
--ddp_timeout 300000 \
--logging_first_step True \
--target_modules all \
--lora_rank 8 \
--lora_alpha 16 \
--lora_dropout 0.05 \
--torch_dtype bfloat16 --optim paged_adamw_32bit \
--device_map auto \
--report_to tensorboard \
--ddp_find_unused_parameters False \
--gradient_checkpointing True --qlora True --cache_dir ./cache
```
- 机器：3*A100(40G), 36hours
## Citation
```latex
@software{textgen,
author = {Ming Xu},
title = {textgen: Implementation of language model finetune},
year = {2023},
url = {https://github.com/shibing624/textgen},
}
```
|
Abhinav28/large-v3-hi-commonvoice-11-peft-trained-adapter-withfp16-30-percent-droupout-0.1
|
Abhinav28
| 2024-01-23T09:24:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Abhinav28/openai-whisper-large-v3",
"base_model:adapter:Abhinav28/openai-whisper-large-v3",
"region:us"
] | null | 2024-01-23T09:24:12Z |
---
library_name: peft
base_model: Abhinav28/openai-whisper-large-v3
---
# 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]
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[More Information Needed]
### 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
### 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 0.7.2.dev0
|
OmBenz/whisper-tiny
|
OmBenz
| 2024-01-23T09:16:19Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-23T09:10:00Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3382526564344746
---
<!-- 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-tiny
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5278
- Wer Ortho: 0.3436
- Wer: 0.3383
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 120
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 3.4388 | 1.43 | 20 | 2.2576 | 0.5022 | 0.4038 |
| 1.1317 | 2.86 | 40 | 0.5981 | 0.4047 | 0.3932 |
| 0.3235 | 4.29 | 60 | 0.4967 | 0.3720 | 0.3707 |
| 0.138 | 5.71 | 80 | 0.5035 | 0.3356 | 0.3282 |
| 0.0563 | 7.14 | 100 | 0.5198 | 0.3362 | 0.3294 |
| 0.033 | 8.57 | 120 | 0.5278 | 0.3436 | 0.3383 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
yc4142/RedPajama-INCITE-Instruct-3B-v1-lora-bias-CoT
|
yc4142
| 2024-01-23T09:16:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-23T09:16:05Z |
---
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]
- **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]
|
ayeshgk/codet5-small-ft-v8-cpatd
|
ayeshgk
| 2024-01-23T09:15:28Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5-small",
"base_model:finetune:Salesforce/codet5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-23T07:42:08Z |
---
license: apache-2.0
base_model: Salesforce/codet5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: codet5-small-ft-v8-cpatd
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. -->
# codet5-small-ft-v8-cpatd
This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4831
- Rouge1: 63.6456
- Rouge2: 52.7919
- Rougel: 63.1978
- Rougelsum: 63.2113
- Gen Len: 17.0514
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.6958 | 1.0 | 3063 | 0.5815 | 61.3112 | 50.8406 | 60.8334 | 60.8509 | 17.2215 |
| 0.6116 | 2.0 | 6126 | 0.5190 | 62.7088 | 51.7838 | 62.2533 | 62.2661 | 17.0108 |
| 0.5564 | 3.0 | 9189 | 0.4942 | 63.4158 | 52.592 | 62.9418 | 62.9443 | 17.1236 |
| 0.5447 | 4.0 | 12252 | 0.4831 | 63.6456 | 52.7919 | 63.1978 | 63.2113 | 17.0514 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
AISimplyExplained/Mistral-LALR64
|
AISimplyExplained
| 2024-01-23T09:11:01Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/mistral-7b",
"base_model:adapter:unsloth/mistral-7b",
"region:us"
] | null | 2024-01-10T11:55:26Z |
---
library_name: peft
base_model: unsloth/mistral-7b
---
# 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 0.7.1
|
anilbhatt1/gpt-scratch-karpathy
|
anilbhatt1
| 2024-01-23T09:09:49Z | 0 | 0 | null |
[
"art",
"text-generation",
"en",
"license:mit",
"region:us"
] |
text-generation
| 2023-10-27T10:03:21Z |
---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- art
---
|
Sprakbanken/norhand-hcr-beta1
|
Sprakbanken
| 2024-01-23T09:02:46Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/trocr-base-stage1",
"base_model:finetune:microsoft/trocr-base-stage1",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-11-28T14:22:24Z |
---
base_model: microsoft/trocr-base-stage1
tags:
- generated_from_trainer
model-index:
- name: norhand-hcr-beta1
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. -->
# norhand-hcr-beta1
This model is a fine-tuned version of [microsoft/trocr-base-stage1](https://huggingface.co/microsoft/trocr-base-stage1) on the NorHand dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4687
- Cer: 0.0439
It achieves the following results on the test set:
- CER: 8.3
- WER: 22.07
This is work in progress.
## 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: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.7606 | 0.71 | 5000 | 1.0489 | 0.1266 |
| 0.5971 | 1.43 | 10000 | 1.2096 | 0.1028 |
| 0.4448 | 2.14 | 15000 | 1.3738 | 0.0771 |
| 0.3919 | 2.85 | 20000 | 1.2733 | 0.0670 |
| 0.2806 | 3.56 | 25000 | 1.3048 | 0.0597 |
| 0.2055 | 4.28 | 30000 | 1.3568 | 0.0557 |
| 0.2486 | 4.99 | 35000 | 0.9717 | 0.0527 |
| 0.1631 | 5.7 | 40000 | 1.3159 | 0.0506 |
| 0.1159 | 6.41 | 45000 | 1.3730 | 0.0480 |
| 0.0605 | 7.13 | 50000 | 1.3399 | 0.0469 |
| 0.0626 | 7.84 | 55000 | 1.3642 | 0.0460 |
| 0.0532 | 8.55 | 60000 | 1.3870 | 0.0450 |
| 0.0177 | 9.26 | 65000 | 1.4912 | 0.0439 |
| 0.0308 | 9.98 | 70000 | 1.4687 | 0.0439 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
|
aws-neuron/Mistral-7B-Instruct-v0.1-neuron-4x2048-24-cores
|
aws-neuron
| 2024-01-23T08:57:07Z | 1 | 0 |
transformers
|
[
"transformers",
"mistral",
"text-generation",
"facebook",
"meta",
"pytorch",
"inferentia2",
"neuron",
"conversational",
"en",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-23T08:49:10Z |
---
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- mistral
- inferentia2
- neuron
---
# Neuronx model for [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
This repository contains [**AWS Inferentia2**](https://aws.amazon.com/ec2/instance-types/inf2/) and [`neuronx`](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/) compatible checkpoints for [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf).
You can find detailed information about the base model on its [Model Card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
This model has been exported to the `neuron` format using specific `input_shapes` and `compiler` parameters detailed in the paragraphs below.
Please refer to the 🤗 `optimum-neuron` [documentation](https://huggingface.co/docs/optimum-neuron/main/en/guides/models#configuring-the-export-of-a-generative-model) for an explanation of these parameters.
## Usage on Amazon SageMaker
_coming soon_
## Usage with 🤗 `optimum-neuron`
```python
>>> from optimum.neuron import pipeline
>>> p = pipeline('text-generation', 'aws-neuron/Mistral-7B-Instruct-v0.1-neuron-4x2048-24-cores')
>>> p("My favorite place on earth is", max_new_tokens=64, do_sample=True, top_k=50)
[{'generated_text': 'My favorite place on earth is the ocean. It is where I feel most
at peace. I love to travel and see new places. I have a'}]
```
This repository contains tags specific to versions of `neuronx`. When using with 🤗 `optimum-neuron`, use the repo revision specific to the version of `neuronx` you are using, to load the right serialized checkpoints.
## Arguments passed during export
**input_shapes**
```json
{
"batch_size": 4,
"sequence_length": 2048,
}
```
**compiler_args**
```json
{
"auto_cast_type": "bf16",
"num_cores": 24,
}
```
|
aws-neuron/Mistral-7B-Instruct-v0.1-neuron-1x2048-2-cores
|
aws-neuron
| 2024-01-23T08:54:16Z | 833 | 0 |
transformers
|
[
"transformers",
"mistral",
"text-generation",
"facebook",
"meta",
"pytorch",
"inferentia2",
"neuron",
"conversational",
"en",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-23T08:50:19Z |
---
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- mistral
- inferentia2
- neuron
---
# Neuronx model for [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
This repository contains [**AWS Inferentia2**](https://aws.amazon.com/ec2/instance-types/inf2/) and [`neuronx`](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/) compatible checkpoints for [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf).
You can find detailed information about the base model on its [Model Card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
This model has been exported to the `neuron` format using specific `input_shapes` and `compiler` parameters detailed in the paragraphs below.
Please refer to the 🤗 `optimum-neuron` [documentation](https://huggingface.co/docs/optimum-neuron/main/en/guides/models#configuring-the-export-of-a-generative-model) for an explanation of these parameters.
## Usage on Amazon SageMaker
_coming soon_
## Usage with 🤗 `optimum-neuron`
```python
>>> from optimum.neuron import pipeline
>>> p = pipeline('text-generation', 'aws-neuron/Mistral-7B-Instruct-v0.1-neuron-1x2048-2-cores')
>>> p("My favorite place on earth is", max_new_tokens=64, do_sample=True, top_k=50)
[{'generated_text': 'My favorite place on earth is the ocean. It is where I feel most
at peace. I love to travel and see new places. I have a'}]
```
This repository contains tags specific to versions of `neuronx`. When using with 🤗 `optimum-neuron`, use the repo revision specific to the version of `neuronx` you are using, to load the right serialized checkpoints.
## Arguments passed during export
**input_shapes**
```json
{
"batch_size": 1,
"sequence_length": 2048,
}
```
**compiler_args**
```json
{
"auto_cast_type": "bf16",
"num_cores": 2,
}
```
|
lukasbahrenberg/distilbert-base-uncased-finetuned-emotion
|
lukasbahrenberg
| 2024-01-23T08:52:05Z | 6 | 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
| 2024-01-23T07:45:01Z |
---
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: 0.923
- name: F1
type: f1
value: 0.9229783194871606
---
<!-- 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: 0.2250
- Accuracy: 0.923
- F1: 0.9230
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3382 | 0.91 | 0.9088 |
| No log | 2.0 | 500 | 0.2250 | 0.923 | 0.9230 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
karawalla/mistral_b_karawalla_aqtraining24008
|
karawalla
| 2024-01-23T08:47:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-23T08:47:35Z |
---
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]
|
s3nh/Daisuke-GGUF
|
s3nh
| 2024-01-23T08:42:25Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T07:07:55Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/TheBigBlender/Daisuke).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### inference
User: Tell me story about what is an quantization and what do we need to build.
Answer: Quantum computing refers to the use of quantum mechanics principles to perform calculations and solve problems that are intractable for classical computers. In simple terms, it aims to exploit the strange properties of particles at the atomic and subatomic level, like superposition and entanglement, for advanced information processing.
To build a quantum computer, we first need to create a system where individual qubits (quantum bits) can be manipulated and controlled. This requires the construction of specialized hardware called a quantum processor or quantum circuitry. The qubits themselves can be realized in various ways:
1)
# Original model card
|
alvinancala/finetuded_model
|
alvinancala
| 2024-01-23T08:40:14Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2024-01-21T07:38:18Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
model-index:
- name: finetuded_model
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. -->
# finetuded_model
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-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 0.7.1
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
nullne/ppoPyramidsRND
|
nullne
| 2024-01-23T08:28:44Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-01-23T08:28:38Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: nullne/ppoPyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DakshJ/falcon7binstruct_counselchat_jan22
|
DakshJ
| 2024-01-23T08:20:17Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"base_model:adapter:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | null | 2024-01-22T05:07:05Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
model-index:
- name: falcon7binstruct_counselchat_jan22
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. -->
# falcon7binstruct_counselchat_jan22
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Rupesh2/mistral_b_college_finetuned_test
|
Rupesh2
| 2024-01-23T08:09:54Z | 8 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-23T08:06:42Z |
---
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]
|
noczero/mistralai-7B-v01-alpaca-demo
|
noczero
| 2024-01-23T08:02:14Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-01-22T01:33:32Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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 0.7.2.dev0
|
imagepipeline/Detail
|
imagepipeline
| 2024-01-23T08:01:15Z | 0 | 0 | null |
[
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-23T08:01:14Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## Detail
<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/530ba359-f680-4704-aeb1-3fefb2cd632d/width=450/02503-1337.jpeg" alt="Generated by Image Pipeline" style="border-radius: 10px;">
**This embedding model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - Adds overall detail to the image, can add swirls and lace parts to clothing and as makeup or tattoos
[](https://imagepipeline.io/models/Detail?id=6bd87fbd-abfd-4c74-9318-aa802bfd6678/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sd/text2image/v1/run"
payload = json.dumps({
"model_id": "sd1.5",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "6bd87fbd-abfd-4c74-9318-aa802bfd6678",
"lora_models": "",
"lora_weights": ""
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sd/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at [email protected]
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
caisarl76/batch1_epochs4_lr1e-05_paged_adamw_32bit_cosine_length2048_warmup_0.05_max_grad1.0_grad_accu32
|
caisarl76
| 2024-01-23T07:51:22Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:MNC-LLM/Mistral-7B-LaAdMoAl-merge-Marcoroni",
"base_model:finetune:MNC-LLM/Mistral-7B-LaAdMoAl-merge-Marcoroni",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-03T07:37:21Z |
---
base_model: MNC-LLM/Mistral-7B-LaAdMoAl-merge-Marcoroni
tags:
- generated_from_trainer
model-index:
- name: batch1_epochs4_lr1e-05_paged_adamw_32bit_cosine_length2048_warmup_0.05_max_grad1.0_grad_accu32
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. -->
# batch1_epochs4_lr1e-05_paged_adamw_32bit_cosine_length2048_warmup_0.05_max_grad1.0_grad_accu32
This model is a fine-tuned version of [MNC-LLM/Mistral-7B-LaAdMoAl-merge-Marcoroni](https://huggingface.co/MNC-LLM/Mistral-7B-LaAdMoAl-merge-Marcoroni) 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
nullne/ppo-SnowballTarget
|
nullne
| 2024-01-23T07:45:40Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-01-23T07:45:33Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: nullne/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dball/zephyr-7b-sft-qlora
|
dball
| 2024-01-23T07:29:59Z | 15 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"sft",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-01-19T15:16:11Z |
---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: zephyr-7b-sft-qlora
results: []
---
# zephyr-7b-sft-qlora
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4ultrachat_200k dataset.
It is the first step (Step 1 SFT, see below) of building Zephyr, i.e. before DPO.
It achieves the following results on the evaluation set:
- Loss: 0.9523
## Model description
QLoRA SFT via
```
# Step 1 - SFT
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml --num_processes=1 scripts/run_sft.py recipes/zephyr-7b-beta/sft/config_qlora.yaml --load_in_4bit=true
```
see https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/README.md
## Intended uses & limitations
```
chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
```
see https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_qlora.yaml
## Training and evaluation data
```
dataset_mixer:
HuggingFaceH4/ultrachat_200k: 1.0
dataset_splits:
- train_sft
- test_sft
```
see https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_qlora.yaml
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.913 | 1.0 | 17428 | 0.9523 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.0
|
smutuvi/whisper-small-sw-ndizi-248
|
smutuvi
| 2024-01-23T07:27:33Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:smutuvi/whisper-small-sw-common-voice",
"base_model:adapter:smutuvi/whisper-small-sw-common-voice",
"license:apache-2.0",
"region:us"
] | null | 2024-01-23T07:27:32Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: smutuvi/whisper-small-sw-common-voice
model-index:
- name: whisper-small-sw-ndizi-248
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-small-sw-ndizi-248
This model is a fine-tuned version of [smutuvi/whisper-small-sw-common-voice](https://huggingface.co/smutuvi/whisper-small-sw-common-voice) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4545
## 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: 22
- total_train_batch_size: 352
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 1.5588 |
| No log | 2.0 | 3 | 1.5582 |
| No log | 3.0 | 4 | 1.5604 |
| No log | 4.0 | 6 | 1.5600 |
| No log | 5.0 | 8 | 1.5584 |
| No log | 6.0 | 9 | 1.5564 |
| No log | 7.0 | 11 | 1.5573 |
| No log | 8.0 | 13 | 1.5578 |
| No log | 9.0 | 14 | 1.5555 |
| No log | 10.0 | 16 | 1.5526 |
| No log | 11.0 | 17 | 1.5512 |
| No log | 12.0 | 18 | 1.5543 |
| No log | 13.0 | 20 | 1.5531 |
| No log | 14.0 | 21 | 1.5537 |
| No log | 15.0 | 23 | 1.5485 |
| 0.7121 | 16.0 | 25 | 1.5470 |
| 0.7121 | 17.0 | 26 | 1.5472 |
| 0.7121 | 18.0 | 28 | 1.5438 |
| 0.7121 | 19.0 | 30 | 1.5405 |
| 0.7121 | 20.0 | 31 | 1.5388 |
| 0.7121 | 21.0 | 33 | 1.5370 |
| 0.7121 | 22.0 | 34 | 1.5358 |
| 0.7121 | 23.0 | 35 | 1.5341 |
| 0.7121 | 24.0 | 37 | 1.5292 |
| 0.7121 | 25.0 | 38 | 1.5314 |
| 0.7121 | 26.0 | 40 | 1.5298 |
| 0.7121 | 27.0 | 42 | 1.5236 |
| 0.7121 | 28.0 | 43 | 1.5198 |
| 0.7121 | 29.0 | 45 | 1.5203 |
| 0.7121 | 30.0 | 47 | 1.5123 |
| 0.7121 | 31.0 | 48 | 1.5138 |
| 0.6959 | 32.0 | 50 | 1.5080 |
| 0.6959 | 33.0 | 51 | 1.5100 |
| 0.6959 | 34.0 | 52 | 1.5065 |
| 0.6959 | 35.0 | 54 | 1.5011 |
| 0.6959 | 36.0 | 55 | 1.4980 |
| 0.6959 | 37.0 | 57 | 1.4947 |
| 0.6959 | 38.0 | 59 | 1.4924 |
| 0.6959 | 39.0 | 60 | 1.4910 |
| 0.6959 | 40.0 | 62 | 1.4885 |
| 0.6959 | 41.0 | 64 | 1.4863 |
| 0.6959 | 42.0 | 65 | 1.4854 |
| 0.6959 | 43.0 | 67 | 1.4784 |
| 0.6959 | 44.0 | 68 | 1.4784 |
| 0.6959 | 45.0 | 69 | 1.4794 |
| 0.6959 | 46.0 | 71 | 1.4727 |
| 0.6959 | 47.0 | 72 | 1.4713 |
| 0.6959 | 48.0 | 74 | 1.4678 |
| 0.7021 | 49.0 | 76 | 1.4735 |
| 0.7021 | 50.0 | 77 | 1.4666 |
| 0.7021 | 51.0 | 79 | 1.4670 |
| 0.7021 | 52.0 | 81 | 1.4634 |
| 0.7021 | 53.0 | 82 | 1.4646 |
| 0.7021 | 54.0 | 84 | 1.4603 |
| 0.7021 | 55.0 | 85 | 1.4617 |
| 0.7021 | 56.0 | 86 | 1.4626 |
| 0.7021 | 57.0 | 88 | 1.4637 |
| 0.7021 | 58.0 | 89 | 1.4614 |
| 0.7021 | 59.0 | 91 | 1.4589 |
| 0.7021 | 60.0 | 93 | 1.4584 |
| 0.7021 | 61.0 | 94 | 1.4623 |
| 0.7021 | 62.0 | 96 | 1.4581 |
| 0.7021 | 63.0 | 98 | 1.4566 |
| 0.7021 | 64.0 | 99 | 1.4574 |
| 0.6465 | 64.43 | 100 | 1.4545 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.38.0.dev0
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
soroushbn/llama-2-7b-persianft
|
soroushbn
| 2024-01-23T07:20:24Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"pytorch",
"llama",
"conversational",
"fa",
"dataset:soroushbn/alpaca-persian-llama",
"region:us"
] |
text-generation
| 2024-01-22T15:06:23Z |
---
datasets:
- soroushbn/alpaca-persian-llama
language:
- fa
library_name: adapter-transformers
pipeline_tag: conversational
---
|
mathurinache/Odysseas-11B
|
mathurinache
| 2024-01-23T07:20:08Z | 1,361 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"vicgalle/CarbonBeagle-11B",
"jeonsworld/CarbonVillain-en-10.7B-v4",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T07:19:27Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- vicgalle/CarbonBeagle-11B
- jeonsworld/CarbonVillain-en-10.7B-v4
---
# Odysseas-11B
Odysseas-11B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [vicgalle/CarbonBeagle-11B](https://huggingface.co/vicgalle/CarbonBeagle-11B)
* [jeonsworld/CarbonVillain-en-10.7B-v4](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: vicgalle/CarbonBeagle-11B
layer_range: [0, 32]
- model: jeonsworld/CarbonVillain-en-10.7B-v4
layer_range: [0, 32]
merge_method: slerp
base_model: vicgalle/CarbonBeagle-11B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
zz001/002
|
zz001
| 2024-01-23T07:07:01Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"base_model:adapter:cagliostrolab/animagine-xl-3.0",
"license:mit",
"region:us"
] |
text-to-image
| 2024-01-23T07:06:58Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/XbpD1BVwv8.png
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: '002'
license: mit
---
# 11
<Gallery />
## Model description
111
## Trigger words
You should use `002` to trigger the image generation.
## Download model
[Download](/zz001/002/tree/main) them in the Files & versions tab.
|
GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15
|
GAI-LLM
| 2024-01-23T07:01:56Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T02:15:59Z |
---
license: cc-by-nc-4.0
language:
- ko
library_name: transformers
pipeline_tag: text-generation
---
**The license is `cc-by-nc-4.0`.**
# **GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15**
## Model Details
**Model Developers** Donghoon Oh, Hanmin Myung, Eunyoung Kim (SK C&C G.AI Eng)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15 is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Base Model** [beomi/OPEN-SOLAR-KO-10.7B](https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B)
**Training Dataset**
- We combined Open Korean Dateset using mixed-strategy
- We use A100 GPU 80GB * 8, when training.
# **Model Benchmark**
## KO-LLM leaderboard
- Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).
# Implementation Code
```python
### GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)
```
|
bellylee/dummy-model
|
bellylee
| 2024-01-23T06:59:25Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-01-23T06:53:35Z |
---
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]
|
FinleyTheTegu/NeuralPipe-7B-slerp
|
FinleyTheTegu
| 2024-01-23T06:58:00Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:merge:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T06:50:46Z |
---
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
base_model:
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
---
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "FinleyTheTegu/NeuralPipe-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
yoon1000/ft_0123_korean_1
|
yoon1000
| 2024-01-23T06:57:44Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-23T00:56:21Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: ft_0123_korean_1
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. -->
# ft_0123_korean_1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7160
- Cer: 0.1832
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 60.2779 | 0.09 | 100 | 120.0675 | 0.9556 |
| 45.2315 | 0.18 | 200 | 66.7612 | 1.0 |
| 30.7852 | 0.27 | 300 | 50.1041 | 1.0 |
| 22.2754 | 0.35 | 400 | 31.5537 | 1.0 |
| 13.4785 | 0.44 | 500 | 13.3387 | 1.0 |
| 6.4901 | 0.53 | 600 | 5.4424 | 1.0 |
| 4.8734 | 0.62 | 700 | 5.0449 | 1.0 |
| 4.7501 | 0.71 | 800 | 4.9923 | 1.0 |
| 4.7477 | 0.8 | 900 | 4.9037 | 1.0 |
| 4.7511 | 0.88 | 1000 | 4.9181 | 1.0 |
| 4.7076 | 0.97 | 1100 | 4.8488 | 1.0 |
| 4.6829 | 1.06 | 1200 | 4.7397 | 1.0 |
| 4.6768 | 1.15 | 1300 | 4.7180 | 1.0 |
| 4.6089 | 1.24 | 1400 | 4.6611 | 1.0 |
| 4.56 | 1.33 | 1500 | 4.6175 | 1.0 |
| 4.499 | 1.41 | 1600 | 4.5056 | 0.9999 |
| 4.38 | 1.5 | 1700 | 4.2237 | 0.9857 |
| 4.1179 | 1.59 | 1800 | 3.8646 | 0.9816 |
| 3.7534 | 1.68 | 1900 | 3.2313 | 0.6453 |
| 3.3149 | 1.77 | 2000 | 2.8237 | 0.5349 |
| 3.0137 | 1.86 | 2100 | 2.5717 | 0.5091 |
| 2.8094 | 1.94 | 2200 | 2.3614 | 0.4624 |
| 2.5519 | 2.03 | 2300 | 2.2160 | 0.4501 |
| 2.4768 | 2.12 | 2400 | 2.0897 | 0.4274 |
| 2.3625 | 2.21 | 2500 | 1.9856 | 0.4168 |
| 2.2876 | 2.3 | 2600 | 1.9065 | 0.4016 |
| 2.2289 | 2.39 | 2700 | 1.8137 | 0.3930 |
| 2.0996 | 2.47 | 2800 | 1.7548 | 0.3755 |
| 2.0683 | 2.56 | 2900 | 1.6713 | 0.3590 |
| 2.0371 | 2.65 | 3000 | 1.6082 | 0.3528 |
| 1.8858 | 2.74 | 3100 | 1.5596 | 0.3453 |
| 1.8692 | 2.83 | 3200 | 1.5088 | 0.3346 |
| 1.8373 | 2.92 | 3300 | 1.4700 | 0.3299 |
| 1.8318 | 3.0 | 3400 | 1.4286 | 0.3208 |
| 1.7482 | 3.09 | 3500 | 1.4024 | 0.3151 |
| 1.6887 | 3.18 | 3600 | 1.3581 | 0.3126 |
| 1.6574 | 3.27 | 3700 | 1.3161 | 0.2998 |
| 1.6119 | 3.36 | 3800 | 1.2720 | 0.2924 |
| 1.6103 | 3.45 | 3900 | 1.2531 | 0.2921 |
| 1.5362 | 3.53 | 4000 | 1.2326 | 0.2917 |
| 1.4972 | 3.62 | 4100 | 1.1904 | 0.2816 |
| 1.5005 | 3.71 | 4200 | 1.1757 | 0.2784 |
| 1.4586 | 3.8 | 4300 | 1.1463 | 0.2737 |
| 1.4483 | 3.89 | 4400 | 1.1246 | 0.2694 |
| 1.4354 | 3.98 | 4500 | 1.0976 | 0.2641 |
| 1.3648 | 4.06 | 4600 | 1.0730 | 0.2606 |
| 1.3194 | 4.15 | 4700 | 1.0460 | 0.2579 |
| 1.3316 | 4.24 | 4800 | 1.0362 | 0.2516 |
| 1.3138 | 4.33 | 4900 | 1.0166 | 0.2475 |
| 1.3217 | 4.42 | 5000 | 0.9917 | 0.2456 |
| 1.2914 | 4.51 | 5100 | 0.9835 | 0.2411 |
| 1.2364 | 4.59 | 5200 | 0.9647 | 0.2409 |
| 1.2034 | 4.68 | 5300 | 0.9621 | 0.2368 |
| 1.2028 | 4.77 | 5400 | 0.9255 | 0.2311 |
| 1.2354 | 4.86 | 5500 | 0.9119 | 0.2280 |
| 1.2295 | 4.95 | 5600 | 0.9113 | 0.2287 |
| 1.2007 | 5.04 | 5700 | 0.8934 | 0.2229 |
| 1.1637 | 5.12 | 5800 | 0.8867 | 0.2256 |
| 1.1221 | 5.21 | 5900 | 0.8787 | 0.2213 |
| 1.171 | 5.3 | 6000 | 0.8607 | 0.2176 |
| 1.1042 | 5.39 | 6100 | 0.8514 | 0.2171 |
| 1.063 | 5.48 | 6200 | 0.8510 | 0.2175 |
| 1.0965 | 5.57 | 6300 | 0.8355 | 0.2107 |
| 1.0611 | 5.65 | 6400 | 0.8298 | 0.2096 |
| 1.0697 | 5.74 | 6500 | 0.8149 | 0.2074 |
| 1.0342 | 5.83 | 6600 | 0.8043 | 0.2037 |
| 1.0586 | 5.92 | 6700 | 0.8060 | 0.2028 |
| 1.0553 | 6.01 | 6800 | 0.8017 | 0.2029 |
| 1.0369 | 6.1 | 6900 | 0.7906 | 0.2015 |
| 0.9646 | 6.18 | 7000 | 0.7870 | 0.1987 |
| 0.9747 | 6.27 | 7100 | 0.7836 | 0.1970 |
| 0.9933 | 6.36 | 7200 | 0.7708 | 0.1963 |
| 0.9793 | 6.45 | 7300 | 0.7740 | 0.1957 |
| 0.9642 | 6.54 | 7400 | 0.7618 | 0.1934 |
| 0.9936 | 6.63 | 7500 | 0.7554 | 0.1919 |
| 0.9466 | 6.71 | 7600 | 0.7438 | 0.1891 |
| 0.9597 | 6.8 | 7700 | 0.7437 | 0.1900 |
| 0.9374 | 6.89 | 7800 | 0.7415 | 0.1909 |
| 0.9719 | 6.98 | 7900 | 0.7352 | 0.1908 |
| 0.9067 | 7.07 | 8000 | 0.7358 | 0.1880 |
| 0.8998 | 7.16 | 8100 | 0.7329 | 0.1879 |
| 0.9271 | 7.24 | 8200 | 0.7262 | 0.1864 |
| 0.8951 | 7.33 | 8300 | 0.7217 | 0.1860 |
| 0.9136 | 7.42 | 8400 | 0.7239 | 0.1854 |
| 0.9446 | 7.51 | 8500 | 0.7214 | 0.1844 |
| 0.8978 | 7.6 | 8600 | 0.7220 | 0.1837 |
| 0.8923 | 7.69 | 8700 | 0.7174 | 0.1838 |
| 0.9406 | 7.77 | 8800 | 0.7187 | 0.1836 |
| 0.9242 | 7.86 | 8900 | 0.7159 | 0.1836 |
| 0.8994 | 7.95 | 9000 | 0.7160 | 0.1832 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
vipul319/cybersecurity
|
vipul319
| 2024-01-23T06:50:41Z | 0 | 0 |
keras
|
[
"keras",
"text-classification",
"en",
"dataset:pki/autonlp-data-cybersecurity",
"dataset:AR2021/cybersecurity-corpus-llama2-1k",
"dataset:bnsapa/cybersecurity-ner",
"license:mit",
"region:us"
] |
text-classification
| 2024-01-23T06:43:45Z |
---
license: mit
datasets:
- pki/autonlp-data-cybersecurity
- AR2021/cybersecurity-corpus-llama2-1k
- bnsapa/cybersecurity-ner
language:
- en
library_name: keras
pipeline_tag: text-classification
---
|
Valliyammai/finetuning-sentiment-model-3004-samples
|
Valliyammai
| 2024-01-23T06:47:00Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-23T04:03:07Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3004-samples
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. -->
# finetuning-sentiment-model-3004-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3248
- Accuracy: 0.8633
- F1: 0.8664
## 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: 2
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
AstraliteHeart/pony-diffusion-v6
|
AstraliteHeart
| 2024-01-23T06:39:02Z | 0 | 57 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-25T18:16:37Z |
---
license: creativeml-openrail-m
---
|
mlx-community/ALMA-13B-R-4bit-mlx
|
mlx-community
| 2024-01-23T06:38:31Z | 5 | 0 |
mlx
|
[
"mlx",
"llama",
"license:mit",
"region:us"
] | null | 2024-01-23T05:00:18Z |
---
license: mit
tags:
- mlx
---
# mlx-community/ALMA-13B-R-4bit-mlx
This model was converted to MLX format from [`haoranxu/ALMA-13B-R`]().
Refer to the [original model card](https://huggingface.co/haoranxu/ALMA-13B-R) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/ALMA-13B-R-4bit-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
dana11235/a2c-PandaReachDense-v3
|
dana11235
| 2024-01-23T06:23:18Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-23T06:20:04Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.18 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
shuvom/NeuralPipe-7B-slerp-tryO
|
shuvom
| 2024-01-23T06:09:02Z | 11 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:merge:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-14T14:18:48Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
base_model:
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
---
# NeuralPipe-7B-slerp-tryO
NeuralPipe-7B-slerp-tryO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "shuvom/NeuralPipe-7B-slerp-tryO"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
ayeshgk/codet5-small-ft-v7-iter-f
|
ayeshgk
| 2024-01-23T05:56:48Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5-small",
"base_model:finetune:Salesforce/codet5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-23T05:56:12Z |
---
license: apache-2.0
base_model: Salesforce/codet5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: codet5-small-ft-v7-iter-f
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. -->
# codet5-small-ft-v7-iter-f
This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3579
- Rouge1: 65.1905
- Rouge2: 58.0093
- Rougel: 64.6754
- Rougelsum: 64.9444
- Gen Len: 12.7593
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 20 | 1.2614 | 53.6789 | 41.4991 | 53.6127 | 53.6281 | 11.9259 |
| No log | 2.0 | 40 | 0.5435 | 53.3609 | 42.8943 | 50.5216 | 50.638 | 16.1852 |
| No log | 3.0 | 60 | 0.3948 | 57.8859 | 48.6662 | 57.8364 | 57.7638 | 11.4815 |
| No log | 4.0 | 80 | 0.3579 | 65.1905 | 58.0093 | 64.6754 | 64.9444 | 12.7593 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
s3nh/MedChat3.5-GGUF
|
s3nh
| 2024-01-23T05:49:55Z | 6 | 2 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-01-23T05:36:54Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/Imran1/MedChat3.5).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### inference
User: Tell me story about what is an quantization and what do we need to build.
{'role': 'assistant', 'content': "In your capacity as a medical expert, your task is to determine the links between genes and diseases in the clinical text. Genes are tagged as @GENE$, and diseases are noted as @DISEASE$. Classify the relationship between genes and diseases in the text as one of the following:\nPositive: If there's a clear connection between the mentioned gene and disease in the text.\nNegative: If there's no apparent connection between the mentioned gene and disease in the text."}
{'role': 'user', 'content': "In
# Original model card
|
MoulikBansal/phi-1_5-finetuned-mcq
|
MoulikBansal
| 2024-01-23T05:49:18Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-1_5",
"base_model:adapter:microsoft/phi-1_5",
"license:mit",
"region:us"
] | null | 2024-01-19T05:58:18Z |
---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-1_5
model-index:
- name: phi-1_5-finetuned-mcq
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. -->
# phi-1_5-finetuned-mcq
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Been1968/my_awesome_wnut_model
|
Been1968
| 2024-01-23T05:40:51Z | 46 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-23T04:25:55Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Been1968/my_awesome_wnut_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Been1968/my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1273
- Validation Loss: 0.2598
- Train Precision: 0.6081
- Train Recall: 0.4306
- Train F1: 0.5042
- Train Accuracy: 0.9460
- Epoch: 2
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.3393 | 0.3254 | 0.0 | 0.0 | 0.0 | 0.9205 | 0 |
| 0.1670 | 0.2740 | 0.5775 | 0.3565 | 0.4408 | 0.9422 | 1 |
| 0.1273 | 0.2598 | 0.6081 | 0.4306 | 0.5042 | 0.9460 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
e22vvb/EN_t5-small_5_wikiSQL
|
e22vvb
| 2024-01-23T05:33:09Z | 91 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikisql",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-22T17:46:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikisql
model-index:
- name: EN_t5-small_5_wikiSQL
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. -->
# EN_t5-small_5_wikiSQL
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikisql dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1248
- Rouge2 Precision: 0.8184
- Rouge2 Recall: 0.7263
- Rouge2 Fmeasure: 0.7626
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.1953 | 1.0 | 4049 | 0.1567 | 0.7952 | 0.7063 | 0.7411 |
| 0.1647 | 2.0 | 8098 | 0.1377 | 0.8094 | 0.7182 | 0.754 |
| 0.1519 | 3.0 | 12147 | 0.1300 | 0.8145 | 0.7232 | 0.7591 |
| 0.1458 | 4.0 | 16196 | 0.1259 | 0.8176 | 0.7261 | 0.7621 |
| 0.142 | 5.0 | 20245 | 0.1248 | 0.8184 | 0.7263 | 0.7626 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.7.dev0
- Tokenizers 0.13.3
|
schmuell/sd-turbo-ort-web
|
schmuell
| 2024-01-23T04:45:11Z | 0 | 2 | null |
[
"onnx",
"license:other",
"region:us"
] | null | 2024-01-23T03:02:52Z |
---
license: other
license_name: stabilityai-non-comercial
license_link: LICENSE
---
Conversion from https://huggingface.co/stabilityai/sd-turbo to onnx as fp16 with some ort-web friendly changes
|
sohamm/my-pet-dog
|
sohamm
| 2024-01-23T04:32:42Z | 2 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-23T04:28:19Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by sohamm following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 69
Sample pictures of this concept:
|
akshataupadhye/finetuning-sentiment-model-reddit-data
|
akshataupadhye
| 2024-01-23T04:17:48Z | 10 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-23T03:50:54Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-reddit-data
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. -->
# finetuning-sentiment-model-reddit-data
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4271
- Accuracy: 0.8845
- F1: 0.8845
## 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: 4
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Shresthadev403/ner-bert-ingredients
|
Shresthadev403
| 2024-01-23T04:17:29Z | 33 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-05T07:33:25Z |
---
tags:
- generated_from_trainer
model-index:
- name: ner-bert-ingredients
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. -->
# ner-bert-ingredients
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3555
## 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: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0466 | 10.0 | 10 | 4.7461 |
| 0.0213 | 20.0 | 20 | 4.9241 |
| 0.01 | 30.0 | 30 | 5.0407 |
| 0.0063 | 40.0 | 40 | 5.1613 |
| 0.0043 | 50.0 | 50 | 5.3555 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
qnguyen3/Mixtral-4x400M
|
qnguyen3
| 2024-01-23T04:14:27Z | 52 | 2 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-17T14:07:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
**Just model init, need pretraining**
## 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]
|
arun100/whisper-base-fr-1
|
arun100
| 2024-01-23T04:02:35Z | 21 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"fr",
"dataset:mozilla-foundation/common_voice_16_0",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-22T16:34:36Z |
---
language:
- fr
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_0 fr
type: mozilla-foundation/common_voice_16_0
config: fr
split: test
args: fr
metrics:
- name: Wer
type: wer
value: 27.650982108014144
---
<!-- 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 Base French
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 fr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5654
- Wer: 27.6510
## 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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 7000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.739 | 0.07 | 500 | 0.7506 | 35.0088 |
| 0.6131 | 1.07 | 1000 | 0.6595 | 31.4298 |
| 0.5311 | 2.07 | 1500 | 0.6301 | 30.6233 |
| 0.551 | 3.07 | 2000 | 0.6141 | 29.7819 |
| 0.4588 | 4.07 | 2500 | 0.6003 | 29.2527 |
| 0.4163 | 5.07 | 3000 | 0.5936 | 29.0292 |
| 0.4553 | 6.07 | 3500 | 0.5838 | 28.4799 |
| 0.4395 | 7.07 | 4000 | 0.5783 | 28.2488 |
| 0.4233 | 8.07 | 4500 | 0.5747 | 28.0779 |
| 0.4204 | 9.07 | 5000 | 0.5712 | 28.1122 |
| 0.4378 | 10.06 | 5500 | 0.5695 | 28.0578 |
| 0.4337 | 11.06 | 6000 | 0.5673 | 27.7817 |
| 0.4277 | 12.06 | 6500 | 0.5658 | 27.6634 |
| 0.419 | 13.06 | 7000 | 0.5654 | 27.6510 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
|
AMead10/Arten-v0
|
AMead10
| 2024-01-23T03:56:27Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-23T03:55:16Z |
---
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]
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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
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[More Information Needed]
## Training Details
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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]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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### Results
[More Information Needed]
#### 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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## Technical Specifications [optional]
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|
vaishu2396/finetuning-sentiment-model-3004-samples
|
vaishu2396
| 2024-01-23T03:52:04Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-23T03:45:06Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3004-samples
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. -->
# finetuning-sentiment-model-3004-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3136
- Accuracy: 0.8667
- F1: 0.8684
## 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: 2
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
epinnock/deepseek-coder-33B-evol-feedback-lora-v3
|
epinnock
| 2024-01-23T03:44:33Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-23T03:44:08Z |
---
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]
|
knoxcs/stable-diffusion-webui-knox
|
knoxcs
| 2024-01-23T03:37:40Z | 4 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-10-08T03:32:32Z |
---
{}
---
# Forked from Automatic1111's webui repo [here](github.com/AUTOMATIC1111/stable-diffusion-webui)
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
- LyCORIS - KohakuBlueleaf
- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
|
homerquan/pdo-pixelcopter-unit4
|
homerquan
| 2024-01-23T03:32:07Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-23T03:30:56Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pdo-pixelcopter-unit4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.20 +/- 38.08
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dvilasuero/distilabeled-Magicoder-S-DS-6.7B
|
dvilasuero
| 2024-01-23T03:25:01Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T02:17:40Z |
---
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]
[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]
|
lunaalice01/sdxl_turbo_lora_GoldenDragonBlossoms
|
lunaalice01
| 2024-01-23T03:22:30Z | 3 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/sdxl-turbo",
"base_model:adapter:stabilityai/sdxl-turbo",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2024-01-23T03:21:08Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: golden dragon,chinese
parameters:
negative_prompt: east
output:
url: images/dragon.jpg
base_model: stabilityai/sdxl-turbo
instance_prompt: dragon
license: apache-2.0
---
# Golden Dragon Blossoms
<Gallery />
## Model description
富贵吉祥
## Trigger words
You should use `dragon` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/lunaalice01/sdxl_turbo_lora_GoldenDragonBlossoms/tree/main) them in the Files & versions tab.
|
danwils/mistral7b-scotch
|
danwils
| 2024-01-23T03:07:30Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-01-23T03:07:27Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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 0.7.1
|
suhas-hegde5/controlnet_fill_circle
|
suhas-hegde5
| 2024-01-23T03:06:32Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-22T05:56:08Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-suhas-hegde5/controlnet_fill_circle
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: red circle with blue background
prompt: cyan circle with brown floral background
|
linhcuem/chamdiemgianhang_yolov8_ver2
|
linhcuem
| 2024-01-23T03:02:22Z | 4 | 0 |
ultralytics
|
[
"ultralytics",
"tensorboard",
"v8",
"ultralyticsplus",
"yolov8",
"yolo",
"vision",
"object-detection",
"pytorch",
"model-index",
"region:us"
] |
object-detection
| 2024-01-23T02:50:37Z |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
library_name: ultralytics
library_version: 8.0.43
inference: false
model-index:
- name: linhcuem/chamdiemgianhang_yolov8_ver2
results:
- task:
type: object-detection
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.83369 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="linhcuem/chamdiemgianhang_yolov8_ver2" src="https://huggingface.co/linhcuem/chamdiemgianhang_yolov8_ver2/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['bom_gen', 'bom_jn', 'bom_knp', 'bom_sachet', 'bom_vtgk', 'bom_ytv', 'hop_jn', 'hop_vtg', 'hop_ytv', 'lo_kid', 'lo_ytv', 'loc_dln', 'loc_jn', 'loc_kid', 'loc_ytv', 'pocky', 'tui_gen', 'tui_jn', 'tui_sachet', 'tui_vtgk']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('linhcuem/chamdiemgianhang_yolov8_ver2')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
|
casque/Pupung
|
casque
| 2024-01-23T02:57:33Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-23T02:56:27Z |
---
license: creativeml-openrail-m
---
|
maximuslee07/llama-2-13b-rockwellautomation
|
maximuslee07
| 2024-01-23T02:55:10Z | 1,376 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:maximuslee07/raqna10k",
"arxiv:1910.09700",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-03T04:29:52Z |
---
license: llama2
datasets:
- maximuslee07/raqna10k
language:
- en
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model card aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
The model has been trained on Rockwell Automation's Technical Support engage dataset and it uses SOTA LoRA & QLoRA running on two rtx4090 server
### 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]
|
alexsherstinsky/Mistral-7B-v0.1-sharded
|
alexsherstinsky
| 2024-01-23T02:46:43Z | 108 | 14 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"pretrained",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-04T15:21:42Z |
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- pretrained
inference:
parameters:
temperature: 0.7
---
# Note: Sharded Version of the Original "Mistral 7B" Model
This is just a version of https://huggingface.co/mistralai/Mistral-7B-v0.1 which is sharded to 2GB maximum parts in order to reduce the RAM required when loading.
# Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our [Release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue:
```
pip install git+https://github.com/huggingface/transformers
```
This should not be required after transformers-v4.33.4.
## Notice
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
homerquan/rl_course_vizdoom_health_gathering_supreme
|
homerquan
| 2024-01-23T02:31:56Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-23T02:31:48Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.54 +/- 4.78
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r homerquan/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
TroyDoesAI/MermaidStable3B
|
TroyDoesAI
| 2024-01-23T02:31:18Z | 101 | 5 |
transformers
|
[
"transformers",
"safetensors",
"stablelm_epoch",
"text-generation",
"FlowChat",
"Mermaid",
"Diagram",
"LLM",
"Skill:FlowChartGeneration",
"Visualize",
"Visualizer",
"Flow",
"diagram",
"flowchart",
"custom_code",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-23T01:34:16Z |
---
license: cc-by-nc-4.0
tags:
- FlowChat
- Mermaid
- Diagram
- LLM
- Skill:FlowChartGeneration
- Visualize
- Visualizer
- Flow
- diagram
- flowchart
---
# MermaidStable3B: A Compact Powerhouse for Flow Chart Innovation
Introducing MermaidStable3B, a compact yet remarkably capable language model that's transforming the landscape of flow chart creation. This model is a testament to the fact that extraordinary capability can come in smaller packages. It's not just about the size; it's about the efficiency and precision in execution, making MermaidStable3B a game-changer in the realm of diagrammatic representations.
Model Essentials:
MermaidStable3B is rooted in the stable-code-3b architecture, encompassing a robust framework with 2.7 billion parameters. Developed from the expertise of Stability AI, this model is fine-tuned to excel in various programming contexts and textual interpretations, serving as a versatile foundation for MermaidStable3B’s unique capabilities.
Core Features:
Enhanced Code Comprehension:
- Specializes in translating code complexities into intuitive Mermaid Diagram Flow Charts.
- An invaluable tool for developers in visualizing and understanding intricate code structures.
Narrative to Diagram Conversion:
- Effortlessly transforms narrative inputs into engaging and structured Mermaid Diagrams.
- Ideal for visualizing character dynamics, plot trajectories, and thematic elements in stories.
Performance Excellence:
- Demonstrates superior efficiency and output quality, challenging the norms of larger models.
- Delivers detailed, organized Mermaid Diagrams for varied storylines and code sequences.
Training and Adaptability:
- Diverse training background, enabling unique story-to-flow chart translations.
- Showcases exceptional performance in resource-limited settings, embodying innovation under constraints.
Collaborative Potential:
MermaidStable3B is open for collaboration and enhancement. With a foundation in the Alpaca-formatted dataset, it offers a nuanced understanding of Python. Interested collaborators are encouraged to reach out to [email protected]
Diverse Applications:
For Code Documentation:
- Aids developers in auto-generating visual flow charts from code, simplifying documentation and comprehension.
For Storyboarding:
- Writers and storytellers can input narratives to receive structured Mermaid Diagrams, offering a clear view of plot and character interplay.
In Project Management:
- Project planners can employ MermaidStable3B for creating detailed project flow maps, enhancing team communication and project visualization.
Educational Tool for Python Learning:
- Aids students and beginners in visually grasping Python code structures, enriching the educational journey.
In Game Design:
- Game designers can utilize the model to map out game narratives and mechanics, bringing clarity to complex design processes.
---
MermaidStable3B represents the spirit of creating innovative, high-quality visual tools, making complex concepts accessible and engaging.
MermaidStable3B is not just a tool; it's a bridge between technical complexity and visual simplicity, aligning perfectly with the needs of those seeking to blend detailed analysis with clear, visual storytelling.
**Proof of Concept:**
MermaidStable3B reaffirms that innovation thrives in compact packages, delivering exceptional performance across diverse applications.
Stay tuned for the release of the VSCode Extension that displays the Live Flow Map every time a user stops typing for more than 10 seconds.
For best results Use Alpaca Instruct Format, use full precision with one of the three different instruction types:
- "instruction": "Create the mermaid diagram for the following code:"
- "instruction": "Create the mermaid diagram for the following story:"
- "instruction": "Create the mermaid diagram for the following:"
Also
It helps to have ### Response:
start with ```mermaid followed by a new line.
This pushes the model to respond with a mermaid diagram on the next line.
|
BlueNipples/DaringLotus-v2-10.7b
|
BlueNipples
| 2024-01-23T02:27:13Z | 21 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-17T05:18:31Z |
---
tags:
- merge
---
# DaringLotus-10.7B-v2
This is a dare ties merge of https://huggingface.co/BlueNipples/SnowLotus-v2-10.7B and it's parent models. It shares it's good prose, and relatively decent coherency, being a little bit more on the side of prose, and a little bit less on the side of coherency. I like this model for generating great prose if I feel like regening a bit. It's a good model as is the other model for RP, and I think both these merged models probably stand up with the best in their weight class (11-13). Which you prefer might be a matter of context and preference which is why I've uploaded both. Credit to Nyx and Sao10k for their models contributions (Frostmaid, FrostWind and SolarDoc), as well as Undi95 and Ikari for Noromaid, the developers of Mergekit, and whomever contributed the medical model used in the frankenmerge portion.
GGUF (Small selection of Imatrix and regular k-quants): https://huggingface.co/BlueNipples/DaringLotus-SnowLotus-10.7b-IQ-GGUF
EXL2s:
https://huggingface.co/zaq-hack/DaringLotus-v2-10.7b-bpw500-h6-exl2
https://huggingface.co/lucyknada/DaringLotus-v2-10.7B-3bpw-exl2
### Format Notes
Solar is desgined for 4k context, but Nyx reports that his merge works to 8k. Given this has a slerp gradient back into that, I'm not sure which applies here. Alpaca instruct formatting.
## Recipe
- model: ./Frostmaid
parameters:
density: [0.45] # density gradient
weight: 0.23
- model: ./FrostMed
parameters:
density: [0.35] # density gradient
weight: 0.18
- model: ./SnowLotus-10.7B-v2
parameters:
density: [1] # density gradient
weight: 1
### Ayumi Index
http://ayumi.m8geil.de/erp4_chatlogs/?S=rma_0#!/index
In the Ayumi ERPv4 Chat Log Index, SnowLotus scores a 94.10 in Flesch which means it produces more complex sentences than Daring (quite complex), DaringLotus scores higher in Var and Ad[jv], which means it makes heavier use of adjectives and adverbs (is more descriptive). Noteably Daring is in the top 8 for adjectives in a sentence, highest in it's weight class if you discount the chinese model, and in general both models did very well on this metric (SnowLotus ranks higher here than anything above it in IQ4), showcasing their descriptive ability.
SnowLotus beats DaringLotus on IQ4 with a score of 70.94, only bet by SOLAR Instruct and Fimbulvetr in it's weight class (altho also noteably Kunoichi 7b by a slim margin), DaringLotus is a bit lower at 65.37 - not as smart.
Interestingly the benchmarking here showed repetition for both models (which I haven't seen), but more with SnowLotus - so it's possible Daring repeats less than SnowLotus? These roughly confirm my impressions of the differences, altho potentially reveal some new details too. I've had a great experience RPing with these models, and seen no repetition myself, but be sure to use MinP or DynaTemp rather than the older samplers and be prepared to regen anything they get stuck on!
|
learn3r/longt5_xl_sfd_memsum_30
|
learn3r
| 2024-01-23T02:20:23Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"longt5",
"text2text-generation",
"generated_from_trainer",
"dataset:learn3r/summ_screen_memsum_oracle",
"base_model:google/long-t5-tglobal-xl",
"base_model:finetune:google/long-t5-tglobal-xl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-22T21:02:36Z |
---
license: apache-2.0
base_model: google/long-t5-tglobal-xl
tags:
- generated_from_trainer
datasets:
- learn3r/summ_screen_memsum_oracle
model-index:
- name: longt5_xl_sfd_memsum_30
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. -->
# longt5_xl_sfd_memsum_30
This model is a fine-tuned version of [google/long-t5-tglobal-xl](https://huggingface.co/google/long-t5-tglobal-xl) on the learn3r/summ_screen_memsum_oracle dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1322
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 30.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6697 | 0.97 | 14 | 2.4168 |
| 2.2272 | 1.95 | 28 | 2.2644 |
| 1.9024 | 2.99 | 43 | 2.2556 |
| 1.6554 | 3.97 | 57 | 2.4007 |
| 1.3619 | 4.94 | 71 | 2.4233 |
| 1.1577 | 5.98 | 86 | 2.6797 |
| 0.9584 | 6.96 | 100 | 2.8449 |
| 0.7197 | 8.0 | 115 | 3.0255 |
| 0.5756 | 8.97 | 129 | 3.1467 |
| 0.485 | 9.95 | 143 | 3.2976 |
| 0.4027 | 10.99 | 158 | 3.8111 |
| 0.2938 | 11.97 | 172 | 3.7330 |
| 0.2665 | 12.94 | 186 | 4.1417 |
| 0.2019 | 13.98 | 201 | 4.0316 |
| 0.1706 | 14.96 | 215 | 4.1357 |
| 0.1418 | 16.0 | 230 | 4.1022 |
| 0.1286 | 16.97 | 244 | 4.1198 |
| 0.1022 | 17.95 | 258 | 4.1862 |
| 0.1122 | 18.99 | 273 | 4.6386 |
| 0.093 | 19.97 | 287 | 4.6829 |
| 0.0783 | 20.94 | 301 | 4.6637 |
| 0.0698 | 21.98 | 316 | 4.7190 |
| 0.0688 | 22.96 | 330 | 5.0200 |
| 0.0633 | 24.0 | 345 | 4.7576 |
| 0.0609 | 24.97 | 359 | 4.7805 |
| 0.0553 | 25.95 | 373 | 4.7338 |
| 0.0503 | 26.99 | 388 | 5.1409 |
| 0.0471 | 27.97 | 402 | 5.1463 |
| 0.0472 | 28.94 | 416 | 5.1636 |
| 0.0376 | 29.22 | 420 | 5.1322 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
BarryFutureman/WildWest-Variant3-7B
|
BarryFutureman
| 2024-01-23T02:18:58Z | 1,457 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-23T01:27:36Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- merge
---
# WildWest-Variant3-7B
Based on a merge of the following models using mergekit
* [BarryFutureman/NeuralTurdusVariant1-7B](https://huggingface.co/BarryFutureman/NeuralTurdusVariant1-7B)
* [mlabonne/NeuralDaredevil-7B](https://huggingface.co/udkai/Turdus)
* [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
* [PetroGPT/Severus-7B-DPO](https://huggingface.co/PetroGPT/Severus-7B-DPO)
|
coversia21/RVCVenom
|
coversia21
| 2024-01-23T02:15:39Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"base_model:adapter:cagliostrolab/animagine-xl-3.0",
"license:openrail",
"region:us"
] |
text-to-image
| 2024-01-23T02:06:53Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/venom-2-fotogramas-1634239260.png
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: null
license: openrail
---
# RVCVenom
<Gallery />
## Download model
[Download](/coversia21/RVCVenom/tree/main) them in the Files & versions tab.
|
BitStarWalkin/RPG_models
|
BitStarWalkin
| 2024-01-23T02:09:35Z | 0 | 21 | null |
[
"arxiv:2401.11708",
"region:us"
] | null | 2024-01-22T16:08:25Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Official Model Card for (RPG) [Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs](https://arxiv.org/abs/2401.11708)
The GitHub repo:https://github.com/YangLing0818/RPG-DiffusionMaster
RPG is a training-free paradigm that utilize MLLMs as prompt recaptioner and layout planner from diffusion models. This paradigm can be generalized to different diffusion models, here we provide some high-quality community models in [CIVITAI](https://civitai.com/models) based on stable-diffusion v1.4/1.5 and SDXL-1.0/SDXL-Turbo for high-quality generation.
We will continue to update our model_pool combining with the ControlNet, stay tuned for our following progress.
## Stable-diffusion v1.4/1.5 based model Details
For Stable-diffusion v1.4/1.5 based models, we use
[AbsoluteReality](https://civitai.com/models/81458/absolutereality) for realistic style generation.
[AnythingV3](https://huggingface.co/Linaqruf/anything-v3.0) for anime style generation.
[Disney Pixar Cartoon](https://civitai.com/models/75650/disney-pixar-cartoon-type-b) for cartoon style generation.
## SDXL v1.0/ SDXL-Turbo based models Details
For SDXL v1.0/ SDXL-Turbo based models, we use
[AlbedoBaseXL](https://civitai.com/models/140737?modelVersionId=281176) for SDXL-baed photorealistic style generation.
[DreamShaperXL](https://civitai.com/models/112902/dreamshaper-xl) for SDXL-Turbo based photorealistic style generation.
|
epinnock/deepseek-coder-33B-evol-feedback-lora-v2
|
epinnock
| 2024-01-23T02:04:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-23T02:03:52Z |
---
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. -->
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
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