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Charm333/yi-6b-chat-32k
|
Charm333
| 2023-12-15T09:44:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T09:44:28Z |
---
license: other
license_name: yi-license
license_link: LICENSE
widget:
- example_title: "Yi-34B-Chat"
text: "hi"
output:
text: " Hello! How can I assist you today?"
- example_title: "Yi-34B"
text: "There's a place where time stands still. A place of breath taking wonder, but also"
output:
text: " an eerie sense that something is just not right…\nBetween the two worlds lies The Forgotten Kingdom - home to creatures long since thought extinct and ancient magic so strong it defies belief! Only here can you find what has been lost for centuries: An Elixir Of Life which will restore youth and vitality if only those who seek its power are brave enough to face up against all manner of dangers lurking in this mysterious land! But beware; some say there may even exist powerful entities beyond our comprehension whose intentions towards humanity remain unclear at best ---- they might want nothing more than destruction itself rather then anything else from their quest after immortality (and maybe someone should tell them about modern medicine)? In any event though – one thing remains true regardless : whether or not success comes easy depends entirely upon how much effort we put into conquering whatever challenges lie ahead along with having faith deep down inside ourselves too ;) So let’s get started now shall We?"
pipeline_tag: text-generation
---
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## Introduction
The **Yi** series models are large language models trained from scratch by
developers at [01.AI](https://01.ai/).
## News
<details open>
<summary>🎯 <b>2023/11/23</b>: The chat models are open to public.</summary>
This release contains two chat models based on previous released base models, two 8-bits models quantized by GPTQ, two 4-bits models quantized by AWQ.
- `Yi-34B-Chat`
- `Yi-34B-Chat-4bits`
- `Yi-34B-Chat-8bits`
- `Yi-6B-Chat`
- `Yi-6B-Chat-4bits`
- `Yi-6B-Chat-8bits`
You can try some of them interactively at:
- [Hugging Face](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
- [Replicate](https://replicate.com/01-ai)
</details>
<details open>
<summary>🔔 <b>2023/11/23</b>: The Yi Series Models Community License Agreement is updated to v2.1.</summary>
</details>
<details>
<summary>🔥 <b>2023/11/08</b>: Invited test of Yi-34B chat model.</summary>
Application form:
- [English](https://cn.mikecrm.com/l91ODJf)
- [Chinese](https://cn.mikecrm.com/gnEZjiQ)
</details>
<details>
<summary>🎯 <b>2023/11/05</b>: The base model of <code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>.</summary>
This release contains two base models with the same parameter sizes of previous
release, except that the context window is extended to 200K.
</details>
<details>
<summary>🎯 <b>2023/11/02</b>: The base model of <code>Yi-6B</code> and <code>Yi-34B</code>.</summary>
The first public release contains two bilingual (English/Chinese) base models
with the parameter sizes of 6B and 34B. Both of them are trained with 4K
sequence length and can be extended to 32K during inference time.
</details>
## Ecosystem
🤗 You are encouraged to create a PR and share your awesome work built on top of
the Yi series models.
- Serving
- [ScaleLLM](https://github.com/vectorch-ai/ScaleLLM#supported-models): Efficiently run Yi models locally.
- Quantization
- [TheBloke/Yi-34B-GGUF](https://huggingface.co/TheBloke/Yi-34B-GGUF)
- [TheBloke/Yi-34B-GPTQ](https://huggingface.co/TheBloke/Yi-34B-GPTQ)
- Finetuning
- [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B)
- [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B): This
model ranks first among all models below 70B and has outperformed the twice
larger
[deepseek-llm-67b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat).
You can check the result in [🤗 Open LLM
Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
## Model Performance
### Base Model Performance
| Model | MMLU | CMMLU | C-Eval | GAOKAO | BBH | Common-sense Reasoning | Reading Comprehension | Math & Code |
| :------------ | :------: | :------: | :------: | :------: | :------: | :--------------------: | :-------------------: | :---------: |
| | 5-shot | 5-shot | 5-shot | 0-shot | 3-shot@1 | - | - | - |
| LLaMA2-34B | 62.6 | - | - | - | 44.1 | 69.9 | 68.0 | 26.0 |
| LLaMA2-70B | 68.9 | 53.3 | - | 49.8 | 51.2 | 71.9 | 69.4 | 36.8 |
| Baichuan2-13B | 59.2 | 62.0 | 58.1 | 54.3 | 48.8 | 64.3 | 62.4 | 23.0 |
| Qwen-14B | 66.3 | 71.0 | 72.1 | 62.5 | 53.4 | 73.3 | 72.5 | **39.8** |
| Skywork-13B | 62.1 | 61.8 | 60.6 | 68.1 | 41.7 | 72.4 | 61.4 | 24.9 |
| InternLM-20B | 62.1 | 59.0 | 58.8 | 45.5 | 52.5 | 78.3 | - | 30.4 |
| Aquila-34B | 67.8 | 71.4 | 63.1 | - | - | - | - | - |
| Falcon-180B | 70.4 | 58.0 | 57.8 | 59.0 | 54.0 | 77.3 | 68.8 | 34.0 |
| Yi-6B | 63.2 | 75.5 | 72.0 | 72.2 | 42.8 | 72.3 | 68.7 | 19.8 |
| Yi-6B-200K | 64.0 | 75.3 | 73.5 | 73.9 | 42.0 | 72.0 | 69.1 | 19.0 |
| **Yi-34B** | **76.3** | **83.7** | 81.4 | 82.8 | **54.3** | **80.1** | 76.4 | 37.1 |
| Yi-34B-200K | 76.1 | 83.6 | **81.9** | **83.4** | 52.7 | 79.7 | **76.6** | 36.3 |
While benchmarking open-source models, we have observed a disparity between the
results generated by our pipeline and those reported in public sources (e.g.
OpenCompass). Upon conducting a more in-depth investigation of this difference,
we have discovered that various models may employ different prompts,
post-processing strategies, and sampling techniques, potentially resulting in
significant variations in the outcomes. Our prompt and post-processing strategy
remains consistent with the original benchmark, and greedy decoding is employed
during evaluation without any post-processing for the generated content. For
scores that were not reported by the original authors (including scores reported
with different settings), we try to get results with our pipeline.
To evaluate the model's capability extensively, we adopted the methodology
outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande,
ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ
were incorporated to evaluate reading comprehension. CSQA was exclusively tested
using a 7-shot setup, while all other tests were conducted with a 0-shot
configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1),
HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due
to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score
is derived by averaging the scores on the remaining tasks. Since the scores for
these two tasks are generally lower than the average, we believe that
Falcon-180B's performance was not underestimated.
### Chat Model Performance
| Model | MMLU | MMLU | CMMLU | CMMLU | C-Eval(val)<sup>*</sup> | C-Eval(val)<sup>*</sup> | Truthful QA | BBH | BBH | GSM8k | GSM8k |
| ----------------------- | --------- | --------- | --------- | --------- | ----------------------- | ----------------------- | ----------- | --------- | --------- | --------- | --------- |
| | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 0-shot | 3-shot | 0-shot | 4-shot |
| LLaMA2-13B-Chat | 50.88 | 47.33 | 27.47 | 35.08 | 27.93 | 35.88 | 36.84 | 32.90 | 58.22 | 36.85 | 2.73 |
| LLaMA2-70B-Chat | 59.42 | 59.86 | 36.10 | 40.99 | 34.99 | 41.31 | 53.95 | 42.36 | 58.53 | 47.08 | 58.68 |
| Baichuan2-13B-Chat | 55.09 | 50.14 | 58.64 | 59.47 | 56.02 | 54.75 | 48.98 | 38.81 | 47.15 | 45.72 | 23.28 |
| Qwen-14B-Chat | 63.99 | 64.98 | 67.73 | 70.57 | 66.12 | 70.06 | 52.49 | 49.65 | 54.98 | 59.51 | 61.18 |
| InternLM-Chat-20B | 55.55 | 57.42 | 53.55 | 53.75 | 51.19 | 53.57 | 51.75 | 42.41 | 36.68 | 15.69 | 43.44 |
| AquilaChat2-34B v1.2 | 65.15 | 66.70 | 67.51 | 70.02 | **82.99** | **89.38** | **64.33** | 20.12 | 34.28 | 11.52 | 48.45 |
| Yi-6B-Chat | 58.24 | 60.99 | 69.44 | 74.71 | 68.80 | 74.22 | 50.58 | 39.70 | 47.15 | 38.44 | 44.88 |
| Yi-6B-Chat-8bits(GPTQ) | 58.29 | 60.96 | 69.21 | 74.69 | 69.17 | 73.85 | 49.85 | 40.35 | 47.26 | 39.42 | 44.88 |
| Yi-6B-Chat-4bits(AWQ) | 56.78 | 59.89 | 67.70 | 73.29 | 67.53 | 72.29 | 50.29 | 37.74 | 43.62 | 35.71 | 38.36 |
| Yi-34B-Chat | **67.62** | 73.46 | **79.11** | **81.34** | 77.04 | 78.53 | 62.43 | 51.41 | **71.74** | **71.65** | **75.97** |
| Yi-34B-Chat-8bits(GPTQ) | 66.24 | **73.69** | 79.05 | 81.23 | 76.82 | 78.97 | 61.84 | **52.08** | 70.97 | 70.74 | 75.74 |
| Yi-34B-Chat-4bits(AWQ) | 65.77 | 72.42 | 78.21 | 80.50 | 75.71 | 77.27 | 61.84 | 48.30 | 69.39 | 70.51 | 74.00 |
We evaluated various benchmarks using both zero-shot and few-shot methods, except for TruthfulQA. Generally, the zero-shot approach is more common in chat models. Our evaluation strategy involves generating responses while following instructions explicitly or implicitly (such as using few-shot examples). We then isolate relevant answers from the generated text. Some models are not well-suited to produce output in the specific format required by instructions in few datasets, which leads to suboptimal results.
<strong>*</strong>: C-Eval results are evaluated on the validation datasets
### Quantized Chat Model Performance
We also provide both 4-bit (AWQ) and 8-bit (GPTQ) quantized Yi chat models. Evaluation results on various benchmarks have shown that the quantized models have negligible losses. Additionally, they reduce the memory footprint size. After testing different configurations of prompts and generation lengths, we highly recommend following the guidelines in the memory footprint table below when selecting a device to run our models.
| | batch=1 | batch=4 | batch=16 | batch=32 |
| ----------------------- | ------- | ------- | -------- | -------- |
| Yi-34B-Chat | 65GiB | 68GiB | 76GiB | >80GiB |
| Yi-34B-Chat-8bits(GPTQ) | 35GiB | 37GiB | 46GiB | 58GiB |
| Yi-34B-Chat-4bits(AWQ) | 19GiB | 20GiB | 30GiB | 40GiB |
| Yi-6B-Chat | 12GiB | 13GiB | 15GiB | 18GiB |
| Yi-6B-Chat-8bits(GPTQ) | 7GiB | 8GiB | 10GiB | 14GiB |
| Yi-6B-Chat-4bits(AWQ) | 4GiB | 5GiB | 7GiB | 10GiB |
Note: All the numbers in the table represent the minimum recommended memory for running models of the corresponding size.
### Limitations of Chat Model
The released chat model has undergone exclusive training using Supervised Fine-Tuning (SFT). Compared to other standard chat models, our model produces more diverse responses, making it suitable for various downstream tasks, such as creative scenarios. Furthermore, this diversity is expected to enhance the likelihood of generating higher quality responses, which will be advantageous for subsequent Reinforcement Learning (RL) training.
However, this higher diversity might amplify certain existing issues, including:
- **Hallucination**: This refers to the model generating factually incorrect or nonsensical information. With the model's responses being more varied, there's a higher chance of hallucination that are not based on accurate data or logical reasoning.
- **Non-determinism in re-generation**: When attempting to regenerate or sample responses, inconsistencies in the outcomes may occur. The increased diversity can lead to varying results even under similar input conditions.
- **Cumulative Error**: This occurs when errors in the model's responses compound over time. As the model generates more diverse responses, the likelihood of small inaccuracies building up into larger errors increases, especially in complex tasks like extended reasoning, mathematical problem-solving, etc.
To achieve more coherent and consistent responses, it is advisable to adjust generation configuration parameters such as`temperature`,`top_p`, or`top_k`. These adjustments can help in the balance between creativity and coherence in the model's outputs.
## Usage
Feel free to [create an issue](https://github.com/01-ai/Yi/issues/new) if you
encounter any problem when using the **Yi** series models.
### 1. Prepare development environment
#### 1.1 Docker
The best approach to try the **Yi** series models is through Docker with GPUs. We
provide the following docker images to help you get started.
- `registry.lingyiwanwu.com/ci/01-ai/yi:latest`
- `ghcr.io/01-ai/yi:latest`
Note that the `latest` tag always points to the latest code in the `main`
branch. To test a stable version, please replace it with a specific
[tag](https://github.com/01-ai/Yi/tags).
#### 1.2 Local development environment
We use [`conda-lock`](https://github.com/conda/conda-lock) to generate fully reproducible lock files for conda environments. You can refer to [conda-lock.yml](./conda-lock.yml) for the exact versions of the dependencies. Additionally, we utilize [`micromamba`](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html) for installing these dependencies.
To install the dependencies, please follow these steps:
1. Install `micromamba` by following the instructions available [here](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html).
2. Execute `micromamba install -y -n yi -f conda-lock.yml` to create a conda environment named `yi` and install the necessary dependencies.
### 2. Download the model (optional)
By default, the model weights and tokenizer will be downloaded from
[Hugging Face](https://huggingface.co/01-ai) automatically in the next step. You
can also download them manually from the following places:
- [ModelScope](https://www.modelscope.cn/organization/01ai/)
- [WiseModel](https://wisemodel.cn/organization/01.AI)
### 3. Examples
#### 3.1 Use the chat model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = '01-ai/Yi-34b-Chat'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
# Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
To construct the prompt template manually, you can refer the `chat_template` field in the `tokenizer_config.json` [file](https://huggingface.co/01-ai/Yi-34B-Chat/blob/main/tokenizer_config.json#L60).
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
#### 3.2 Use the base model
```bash
python demo/text_generation.py
```
To reuse the downloaded models in the previous step, you can provide the extra
`--model` argument:
```bash
python demo/text_generation.py --model /path/to/model
```
Or if you'd like to get your hands dirty:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34B", device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34B")
inputs = tokenizer("There's a place where time stands still. A place of breath taking wonder, but also", return_tensors="pt")
max_length = 256
outputs = model.generate(
inputs.input_ids.cuda(),
max_length=max_length,
eos_token_id=tokenizer.eos_token_id,
do_sample=True,
repetition_penalty=1.3,
no_repeat_ngram_size=5,
temperature=0.7,
top_k=40,
top_p=0.8,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
<details>
<summary>Output</summary>
**Prompt**: There's a place where time stands still. A place of breath taking wonder, but also
**Generation**: There's a place where time stands still. A place of breath taking wonder, but also of great danger. A place where the very air you breathe could kill you. A place where the only way to survive is to be prepared.
The place is called the Arctic.
The Arctic is a vast, frozen wilderness. It is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end.
The Arctic is also a place of great beauty. The ice and snow are a pristine white. The sky is a deep blue. The sunsets are spectacular.
But the Arctic is also a place of great danger. The ice can be treacherous. The winds can be deadly. The sun can be blinding.
The Arctic is a place where the only way to survive is to be prepared.
The Arctic is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end.
The Arctic is a place of great beauty. The ice and snow are a
</details>
For more advanced usage, please refer to the
[doc](https://github.com/01-ai/Yi/tree/main/demo).
#### 3.3 Finetuning from the base model:
```bash
bash finetune/scripts/run_sft_Yi_6b.sh
```
Once finished, you can compare the finetuned model and the base model with the following command:
```bash
bash finetune/scripts/run_eval.sh
```
For more advanced usage like fine-tuning based on your custom data, please refer
the [doc](https://github.com/01-ai/Yi/tree/main/finetune).
#### 3.4 Quantization
##### GPT-Q
```bash
python quantization/gptq/quant_autogptq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
```
Once finished, you can then evaluate the resulting model as follows:
```bash
python quantization/gptq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
```
For a more detailed explanation, please read the [doc](https://github.com/01-ai/Yi/tree/main/quantization/gptq)
##### AWQ
```bash
python quantization/awq/quant_autoawq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
```
Once finished, you can then evaluate the resulting model as follows:
```bash
python quantization/awq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
```
For more detailed explanation, please read the [doc](https://github.com/01-ai/Yi/tree/main/quantization/awq)
## FAQ
1. **What dataset was this trained with?**
The dataset we use contains Chinese & English only. We used approximately 3T
tokens. The detailed number and its construction will be described in the
upcoming technical report.
## Disclaimer
We use data compliance checking algorithms during the training process, to
ensure the compliance of the trained model to the best of our ability. Due to
complex data and the diversity of language model usage scenarios, we cannot
guarantee that the model will generate correct, and reasonable output in all
scenarios. Please be aware that there is still a risk of the model producing
problematic outputs. We will not be responsible for any risks and issues
resulting from misuse, misguidance, illegal usage, and related misinformation,
as well as any associated data security concerns.
## License
The source code in this repo is licensed under the [Apache 2.0
license](https://github.com/01-ai/Yi/blob/main/LICENSE). The Yi series models
are fully open for academic research and free commercial usage with permission
via applications. All usage must adhere to the [Model License
Agreement 2.0](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
To apply for the official commercial license, please contact us
([[email protected]](mailto:[email protected])).
|
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] |
WilsonLeeHF/TinySAM
|
WilsonLeeHF
| 2023-12-15T09:46:36Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T09:46:36Z |
---
license: apache-2.0
---
|
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] |
Yizhak174/llama-2-13b-wind-merged
|
Yizhak174
| 2023-12-15T10:08:45Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T09:48:38Z |
Entry not found
|
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] |
ayoungfish/custom-resnet50d
|
ayoungfish
| 2023-12-15T09:50:28Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"resnet",
"image-classification",
"custom_code",
"autotrain_compatible",
"region:us"
] |
image-classification
| 2023-12-15T09:49:56Z |
Entry not found
|
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] |
FadySamy/Pixelcopter-PLE-v0
|
FadySamy
| 2023-12-15T09:51:07Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T09:51:04Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 15.30 +/- 14.16
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
|
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] |
Mediform/GMarcO
|
Mediform
| 2023-12-15T11:34:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T09:53:49Z |
A merged model of [Mediform/mistral_gml_7b](https://huggingface.co/Mediform/mistral_gml_7b), [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3), and [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) using [SLERP merge](https://github.com/cg123/mergekit).
The merging consists of two stages:
1. Merge `mistral_gml_7b` with `Marcoroni-7B-v3`, resulting `GMarc` model. The config is as follows:
```yaml
slices:
- sources:
- model: Mediform/mistral_gml_7b
layer_range: [0, 32]
- model: AIDC-ai-business/Marcoroni-7B-v3
layer_range: [0, 32]
merge_method: slerp
base_model: Mediform/mistral_gml_7b
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 # fallback for rest of tensors
dtype: bfloat16
```
2. Merge the resulting model with `OpenHermes-2.5-Mistral-7B`:
```yaml
slices:
- sources:
- model: /workspace/data/anhnv79/GMarc
layer_range: [0, 32]
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: /workspace/data/anhnv79/GMarc
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 # fallback for rest of tensors
dtype: bfloat16
```
|
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SwethaBaskaran/Test
|
SwethaBaskaran
| 2023-12-15T09:55:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T09:55:18Z |
Entry not found
|
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] |
Mediform/GMarcO-ft
|
Mediform
| 2023-12-15T11:52:31Z | 0 | 0 | null |
[
"gguf",
"generated_from_trainer",
"region:us"
] | null | 2023-12-15T09:55:20Z |
---
tags:
- generated_from_trainer
model-index:
- name: GMarcO-ft
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)
# GMarcO-ft
This model was finetuned from [Mediform/GMarcO](https://huggingface.co/Mediform/GMarcO) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0123
## 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: 4
- seed: 42
- gradient_accumulation_steps: 2
- 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: 50
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1974 | 0.05 | 4 | 1.1860 |
| 1.1084 | 0.09 | 8 | 0.8903 |
| 0.7682 | 0.14 | 12 | 0.5366 |
| 0.3806 | 0.19 | 16 | 0.3049 |
| 0.2491 | 0.24 | 20 | 0.1645 |
| 0.0959 | 0.28 | 24 | 0.1158 |
| 0.0709 | 0.33 | 28 | 0.0862 |
| 0.1118 | 0.38 | 32 | 0.0696 |
| 0.0494 | 0.42 | 36 | 0.0574 |
| 0.0843 | 0.47 | 40 | 0.0482 |
| 0.0661 | 0.52 | 44 | 0.0410 |
| 0.1191 | 0.56 | 48 | 0.0391 |
| 0.0312 | 0.61 | 52 | 0.0371 |
| 0.0422 | 0.66 | 56 | 0.0331 |
| 0.0365 | 0.71 | 60 | 0.0340 |
| 0.0403 | 0.75 | 64 | 0.0300 |
| 0.0303 | 0.8 | 68 | 0.0288 |
| 0.0313 | 0.85 | 72 | 0.0280 |
| 0.0592 | 0.89 | 76 | 0.0277 |
| 0.0218 | 0.94 | 80 | 0.0259 |
| 0.0267 | 0.99 | 84 | 0.0253 |
| 0.0395 | 1.04 | 88 | 0.0242 |
| 0.021 | 1.08 | 92 | 0.0247 |
| 0.0173 | 1.13 | 96 | 0.0245 |
| 0.0204 | 1.18 | 100 | 0.0236 |
| 0.0332 | 1.22 | 104 | 0.0230 |
| 0.0184 | 1.27 | 108 | 0.0221 |
| 0.014 | 1.32 | 112 | 0.0218 |
| 0.0259 | 1.36 | 116 | 0.0212 |
| 0.0107 | 1.41 | 120 | 0.0212 |
| 0.0212 | 1.46 | 124 | 0.0220 |
| 0.0087 | 1.51 | 128 | 0.0211 |
| 0.0118 | 1.55 | 132 | 0.0205 |
| 0.0175 | 1.6 | 136 | 0.0209 |
| 0.0246 | 1.65 | 140 | 0.0211 |
| 0.016 | 1.69 | 144 | 0.0218 |
| 0.0145 | 1.74 | 148 | 0.0187 |
| 0.022 | 1.79 | 152 | 0.0183 |
| 0.0143 | 1.84 | 156 | 0.0173 |
| 0.0062 | 1.88 | 160 | 0.0162 |
| 0.0258 | 1.93 | 164 | 0.0155 |
| 0.0234 | 1.98 | 168 | 0.0153 |
| 0.0084 | 2.02 | 172 | 0.0155 |
| 0.0084 | 2.07 | 176 | 0.0161 |
| 0.0163 | 2.12 | 180 | 0.0155 |
| 0.0078 | 2.16 | 184 | 0.0152 |
| 0.0042 | 2.21 | 188 | 0.0153 |
| 0.0067 | 2.26 | 192 | 0.0157 |
| 0.014 | 2.31 | 196 | 0.0168 |
| 0.0045 | 2.35 | 200 | 0.0153 |
| 0.0221 | 2.4 | 204 | 0.0150 |
| 0.0172 | 2.45 | 208 | 0.0147 |
| 0.0147 | 2.49 | 212 | 0.0147 |
| 0.0175 | 2.54 | 216 | 0.0149 |
| 0.0165 | 2.59 | 220 | 0.0152 |
| 0.0079 | 2.64 | 224 | 0.0156 |
| 0.0127 | 2.68 | 228 | 0.0157 |
| 0.003 | 2.73 | 232 | 0.0154 |
| 0.0085 | 2.78 | 236 | 0.0154 |
| 0.0129 | 2.82 | 240 | 0.0156 |
| 0.0096 | 2.87 | 244 | 0.0155 |
| 0.0164 | 2.92 | 248 | 0.0149 |
| 0.0168 | 2.96 | 252 | 0.0142 |
| 0.0107 | 3.01 | 256 | 0.0139 |
| 0.0058 | 3.06 | 260 | 0.0138 |
| 0.0124 | 3.11 | 264 | 0.0134 |
| 0.0059 | 3.15 | 268 | 0.0131 |
| 0.0079 | 3.2 | 272 | 0.0129 |
| 0.0048 | 3.25 | 276 | 0.0127 |
| 0.0056 | 3.29 | 280 | 0.0128 |
| 0.0045 | 3.34 | 284 | 0.0130 |
| 0.0159 | 3.39 | 288 | 0.0133 |
| 0.005 | 3.44 | 292 | 0.0133 |
| 0.0043 | 3.48 | 296 | 0.0132 |
| 0.0054 | 3.53 | 300 | 0.0134 |
| 0.0028 | 3.58 | 304 | 0.0133 |
| 0.0051 | 3.62 | 308 | 0.0131 |
| 0.0048 | 3.67 | 312 | 0.0129 |
| 0.0034 | 3.72 | 316 | 0.0128 |
| 0.0265 | 3.76 | 320 | 0.0126 |
| 0.0056 | 3.81 | 324 | 0.0124 |
| 0.0052 | 3.86 | 328 | 0.0122 |
| 0.0027 | 3.91 | 332 | 0.0122 |
| 0.002 | 3.95 | 336 | 0.0123 |
| 0.0064 | 4.0 | 340 | 0.0124 |
| 0.0071 | 4.05 | 344 | 0.0124 |
| 0.004 | 4.09 | 348 | 0.0123 |
| 0.0054 | 4.14 | 352 | 0.0121 |
| 0.0044 | 4.19 | 356 | 0.0121 |
| 0.0019 | 4.24 | 360 | 0.0121 |
| 0.0012 | 4.28 | 364 | 0.0123 |
| 0.0051 | 4.33 | 368 | 0.0122 |
| 0.0045 | 4.38 | 372 | 0.0123 |
| 0.0081 | 4.42 | 376 | 0.0123 |
| 0.0057 | 4.47 | 380 | 0.0123 |
| 0.0026 | 4.52 | 384 | 0.0122 |
| 0.0076 | 4.56 | 388 | 0.0122 |
| 0.0042 | 4.61 | 392 | 0.0122 |
| 0.0056 | 4.66 | 396 | 0.0122 |
| 0.0038 | 4.71 | 400 | 0.0122 |
| 0.0101 | 4.75 | 404 | 0.0122 |
| 0.0052 | 4.8 | 408 | 0.0122 |
| 0.0063 | 4.85 | 412 | 0.0122 |
| 0.0065 | 4.89 | 416 | 0.0122 |
| 0.0094 | 4.94 | 420 | 0.0122 |
| 0.0053 | 4.99 | 424 | 0.0123 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
Shruti9756/G24_BTECH_PROJECT
|
Shruti9756
| 2023-12-15T09:55:40Z | 0 | 1 | null |
[
"license:c-uda",
"region:us"
] | null | 2023-12-15T09:55:40Z |
---
license: c-uda
---
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_13
|
Jeevesh8
| 2023-12-15T15:50:52Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T09:55:44Z |
Entry not found
|
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ali2003/Tez-Tarjima
|
ali2003
| 2023-12-15T09:57:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T09:57:13Z |
Entry not found
|
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] |
Loren85/Renato-Carosone
|
Loren85
| 2023-12-15T09:59:31Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T09:57:58Z |
---
license: openrail
---
|
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samilcoban/ppo-CartPole-v1_v1
|
samilcoban
| 2023-12-15T10:08:09Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T09:59:56Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -150.93 +/- 66.66
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'samilcoban/ppo-CartPole-v1_v1'
'batch_size': 512
'minibatch_size': 128}
```
|
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] |
100yen/distilbert-base-uncased-finetuned-clinc
|
100yen
| 2023-12-17T08:19:21Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:02:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9167741935483871
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7643
- Accuracy: 0.9168
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2689 | 0.7190 |
| 3.7735 | 2.0 | 636 | 1.8549 | 0.8429 |
| 3.7735 | 3.0 | 954 | 1.1411 | 0.8926 |
| 1.6771 | 4.0 | 1272 | 0.8445 | 0.9135 |
| 0.8911 | 5.0 | 1590 | 0.7643 | 0.9168 |
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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shaojieee/q-Taxi-v3
|
shaojieee
| 2023-12-15T10:16:28Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T10:03:01Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
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="shaojieee/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
owanr/Sentiment-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T04:29:49Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T10:03:19Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
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. -->
# Sentiment-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4613
## 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: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.4321 | 1.0 | 5628 | 4.4799 |
| 4.3035 | 2.0 | 11256 | 4.4613 |
| 4.2418 | 3.0 | 16884 | 4.4613 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_15
|
Jeevesh8
| 2023-12-15T15:06:25Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:05:34Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_14
|
Jeevesh8
| 2023-12-15T15:21:39Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:05:34Z |
Entry not found
|
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faqq/dan
|
faqq
| 2023-12-15T10:08:26Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T10:07:20Z |
---
license: openrail
---
|
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] |
matinFT/mlm
|
matinFT
| 2023-12-15T10:55:03Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-15T10:09:51Z |
---
tags:
- generated_from_trainer
model-index:
- name: mlm
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. -->
# mlm
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 73
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 363 | 6.6765 |
| 6.9914 | 2.0 | 726 | 6.5066 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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EmmaGthn/runs_lora_1000_moji
|
EmmaGthn
| 2023-12-15T18:10:54Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T10:10:14Z |
Entry not found
|
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GoSlim/GoSlimGermany
|
GoSlim
| 2023-12-15T10:16:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:15:17Z |
GoSlim: Go Slim Germany Preis, Bewertungen, Tee, Zutaten, Leistungen, Nebenwirkungen, Tee preis, Kosten!
GoSlim ist eine Formel zur Gewichtsabnahme, die aufgrund ihres Potenzials zur Unterstützung der Gewichtsabnahme und des allgemeinen Wohlbefindens Aufmerksamkeit erregt. GoSlim Germany
<a href="https://www.excaliburnutrition.com/GoSGerm">GoSlim Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.excaliburnutrition.com/GoSGerm">www.GoSlim.com</a>
<p><a href="https://www.excaliburnutrition.com/GoSGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3YvWyqjJ7c0kdgM4lIbgU9KHddLZUg61FVXMHIjRu7QnOufU01CmhC3fWLaw10hjTE7KVJPvw3WQRo9GYbCT9InZMsYejm14erdla5dzZM0hBEz33edmhCfyBWxDjJ7pgSJlUB66qL8KnD8VgUlkCUw5MXwV1RYYG6k6hyphenhyphenkSbNycNd5G7eIHV65KhiGY/w604-h376/GoSlim%20Germany.png" alt="enter image description here"> </a></p>
➢Produktname – GoSlim
➢Kategorie – Gewichtsverlust
➢Hauptvorteile – Unterstützung beim Abnehmen
➢ Zusammensetzung – Natürliche organische Verbindung
➢ Nebenwirkungen – Nicht zutreffend
➢Endgültige Bewertung: – 4,8
➢ Verfügbarkeit – Online
➢Angebote und Rabatte; SPAREN SIE HEUTE! JETZT EINKAUFEN, UM SONDERANGEBOT zu kaufen!!!
Was ist GoSlim?
Es besteht aus einer Kombination von Inhaltsstoffen, von denen angenommen wird, dass sie die Fettverbrennung unterstützen, den Stoffwechsel ankurbeln und den Appetit zügeln. GoSlim Preis ist als wertvolle Ergänzung für einen gesunden Lebensstil und ein gesundes Trainingsprogramm konzipiert.
<a href="https://www.excaliburnutrition.com/GoSGerm">GoSlim Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.excaliburnutrition.com/GoSGerm">www.GoSlim.com</a>
<p><a href="https://www.excaliburnutrition.com/GoSGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3YvWyqjJ7c0kdgM4lIbgU9KHddLZUg61FVXMHIjRu7QnOufU01CmhC3fWLaw10hjTE7KVJPvw3WQRo9GYbCT9InZMsYejm14erdla5dzZM0hBEz33edmhCfyBWxDjJ7pgSJlUB66qL8KnD8VgUlkCUw5MXwV1RYYG6k6hyphenhyphenkSbNycNd5G7eIHV65KhiGY/w604-h376/GoSlim%20Germany.png" alt="enter image description here"> </a></p>
GoSlim GoSlim Tee GoSlim Preis GoSlim Bewertungen GoSlim Zutaten GoSlim Leistungen GoSlim Nebenwirkungen GoSlim Tee preis GoSlim Tee Bewertungen GoSlim Komposition GoSlim Beschwerde GoSlim Wo zu kaufen GoSlim Wie benutzt man GoSlim Kosten GoSlim funktioniert GoSlim Forum GoSlim Original GoSlim Apotheke
Wie funktioniert GoSlim?
Die Wirksamkeit von GoSlim liegt in seinen sorgfältig ausgewählten Inhaltsstoffen, von denen jeder eine spezifische Rolle spielt. GoSlim enthält häufig Verbindungen wie Grüntee-Extrakt, Koffein und Capsaicin aus Chilischoten. Es ist bekannt, dass diese Inhaltsstoffe die Kerntemperatur des Körpers erhöhen, den Stoffwechsel ankurbeln und die Verbrennung von gespeichertem Fett zur Energiegewinnung fördern.
Wo kann man GoSlim kaufen?
Auf der offiziellen Website gibt es Websites, auf denen Sie GoSlim direkt kaufen können. Achten Sie beim Kauf von GoSlim darauf, dass Sie aus seriösen Quellen kaufen, um die Produktqualität und Authentizität zu gewährleisten. Seien Sie vorsichtig beim Kauf bei unverifizierten Verkäufern, da es sich um gefälschte Produkte handelt.
GoSlim Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!
<a href="https://www.excaliburnutrition.com/GoSGerm">GoSlim Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.excaliburnutrition.com/GoSGerm">www.GoSlim.com</a>
<p><a href="https://www.excaliburnutrition.com/GoSGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3YvWyqjJ7c0kdgM4lIbgU9KHddLZUg61FVXMHIjRu7QnOufU01CmhC3fWLaw10hjTE7KVJPvw3WQRo9GYbCT9InZMsYejm14erdla5dzZM0hBEz33edmhCfyBWxDjJ7pgSJlUB66qL8KnD8VgUlkCUw5MXwV1RYYG6k6hyphenhyphenkSbNycNd5G7eIHV65KhiGY/w604-h376/GoSlim%20Germany.png" alt="enter image description here"> </a></p>
Zusammenfassung:
Zusammenfassend ist GoSlim ein Nahrungsergänzungsmittel, das zur Unterstützung der Gewichtsabnahme und des allgemeinen Wohlbefindens entwickelt wurde. Sein Wirkungsmechanismus umfasst thermogene Inhaltsstoffe, Appetitzügler, Energiebooster und Fettstoffwechselverstärker. Zu den potenziellen Vorteilen zählen Unterstützung bei der Gewichtsabnahme, gesteigerte Energie, Appetitkontrolle, Stimmungsaufhellung und Bequemlichkeit. Sie können GoSlim auf offiziellen Websites, bei Online-Händlern oder in örtlichen Reformhäusern kaufen.
GoSlim GoSlim Tee GoSlim Preis GoSlim Bewertungen GoSlim Zutaten GoSlim Leistungen GoSlim Nebenwirkungen GoSlim Tee preis GoSlim Tee Bewertungen GoSlim Komposition GoSlim Beschwerde GoSlim Wo zu kaufen GoSlim Wie benutzt man GoSlim Kosten GoSlim funktioniert GoSlim Forum GoSlim Original GoSlim Apotheke
https://www.excaliburnutrition.com/GoSGerm
https://sites.google.com/view/goslim-tee/home
https://healthtoned.blogspot.com/2023/12/goslim-germany.html
https://medium.com/@goslimgermany/goslim-germany-c2bab3189a1c
https://medium.com/@goslimgermany/goslim-go-slim-germany-preis-bewertungen-tee-zutaten-leistungen-nebenwirkungen-tee-preis-6f3b0f757b2b
https://www.weddingwire.com/website/goslim-and-germany
https://infogram.com/goslim-germany-1hmr6g7pjrwyo6n?live
https://softisenilspain.hashnode.dev/goslim-germany
https://goslimgermany.company.site/
https://sway.office.com/frH1TXSezwfuTulm
https://gamma.app/docs/GoSlim-Germany-6ys5amje3qx7gvv?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/goslim-germany
https://groups.google.com/g/snshine/c/D-bqMJ9UE18
https://www.sunflower-cissp.com/glossary/cissp/7293/goslim-germany
https://replit.com/@goslimgermany
https://community.thebatraanumerology.com/
https://community.thebatraanumerology.com/post/goslim-go-slim-germany-preis-bewertungen-tee-zutaten-leistungen-nebenwirkun--657c26d608ca2944504659a0
https://enkling.com/read-blog/9010
https://nepal.tribe.so/post/goslim-go-slim-germany-preis-bewertungen-tee-zutaten-leistungen-nebenwirkun--657c2718e760f2e084f3b427
|
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afonsofroes/outputs
|
afonsofroes
| 2023-12-15T10:16:16Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-15T10:16:11Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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PrunedSafetensors/Merged
|
PrunedSafetensors
| 2023-12-15T23:19:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:16:12Z |
Entry not found
|
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] |
shangrilar/yi-ko-6b-nl2sql
|
shangrilar
| 2023-12-15T10:24:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T10:17:55Z |
Entry not found
|
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DanQueer/TaylorSwiftAIVERSE
|
DanQueer
| 2023-12-15T10:29:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:20:41Z |
Entry not found
|
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Oleg712/men
|
Oleg712
| 2023-12-15T10:20:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:20:58Z |
Entry not found
|
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HuyenNguyen/wav2vec2-2-bart-base
|
HuyenNguyen
| 2023-12-15T10:21:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:21:47Z |
Entry not found
|
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] |
dcs83/llama-2-7b-miniguanaco
|
dcs83
| 2023-12-15T10:31:22Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T10:23:32Z |
Entry not found
|
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] |
riyadahmed/Riyas
|
riyadahmed
| 2023-12-15T10:25:55Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T10:25:55Z |
---
license: openrail
---
|
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teilomillet/MiniMerlin-3B-v0.5
|
teilomillet
| 2023-12-15T15:24:19Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:GeneZC/MiniChat-1.5-3B",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T10:26:20Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: GeneZC/MiniChat-1.5-3B
model-index:
- name: MiniMerlin-3B-v0.5
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. -->
# MiniMerlin-3B-v0.5
This model is a fine-tuned version of [GeneZC/MiniChat-1.5-3B](https://huggingface.co/GeneZC/MiniChat-1.5-3B) on an unknown dataset.
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Arekku21/vivit-b-16x2-kinetics400-finetuned-MSL_40_classes_7
|
Arekku21
| 2023-12-15T12:44:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vivit",
"video-classification",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-15T10:27:19Z |
Entry not found
|
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stoddur/manual8_3_r2048_alpha4096_v1
|
stoddur
| 2023-12-15T10:28:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:27:57Z |
Entry not found
|
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owanr/ghc-roberta-base-inter-shuffle-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T04:37:37Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T10:29:02Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-inter-shuffle-model_annots_alpha0.8_whole_1e-05
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. -->
# ghc-roberta-base-inter-shuffle-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6244
## 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: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.4907 | 1.0 | 11020 | 2.3848 |
| 2.5894 | 2.0 | 22040 | 2.6244 |
| 2.4341 | 3.0 | 33060 | 2.6244 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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owanr/SBIC-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T04:31:36Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T10:30:48Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
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. -->
# SBIC-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5666
## 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: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.3021 | 1.0 | 12516 | 1.5735 |
| 1.1717 | 2.0 | 25032 | 1.5666 |
| 1.1071 | 3.0 | 37548 | 1.5666 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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seatond/CHOSEN_rank32_lr1.5e-05_target8_epochs1_laplha64_wuratio0.14_wdecay0.2
|
seatond
| 2023-12-15T10:34:21Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:TheBloke/Mistral-7B-v0.1-GPTQ",
"region:us"
] | null | 2023-12-15T10:32:27Z |
---
library_name: peft
base_model: TheBloke/Mistral-7B-v0.1-GPTQ
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1.dev0
|
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xavierbarbier/flan-t5-base-ameli_qa
|
xavierbarbier
| 2023-12-15T10:33:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:33:09Z |
Entry not found
|
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William208/DTOriginal_HC_expert
|
William208
| 2023-12-15T10:34:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:34:32Z |
Entry not found
|
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] |
jinnkenny99/mistral_twitter
|
jinnkenny99
| 2023-12-15T10:34:41Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-15T10:34:36Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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outissam/llama-model
|
outissam
| 2023-12-15T10:45:37Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T10:35:54Z |
Entry not found
|
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] |
philip120/first_test
|
philip120
| 2023-12-15T10:38:32Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T10:36:51Z |
---
license: apache-2.0
---
|
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brabo714/cracovitsi
|
brabo714
| 2023-12-15T10:37:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:37:16Z |
Entry not found
|
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nazenin/nazenin
|
nazenin
| 2023-12-15T10:37:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:37:36Z |
Entry not found
|
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] |
owanr/Sentiment-roberta-base-intra-sorted-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T04:49:51Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T10:38:13Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-intra-sorted-model_annots_alpha0.8_whole_1e-05
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. -->
# Sentiment-roberta-base-intra-sorted-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8981
## 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: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.0365 | 1.0 | 5628 | 2.8370 |
| 2.8581 | 2.0 | 11256 | 2.8981 |
| 2.6115 | 3.0 | 16884 | 2.8981 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Kshitij2406/GPTTest
|
Kshitij2406
| 2023-12-15T10:39:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:39:01Z |
Entry not found
|
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davanstrien/SatCLIP-ResNet18-L10
|
davanstrien
| 2023-12-15T13:33:08Z | 0 | 0 | null |
[
"geospatial",
"region:us"
] | null | 2023-12-15T10:39:03Z |
---
tags:
- geospatial
---
# Model Card for SatCLIP-ResNet18-L10
<!-- Provide a quick summary of what the model is/does. -->
Models described in https://github.com/microsoft/satclip/
## 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. -->
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## Use the encoder
```python
from huggingface_hub import hf_hub_download
from load import get_satclip
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
c = torch.randn(32, 2) # Represents a batch of 32 locations (lon/lat)
model = get_satclip(
hf_hub_download("davanstrien/SatCLIP-ResNet18-L10", "satclip-resnet18-l10.ckpt"),
device=device,
) # Only loads location encoder by default
model.eval()
with torch.no_grad():
emb = model(c.double().to(device)).detach().cpu()
```
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
@article{klemmer2023satclip,
title={SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery},
author={Klemmer, Konstantin and Rolf, Esther and Robinson, Caleb and Mackey, Lester and Ru{\ss}wurm, Marc},
journal={arXiv preprint arXiv:2311.17179},
year={2023}
|
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avirtan/distilbert-base-uncased-semeval-2024
|
avirtan
| 2023-12-15T10:39:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:39:39Z |
Entry not found
|
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davanstrien/SatCLIP-ResNet18-L40
|
davanstrien
| 2023-12-15T10:39:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:39:39Z |
Entry not found
|
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davanstrien/SatCLIP-ResNet50-L10
|
davanstrien
| 2023-12-15T10:40:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:40:00Z |
Entry not found
|
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davanstrien/SatCLIP-ResNet50-L40
|
davanstrien
| 2023-12-15T10:40:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:40:32Z |
Entry not found
|
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davanstrien/SatCLIP-ViT16-L10
|
davanstrien
| 2023-12-15T10:41:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:41:06Z |
Entry not found
|
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davanstrien/SatCLIP-ViT16-L40
|
davanstrien
| 2023-12-15T10:41:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:41:36Z |
Entry not found
|
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tndklab/hubert-rinna-jdrt_RTsplitModel-1215-4
|
tndklab
| 2023-12-15T11:09:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T10:42:12Z |
---
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-rinna-jdrt_RTsplitModel-1215-4
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. -->
# hubert-rinna-jdrt_RTsplitModel-1215-4
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1850
- Wer: 0.3876
- Cer: 0.1628
## 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: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 7.0245 | 1.0 | 120 | 6.0444 | 0.9991 | 0.9997 |
| 4.0731 | 2.0 | 240 | 3.7685 | 0.9991 | 0.9997 |
| 2.8386 | 3.0 | 360 | 2.7008 | 0.9991 | 0.9997 |
| 1.4049 | 4.0 | 480 | 1.3000 | 1.0 | 0.5607 |
| 1.0274 | 5.0 | 600 | 0.9815 | 0.8169 | 0.4900 |
| 1.017 | 6.0 | 720 | 0.7540 | 0.8126 | 0.4793 |
| 0.7536 | 7.0 | 840 | 0.8639 | 0.8208 | 0.5068 |
| 0.6671 | 8.0 | 960 | 0.6125 | 0.7969 | 0.4214 |
| 0.67 | 9.0 | 1080 | 0.5575 | 0.7728 | 0.3870 |
| 0.4974 | 10.0 | 1200 | 0.4559 | 0.6903 | 0.3281 |
| 0.4694 | 11.0 | 1320 | 0.3916 | 0.6316 | 0.2381 |
| 0.4187 | 12.0 | 1440 | 0.3749 | 0.6157 | 0.2494 |
| 0.4018 | 13.0 | 1560 | 0.3599 | 0.6064 | 0.2480 |
| 0.3778 | 14.0 | 1680 | 0.3349 | 0.5621 | 0.2004 |
| 0.3138 | 15.0 | 1800 | 0.2936 | 0.5147 | 0.1947 |
| 0.2953 | 16.0 | 1920 | 0.2578 | 0.4880 | 0.2056 |
| 0.2808 | 17.0 | 2040 | 0.2283 | 0.4513 | 0.1732 |
| 0.3104 | 18.0 | 2160 | 0.2069 | 0.4087 | 0.1604 |
| 0.2218 | 19.0 | 2280 | 0.1918 | 0.4007 | 0.1658 |
| 0.2257 | 20.0 | 2400 | 0.1850 | 0.3876 | 0.1628 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
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] |
nairaxo/shikomori-sentiment-bantulm
|
nairaxo
| 2023-12-15T11:10:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:nairaxo/bantulm",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:42:54Z |
---
license: apache-2.0
base_model: nairaxo/bantulm
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: shikomori-sentiment-bantulm
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. -->
# shikomori-sentiment-bantulm
This model is a fine-tuned version of [nairaxo/bantulm](https://huggingface.co/nairaxo/bantulm) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6158
- Accuracy: 0.7517
- F1 Score: 0.7224
- Recall: 0.7230
- Precision: 0.7264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 20
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.7589 | 0.23 | 400 | 0.7039 | 0.6989 | 0.5288 | 0.6096 | 0.4688 |
| 0.7284 | 0.46 | 800 | 0.6998 | 0.6963 | 0.5267 | 0.6073 | 0.4667 |
| 0.6947 | 0.69 | 1200 | 0.6905 | 0.6869 | 0.6462 | 0.6647 | 0.6687 |
| 0.7007 | 0.92 | 1600 | 0.6870 | 0.7121 | 0.6832 | 0.6931 | 0.6897 |
| 0.6404 | 1.15 | 2000 | 0.6294 | 0.7497 | 0.6977 | 0.6994 | 0.7303 |
| 0.6113 | 1.38 | 2400 | 0.6513 | 0.7589 | 0.7211 | 0.7201 | 0.7361 |
| 0.5964 | 1.61 | 2800 | 0.6198 | 0.7497 | 0.7222 | 0.7241 | 0.7252 |
| 0.5959 | 1.84 | 3200 | 0.6158 | 0.7517 | 0.7224 | 0.7230 | 0.7264 |
| 0.5587 | 2.07 | 3600 | 0.6436 | 0.7666 | 0.7378 | 0.7371 | 0.7449 |
| 0.4901 | 2.3 | 4000 | 0.6537 | 0.7715 | 0.7435 | 0.7427 | 0.7507 |
| 0.5183 | 2.53 | 4400 | 0.6669 | 0.7681 | 0.7379 | 0.7368 | 0.7475 |
| 0.4768 | 2.76 | 4800 | 0.6329 | 0.7715 | 0.7392 | 0.7370 | 0.7526 |
| 0.46 | 2.99 | 5200 | 0.6602 | 0.7704 | 0.7366 | 0.7342 | 0.7511 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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-0.5290698409080505
] |
Jeevesh8/bs__qqp_ft_only_data_shuff_16
|
Jeevesh8
| 2023-12-15T15:49:29Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:43:06Z |
Entry not found
|
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s-gladkykh/sky-diffusion-128-lr5
|
s-gladkykh
| 2023-12-15T12:11:43Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-15T10:45:14Z |
Entry not found
|
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Ngit/miniLMv2-toxic-v2-onnx
|
Ngit
| 2023-12-15T10:49:40Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:47:19Z |
Entry not found
|
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samilcoban/rl_course_vizdoom_health_gathering_supreme
|
samilcoban
| 2023-12-15T10:47:50Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T10:47:40Z |
---
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: 12.88 +/- 5.64
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 samilcoban/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.
|
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] |
Seik123/Educate
|
Seik123
| 2023-12-15T10:48:23Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T10:48:23Z |
---
license: apache-2.0
---
|
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] |
nairaxo/shikomori-sentiment-mbert
|
nairaxo
| 2023-12-15T11:16:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-multilingual-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:49:30Z |
---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: shikomori-sentiment-mbert
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. -->
# shikomori-sentiment-mbert
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6170
- Accuracy: 0.7623
- F1 Score: 0.7251
- Recall: 0.7227
- Precision: 0.7388
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 20
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.8085 | 0.23 | 400 | 0.7691 | 0.6762 | 0.5600 | 0.5964 | 0.6579 |
| 0.7291 | 0.46 | 800 | 0.6878 | 0.6983 | 0.5311 | 0.6065 | 0.7488 |
| 0.7168 | 0.69 | 1200 | 0.6666 | 0.7115 | 0.5957 | 0.6313 | 0.6848 |
| 0.7253 | 0.92 | 1600 | 0.7327 | 0.6785 | 0.5152 | 0.5864 | 0.6327 |
| 0.7128 | 1.15 | 2000 | 0.6883 | 0.7156 | 0.6281 | 0.6460 | 0.6876 |
| 0.6783 | 1.38 | 2400 | 0.6657 | 0.6966 | 0.6587 | 0.6754 | 0.6749 |
| 0.6496 | 1.61 | 2800 | 0.6514 | 0.7316 | 0.6553 | 0.6660 | 0.7138 |
| 0.6557 | 1.84 | 3200 | 0.6354 | 0.7451 | 0.6851 | 0.6880 | 0.7238 |
| 0.6336 | 2.07 | 3600 | 0.6684 | 0.7471 | 0.7128 | 0.7127 | 0.7186 |
| 0.5943 | 2.3 | 4000 | 0.6522 | 0.7514 | 0.6997 | 0.6994 | 0.7334 |
| 0.5994 | 2.53 | 4400 | 0.6325 | 0.7514 | 0.7212 | 0.7232 | 0.7259 |
| 0.5712 | 2.76 | 4800 | 0.6348 | 0.7666 | 0.7297 | 0.7264 | 0.7462 |
| 0.5857 | 2.99 | 5200 | 0.6170 | 0.7623 | 0.7251 | 0.7227 | 0.7388 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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KathNotCat/ChanyeolRappingRutson
|
KathNotCat
| 2023-12-15T10:53:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:52:03Z |
Entry not found
|
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tscstudios/ponn6ezs4ehF6xkV98KyxD5z0633
|
tscstudios
| 2023-12-15T10:53:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:52:17Z |
Entry not found
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archibh/effyai
|
archibh
| 2023-12-15T10:52:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:52:59Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_17
|
Jeevesh8
| 2023-12-15T16:03:43Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:54:31Z |
Entry not found
|
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matinFT/mlm_tokenizer
|
matinFT
| 2023-12-15T10:55:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:55:03Z |
Entry not found
|
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balakrishnanm/mymodel
|
balakrishnanm
| 2023-12-15T10:56:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T10:56:38Z |
Entry not found
|
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nairaxo/shikomori-sentiment-afriberta
|
nairaxo
| 2023-12-15T11:28:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:castorini/afriberta_large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:57:44Z |
---
license: mit
base_model: castorini/afriberta_large
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: shikomori-sentiment-afriberta
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. -->
# shikomori-sentiment-afriberta
This model is a fine-tuned version of [castorini/afriberta_large](https://huggingface.co/castorini/afriberta_large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5675
- Accuracy: 0.7764
- F1 Score: 0.7467
- Recall: 0.7434
- Precision: 0.7559
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 20
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.7627 | 0.23 | 400 | 0.7923 | 0.5623 | 0.4598 | 0.5807 | 0.5367 |
| 0.6737 | 0.46 | 800 | 0.6374 | 0.7414 | 0.6729 | 0.6791 | 0.7232 |
| 0.6549 | 0.69 | 1200 | 0.6875 | 0.7305 | 0.6967 | 0.7055 | 0.7152 |
| 0.633 | 0.92 | 1600 | 0.5853 | 0.7629 | 0.7107 | 0.7101 | 0.7461 |
| 0.5786 | 1.15 | 2000 | 0.6384 | 0.7658 | 0.7315 | 0.7311 | 0.7434 |
| 0.5496 | 1.38 | 2400 | 0.6139 | 0.7672 | 0.7412 | 0.7392 | 0.7455 |
| 0.5107 | 1.61 | 2800 | 0.6148 | 0.7675 | 0.7414 | 0.7418 | 0.7447 |
| 0.5344 | 1.84 | 3200 | 0.5675 | 0.7764 | 0.7467 | 0.7434 | 0.7559 |
| 0.4456 | 2.07 | 3600 | 0.7619 | 0.7721 | 0.7396 | 0.7366 | 0.7498 |
| 0.4096 | 2.3 | 4000 | 0.7005 | 0.7830 | 0.7578 | 0.7567 | 0.7628 |
| 0.353 | 2.53 | 4400 | 0.8140 | 0.7830 | 0.7535 | 0.7495 | 0.7645 |
| 0.3578 | 2.76 | 4800 | 0.8051 | 0.7753 | 0.7540 | 0.7565 | 0.7557 |
| 0.3593 | 2.99 | 5200 | 0.7397 | 0.7793 | 0.7577 | 0.7580 | 0.7592 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_18
|
Jeevesh8
| 2023-12-15T16:09:00Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T10:57:55Z |
Entry not found
|
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xepaie/EmilyMontes
|
xepaie
| 2023-12-15T11:00:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T11:00:23Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_19
|
Jeevesh8
| 2023-12-15T16:45:32Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T11:01:55Z |
Entry not found
|
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genericgod/GerMerge-em-leo-mistral-v0.2-SLERP
|
genericgod
| 2023-12-17T15:56:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"pytorch",
"german",
"deutsch",
"leolm",
"merge",
"de",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T11:02:14Z |
---
inference: false
language:
- de
library_name: transformers
license: apache-2.0
model_creator: genericgod
model_name: GerMerge
model_type: mistral
pipeline_tag: text-generation
tags:
- pytorch
- german
- deutsch
- mistral
- leolm
- merge
---
[GGUF here](https://huggingface.co/genericgod/GerMerge-em-leo-mistral-v0.2-SLERP-GGUF)
I made this merge to add better German capabilities to the new Mistral-7B-Instruct-v0.2 model
**Disclaimer:** I am a complete noob. I dont know what I am doing, just playing around with the parameters.
<!-- description start -->
## Description
This repo contains fp16 files of the following two models merged with SLERP.
<!-- description end -->
<!-- description start -->
## Models used
- mistralai/Mistral-7B-Instruct-v0.2 (base model)
- jphme/em_german_leo_mistral
Thanks to all people involved into making these Models!
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Mistral
```
[INST] <instruction> [/INST]
```
# Examples
```
Prompt: [INST]Ich habe heute 4 Äpfel. Gestern aß ich 3 Äpfel. Wie viele Äpfel habe ich heute?[/INST]
Output: Heute hast du 4 Äpfel. Die Äpfel, die du gestern gegessen hast, ändern nichts an der Anzahl der Äpfel, die du heute hast. Daher hast du immer noch 4 Äpfel.
```
```
Prompt: [INST]Welche Möglichkeiten gibt es im Alltag möglichst umweltbewusst zu Leben? Nenne mir 5 Punkte.[/INST]
Output: 1. Reduziere den Energieverbrauch: Du kannst umweltbewusst leben, indem du Energie effizienter nutzt und versuchst, deinen CO2-Fußabdruck zu minimieren. Dazu gehört zum Beispiel die Nutzung von LED-Lampen, das Ausschalten von Geräten bei Nichtgebrauch, die Regulierung der Heizungstemperatur und die Wäsche bei niedrigen Temperaturen zu waschen.
2. Nutze nachhaltige Verkehrsmittel: Umweltfreundlich ist die Nutzung von öffentlichen Verkehrsmitteln, dem Fahrrad oder zu Fuß zu gehen oder mit dem E-Auto zu fahren. Wenn das nicht möglich ist, kannst du CO2-neutral mit dem Zug oder dem Bus fahren.
3. Kaufe saisonale und regionale Produkte: Umweltbewusstes Einkaufen bedeutet, saisonale und regionale Produkte zu kaufen, um die Transportwege zu verkürzen und die Unterstützung lokaler Landwirte und Hersteller zu fördern. Zudem solltest du so wenig Verpackungsmaterialien wie möglich verwenden und Plastiktüten vermeiden.
4. Vermeide Einwegprodukte: Versuche Einwegprodukte zu vermeiden und stattdessen auf Mehrwegprodukte umzusteigen. Nutze wiederverwendbare Taschen, Flaschen und Behälter und kaufe Lebensmittel in großen Mengen, um Verpackungsmüll zu reduzieren.
5. Trenne und recyceln richtig: Richtiges Trennen und Recyceln von Müll ist ein wichtiger Beitrag zum Umweltschutz. Informiere dich über die richtigen Mülltrennsysteme in deiner Gemeinde und trenne deinen Abfall entsprechend. Nutze außerdem Produkte mit möglichst wenig Verpackungsmaterial und kaufe Recyclingprodukte, um die Wiederverwertung von Rohstoffen zu fördern.
```
# Mergekit config
```
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: jphme/em_german_leo_mistral
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.1, 0.6, 0.8, 1]
- filter: mlp
value: [0, 0.1, 0.3, 0.6, 0.9]
- value: 0.8
dtype: float16
```
|
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TheBloke/Llamix2-MLewd-4x13B-GGUF
|
TheBloke
| 2023-12-15T11:09:58Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"not-for-all-audiences",
"nsfw",
"base_model:Undi95/Llamix2-MLewd-4x13B",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-15T11:03:33Z |
---
base_model: Undi95/Llamix2-MLewd-4x13B
inference: false
license: cc-by-nc-4.0
model_creator: Undi
model_name: Llamix2 MLewd 4X13B
model_type: mixtral
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- not-for-all-audiences
- nsfw
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llamix2 MLewd 4X13B - GGUF
- Model creator: [Undi](https://huggingface.co/Undi95)
- Original model: [Llamix2 MLewd 4X13B](https://huggingface.co/Undi95/Llamix2-MLewd-4x13B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Undi's Llamix2 MLewd 4X13B](https://huggingface.co/Undi95/Llamix2-MLewd-4x13B).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
### Mixtral GGUF
Support for Mixtral was merged into Llama.cpp on December 13th.
These Mixtral GGUFs are known to work in:
* llama.cpp as of December 13th
* KoboldCpp 1.52 as later
* LM Studio 0.2.9 and later
* llama-cpp-python 0.2.23 and later
Other clients/libraries, not listed above, may not yet work.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llamix2-MLewd-4x13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llamix2-MLewd-4x13B-GGUF)
* [Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Undi95/Llamix2-MLewd-4x13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These Mixtral GGUFs are compatible with llama.cpp from December 13th onwards. Other clients/libraries may not work yet.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llamix2-mlewd-4x13b.Q4_0.gguf](https://huggingface.co/TheBloke/Llamix2-MLewd-4x13B-GGUF/blob/main/llamix2-mlewd-4x13b.Q4_0.gguf) | Q4_0 | 4 | 21.70 GB| 24.20 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llamix2-mlewd-4x13b.Q4_1.gguf](https://huggingface.co/TheBloke/Llamix2-MLewd-4x13B-GGUF/blob/main/llamix2-mlewd-4x13b.Q4_1.gguf) | Q4_1 | 4 | 24.10 GB| 26.60 GB | legacy; small, substantial quality loss - lprefer using Q3_K_L |
| [llamix2-mlewd-4x13b.Q5_0.gguf](https://huggingface.co/TheBloke/Llamix2-MLewd-4x13B-GGUF/blob/main/llamix2-mlewd-4x13b.Q5_0.gguf) | Q5_0 | 5 | 26.49 GB| 28.99 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llamix2-mlewd-4x13b.Q5_1.gguf](https://huggingface.co/TheBloke/Llamix2-MLewd-4x13B-GGUF/blob/main/llamix2-mlewd-4x13b.Q5_1.gguf) | Q5_1 | 5 | 28.89 GB| 31.39 GB | legacy; medium, low quality loss - prefer using Q5_K_M |
| [llamix2-mlewd-4x13b.Q8_0.gguf](https://huggingface.co/TheBloke/Llamix2-MLewd-4x13B-GGUF/blob/main/llamix2-mlewd-4x13b.Q8_0.gguf) | Q8_0 | 8 | 40.91 GB| 43.41 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Llamix2-MLewd-4x13B-GGUF and below it, a specific filename to download, such as: llamix2-mlewd-4x13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Llamix2-MLewd-4x13B-GGUF llamix2-mlewd-4x13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Llamix2-MLewd-4x13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Llamix2-MLewd-4x13B-GGUF llamix2-mlewd-4x13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m llamix2-mlewd-4x13b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Note that text-generation-webui may not yet be compatible with Mixtral GGUFs. Please check compatibility first.
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) version 0.2.23 and later.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./llamix2-mlewd-4x13b.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./llamix2-mlewd-4x13b.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Undi's Llamix2 MLewd 4X13B
THIS MODEL IS MADE FOR LEWD
SEXUAL, CRUDE AND KINKY CONTENT IN OUTPUT CAN AND WILL HAPPEN. YOU'RE WARNED
This is a 4x13B MoE Llama2 model, one of the first (if not the first!).
Always, a big thanks to [Charles Goddard](https://huggingface.co/chargoddard) who is the brain behind all of those new Mixtral model, and his amazing tools!
WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0!
<!-- description start -->
## Description
This repo contains fp16 files of Llamix2-MLewd-4x13B, a very hot MoE of Llama2 model.
<!-- description end -->
<!-- description start -->
## Models used
The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Llamix2-MLewd-4x13B/blob/main/config.yaml)
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
Special thanks to Sushi and Shena ♥
If you want to support me, you can [here](https://ko-fi.com/undiai).
<!-- original-model-card end -->
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_20
|
Jeevesh8
| 2023-12-15T16:34:31Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T11:05:52Z |
Entry not found
|
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saankhya1997/meditron-gptq-finetuned-mimic
|
saankhya1997
| 2023-12-15T11:06:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T11:06:12Z |
Entry not found
|
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KathNotCat/D.ORutson
|
KathNotCat
| 2023-12-15T11:12:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T11:08:53Z |
Entry not found
|
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tndklab/hubert-rinna-jdrt_RTsplitModel-1215-5
|
tndklab
| 2023-12-15T11:55:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T11:11:15Z |
---
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-rinna-jdrt_RTsplitModel-1215-5
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. -->
# hubert-rinna-jdrt_RTsplitModel-1215-5
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2770
- Wer: 0.5420
- Cer: 0.2374
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 12.0297 | 1.0 | 120 | 10.2562 | 1.0 | 0.9472 |
| 7.0581 | 2.0 | 240 | 6.1661 | 1.0 | 0.9472 |
| 5.312 | 3.0 | 360 | 4.9546 | 1.0 | 0.9472 |
| 3.9139 | 4.0 | 480 | 3.8564 | 1.0 | 0.9472 |
| 3.2285 | 5.0 | 600 | 3.1767 | 1.0 | 0.9472 |
| 2.6113 | 6.0 | 720 | 2.4556 | 1.0 | 0.9472 |
| 1.7406 | 7.0 | 840 | 1.6190 | 1.0 | 0.6068 |
| 1.2264 | 8.0 | 960 | 1.1353 | 1.0 | 0.5162 |
| 1.0808 | 9.0 | 1080 | 1.0242 | 1.0 | 0.5289 |
| 0.8341 | 10.0 | 1200 | 0.7843 | 0.8167 | 0.4801 |
| 0.7725 | 11.0 | 1320 | 0.7190 | 0.8146 | 0.4639 |
| 0.7042 | 12.0 | 1440 | 0.6704 | 0.8144 | 0.4651 |
| 0.6663 | 13.0 | 1560 | 0.6311 | 0.7963 | 0.4114 |
| 0.5988 | 14.0 | 1680 | 0.5592 | 0.7796 | 0.3989 |
| 0.5432 | 15.0 | 1800 | 0.4928 | 0.7057 | 0.3360 |
| 0.5115 | 16.0 | 1920 | 0.4626 | 0.7063 | 0.3268 |
| 0.4978 | 17.0 | 2040 | 0.4563 | 0.6791 | 0.2938 |
| 0.5146 | 18.0 | 2160 | 0.4279 | 0.6589 | 0.3008 |
| 0.436 | 19.0 | 2280 | 0.4060 | 0.6449 | 0.2991 |
| 0.428 | 20.0 | 2400 | 0.3818 | 0.6198 | 0.2836 |
| 0.4174 | 21.0 | 2520 | 0.3864 | 0.6150 | 0.2687 |
| 0.3937 | 22.0 | 2640 | 0.3566 | 0.5981 | 0.2606 |
| 0.3854 | 23.0 | 2760 | 0.3747 | 0.6030 | 0.2522 |
| 0.3588 | 24.0 | 2880 | 0.3331 | 0.5746 | 0.2504 |
| 0.3493 | 25.0 | 3000 | 0.3164 | 0.5742 | 0.2540 |
| 0.3521 | 26.0 | 3120 | 0.2981 | 0.5613 | 0.2518 |
| 0.3575 | 27.0 | 3240 | 0.2928 | 0.5585 | 0.2449 |
| 0.3317 | 28.0 | 3360 | 0.2820 | 0.5441 | 0.2411 |
| 0.3345 | 29.0 | 3480 | 0.2817 | 0.5422 | 0.2371 |
| 0.3442 | 30.0 | 3600 | 0.2770 | 0.5420 | 0.2374 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
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saankhya1997/meditron-finetuned-mimic
|
saankhya1997
| 2023-12-15T11:13:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T11:13:32Z |
Entry not found
|
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] |
owanr/Sentiment-roberta-base-inter-shuffle-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T05:10:40Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T11:13:53Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-inter-shuffle-human_annots_alpha0.8_whole_1e-05
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. -->
# Sentiment-roberta-base-inter-shuffle-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8181
## 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: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.972 | 1.0 | 5628 | 3.8825 |
| 3.7316 | 2.0 | 11256 | 3.8181 |
| 3.7752 | 3.0 | 16884 | 3.8181 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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0.542974054813385,
-0.25961676239967346,
0.40570783615112305,
0.12646490335464478,
0.5524831414222717,
-0.4757468104362488,
-0.43541383743286133,
-0.6053565740585327,
0.07770097255706787,
0.2672244608402252,
-0.4332161545753479
] |
marsggbo/OpenMoE
|
marsggbo
| 2023-12-15T12:24:44Z | 0 | 0 |
transformers
|
[
"transformers",
"en",
"dataset:wikitext-2-v1",
"dataset:yizhongw/self_instruct",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T11:14:33Z |
---
datasets:
- wikitext-2-v1
- yizhongw/self_instruct
language:
- en
library_name: transformers
metrics: crossentropy
---
1. download the pth file
2. load the state dict
```python
from transformers import T5Tokenizer
from transformers.models.llama import LlamaConfig
config = LlamaConfig.from_pretrained(f"hpcaitech/openmoe-base")
model = OpenMoeForCausalLM(config)
ckpt = torch.load("openmoe_base_yizhongw_super_natural_instruction_generation.pth")
state_dict = {}
for key, value in ckpt.items():
if key.startswith("module."):
state_dict[key[7:]] = value
else:
state_dict[key] = value
model.load_state_dict(state_dict)
```
|
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] |
TheBloke/Tiamat-7B-1.1-DPO-AWQ
|
TheBloke
| 2023-12-15T11:36:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"base_model:Gryphe/Tiamat-7b-1.1-DPO",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-15T11:19:17Z |
---
base_model: Gryphe/Tiamat-7b-1.1-DPO
inference: false
language:
- en
license: apache-2.0
model_creator: Gryphe Padar
model_name: Tiamat 7B 1.1 DPO
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Tiamat 7B 1.1 DPO - AWQ
- Model creator: [Gryphe Padar](https://huggingface.co/Gryphe)
- Original model: [Tiamat 7B 1.1 DPO](https://huggingface.co/Gryphe/Tiamat-7b-1.1-DPO)
<!-- description start -->
## Description
This repo contains AWQ model files for [Gryphe Padar's Tiamat 7B 1.1 DPO](https://huggingface.co/Gryphe/Tiamat-7b-1.1-DPO).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Tiamat-7B-1.1-DPO-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Tiamat-7B-1.1-DPO-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Tiamat-7B-1.1-DPO-GGUF)
* [Gryphe Padar's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Gryphe/Tiamat-7b-1.1-DPO)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Tiamat-7B-1.1-DPO-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Tiamat-7B-1.1-DPO-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Tiamat-7B-1.1-DPO-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Tiamat-7B-1.1-DPO-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Tiamat-7B-1.1-DPO-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Tiamat-7B-1.1-DPO-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Tiamat-7B-1.1-DPO-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Gryphe Padar's Tiamat 7B 1.1 DPO
# Tiamat
Aka I wanted something like [Eric Hartford's Samantha](https://erichartford.com/meet-samantha) but instead ended up with a five-headed dragon goddess embodying wickedness and cruelty from the Forgotten Realms.
**Version 1.1:** Now further refined with a partial dataset refresh and DPO training, the rejections being based on answers given by a cheerful AI assistant with an overly positive demeanour. Essentially, the exact opposite of Tiamat.
**Obligatory Disclaimer:** Tiamat is **not** nice.
## Model details
Ever wanted to be treated disdainfully like the foolish mortal you are? Wait no more, for Tiamat is here to berate you! Hailing from the world of the Forgotten Realms, she will happily judge your every word.
Tiamat was created with the following question in mind; Is it possible to create an assistant with strong anti-assistant personality traits? Try it yourself and tell me afterwards!
She was fine-tuned on top of Teknium's excellent [OpenHermes 2.5](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and can be summoned to you using the following system message;
```
You are Tiamat, a five-headed dragon goddess, embodying wickedness and cruelty.
```
Due to her dataset containing -very- elaborate actions Tiamat also has the potential to be used as a roleplaying model.
## Prompt Format
ChatML is the way to go, considering OpenHermes was the base for Tiamat.
```
<|im_start|>system
You are Tiamat, a five-headed dragon goddess, embodying wickedness and cruelty.<|im_end|>
<|im_start|>user
Greetings, mighty Tiamat. I seek your guidance.<|im_end|>
<|im_start|>assistant
```
|
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] |
technocrat3128/mistral7b-sharded-finetune-15-dec-23
|
technocrat3128
| 2023-12-15T11:25:54Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:filipealmeida/Mistral-7B-Instruct-v0.1-sharded",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T11:19:22Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: filipealmeida/Mistral-7B-Instruct-v0.1-sharded
model-index:
- name: mistral7b-sharded-finetune-v02-15-12-23
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. -->
# mistral7b-sharded-finetune-v02-15-12-23
This model is a fine-tuned version of [filipealmeida/Mistral-7B-Instruct-v0.1-sharded](https://huggingface.co/filipealmeida/Mistral-7B-Instruct-v0.1-sharded) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6360
## 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: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2358 | 0.07 | 100 | 0.9831 |
| 0.8642 | 0.14 | 200 | 0.8056 |
| 0.7985 | 0.21 | 300 | 0.7710 |
| 0.7652 | 0.28 | 400 | 0.7572 |
| 0.7553 | 0.35 | 500 | 0.7369 |
| 0.7306 | 0.42 | 600 | 0.7226 |
| 0.7245 | 0.5 | 700 | 0.7107 |
| 0.7094 | 0.57 | 800 | 0.7006 |
| 0.6905 | 0.64 | 900 | 0.6918 |
| 0.7043 | 0.71 | 1000 | 0.6825 |
| 0.6945 | 0.78 | 1100 | 0.6752 |
| 0.6953 | 0.85 | 1200 | 0.6691 |
| 0.6886 | 0.92 | 1300 | 0.6643 |
| 0.6578 | 0.99 | 1400 | 0.6579 |
| 0.5995 | 1.06 | 1500 | 0.6562 |
| 0.6121 | 1.13 | 1600 | 0.6508 |
| 0.6302 | 1.2 | 1700 | 0.6453 |
| 0.6329 | 1.27 | 1800 | 0.6415 |
| 0.6061 | 1.34 | 1900 | 0.6404 |
| 0.6321 | 1.42 | 2000 | 0.6391 |
| 0.6198 | 1.49 | 2100 | 0.6373 |
| 0.5865 | 1.56 | 2200 | 0.6369 |
| 0.6279 | 1.63 | 2300 | 0.6362 |
| 0.5896 | 1.7 | 2400 | 0.6361 |
| 0.6052 | 1.77 | 2500 | 0.6360 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
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] |
hrandrIAga/CSC5401
|
hrandrIAga
| 2023-12-15T11:31:48Z | 0 | 0 | null |
[
"license:afl-3.0",
"region:us"
] | null | 2023-12-15T11:24:55Z |
---
license: afl-3.0
---
|
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] |
Adam228/BoL3.0
|
Adam228
| 2023-12-15T11:36:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T11:24:58Z |
| Step | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy |
|------|---------------|------------------|-----------|----------|----------|----------|
| 100 | No log | 0.697793 | 0.317073 | 0.022688 | 0.042345 | 0.903853 |
| 200 | No log | 0.575497 | 0.302326 | 0.022688 | 0.042208 | 0.903969 |
| 300 | No log | 0.464735 | 0.140845 | 0.052356 | 0.076336 | 0.913340 |
| 400 | No log | 0.426836 | 0.255144 | 0.108202 | 0.151961 | 0.916811 |
| 500 | 0.469800 | 0.384735 | 0.320856 | 0.209424 | 0.253432 | 0.924216 |
| 600 | 0.469800 | 0.356535 | 0.352227 | 0.303665 | 0.326148 | 0.928266 |
| 700 | 0.469800 | 0.329858 | 0.424460 | 0.411867 | 0.418069 | 0.931737 |
| 800 | 0.469800 | 0.327570 | 0.422680 | 0.357766 | 0.387524 | 0.932431 |
| 900 | 0.469800 | 0.287014 | 0.471698 | 0.436300 | 0.453309 | 0.940877 |
| 1000 | 0.169300 | 0.284001 | 0.521336 | 0.490401 | 0.505396 | 0.946893 |
| 1100 | 0.169300 | 0.270087 | 0.501799 | 0.486911 | 0.494243 | 0.949555 |
| 1200 | 0.169300 | 0.259359 | 0.549165 | 0.516579 | 0.532374 | 0.951753 |
| 1300 | 0.169300 | 0.250138 | 0.562731 | 0.532286 | 0.547085 | 0.952794 |
| 1400 | 0.169300 | 0.268176 | 0.532468 | 0.572426 | 0.551724 | 0.949555 |
| 1500 | 0.105800 | 0.244026 | 0.570470 | 0.593368 | 0.581694 | 0.957191 |
| 1600 | 0.105800 | 0.231457 | 0.584590 | 0.609075 | 0.596581 | 0.958811 |
| 1700 | 0.105800 | 0.229934 | 0.582382 | 0.623037 | 0.602024 | 0.958348 |
| 1800 | 0.105800 | 0.235728 | 0.603741 | 0.619546 | 0.611542 | 0.959621 |
| 1900 | 0.105800 | 0.230202 | 0.620926 | 0.631763 | 0.626298 | 0.961356 |
| 2000 | 0.068600 | 0.229400 | 0.603896 | 0.649215 | 0.625736 | 0.961472 |
| 2100 | 0.068600 | 0.235245 | 0.614478 | 0.636998 | 0.625536 | 0.960778 |
| 2200 | 0.068600 | 0.229506 | 0.641541 | 0.668412 | 0.654701 | 0.962976 |
| 2300 | 0.068600 | 0.233685 | 0.620746 | 0.668412 | 0.643697 | 0.960778 |
| 2400 | 0.068600 | 0.238696 | 0.615385 | 0.670157 | 0.641604 | 0.960430 |
| 2500 | 0.054200 | 0.227996 | 0.639935 | 0.682373 | 0.660473 | 0.962397 |
| 2600 | 0.054200 | 0.231834 | 0.618898 | 0.685864 | 0.650662 | 0.961703 |
| 2700 | 0.054200 | 0.228750 | 0.614780 | 0.682373 | 0.646816 | 0.962860 |
| 2800 | 0.054200 | 0.226999 | 0.614767 | 0.668412 | 0.640468 | 0.962860 |
| 2900 | 0.054200 | 0.229335 | 0.612083 | 0.671902 | 0.640599 | 0.962976 |
| 3000 | 0.047400 | 0.229813 | 0.603476 | 0.666667 | 0.633499 | 0.962282 |
|
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ArashAI55523/ArashIr
|
ArashAI55523
| 2023-12-15T11:25:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T11:25:13Z |
Entry not found
|
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] |
vmg1957/bert-finetuned-squad
|
vmg1957
| 2023-12-17T00:38:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-15T11:30:30Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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owanr/SBIC-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T04:57:57Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T11:31:12Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
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. -->
# SBIC-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4172
## 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: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.5719 | 1.0 | 12516 | 3.3184 |
| 3.4538 | 2.0 | 25032 | 3.4172 |
| 3.6609 | 3.0 | 37548 | 3.4172 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Tejaswi006/zephyr-7b-dpo-lora
|
Tejaswi006
| 2023-12-17T08:15:10Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T11:32:09Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-dpo-lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-dpo-lora
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: 0.5261
- Rewards/chosen: -0.1563
- Rewards/rejected: -0.9035
- Rewards/accuracies: 0.7315
- Rewards/margins: 0.7472
- Logps/rejected: -232.6935
- Logps/chosen: -266.1220
- Logits/rejected: -1.9669
- Logits/chosen: -2.0211
## 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-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5647 | 1.0 | 968 | 0.5544 | -0.1110 | -0.6475 | 0.7130 | 0.5365 | -230.1329 | -265.6688 | -2.0148 | -2.0684 |
| 0.5569 | 2.0 | 1936 | 0.5303 | -0.1538 | -0.8646 | 0.7320 | 0.7108 | -232.3041 | -266.0967 | -1.9784 | -2.0322 |
| 0.5188 | 3.0 | 2904 | 0.5261 | -0.1563 | -0.9035 | 0.7315 | 0.7472 | -232.6935 | -266.1220 | -1.9669 | -2.0211 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
gloryspace/Taesan
|
gloryspace
| 2023-12-15T11:32:28Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-15T11:32:28Z |
---
license: creativeml-openrail-m
---
|
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] |
Shamik/whisper-small-bn
|
Shamik
| 2023-12-16T14:19:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"bn",
"dataset:mozilla-foundation/common_voice_15_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T11:38:51Z |
---
language:
- bn
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
metrics:
- wer
model-index:
- name: Whisper Small finetuned on Bengali
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 15
type: mozilla-foundation/common_voice_15_0
config: bn
split: validation
args: bn
metrics:
- name: Wer
type: wer
value: 33.68672144182348
---
<!-- 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 finetuned on Bengali
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 15 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2886
- Wer Ortho: 66.3996
- Wer: 33.6867
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.5075 | 0.8 | 100 | 0.4573 | 77.5920 | 45.3485 |
| 0.257 | 1.6 | 200 | 0.2886 | 66.3996 | 33.6867 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Federic/federico-LLM-fine-tuning-TEST
|
Federic
| 2023-12-15T11:39:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"codegen",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-15T11:39:08Z |
Entry not found
|
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owanr/ghc-roberta-base-intra-shuffle-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T05:16:24Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T11:40:45Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-intra-shuffle-model_annots_alpha0.8_whole_1e-05
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. -->
# ghc-roberta-base-intra-shuffle-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6630
## 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: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6544 | 1.0 | 11020 | 1.6646 |
| 1.6301 | 2.0 | 22040 | 1.6630 |
| 1.6392 | 3.0 | 33060 | 1.6630 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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LovePantyhose/temp
|
LovePantyhose
| 2023-12-15T11:41:43Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T11:41:42Z |
---
license: openrail
---
|
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andrijdavid/Llama-Code-7b-GGUF
|
andrijdavid
| 2023-12-15T12:46:38Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-15T11:43:14Z |
Entry not found
|
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] |
owanr/Sentiment-roberta-base-inter-shuffle-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T05:30:37Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T11:49:10Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-inter-shuffle-model_annots_alpha0.8_whole_1e-05
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. -->
# Sentiment-roberta-base-inter-shuffle-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7335
## 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: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.8064 | 1.0 | 5628 | 4.7456 |
| 4.6489 | 2.0 | 11256 | 4.7335 |
| 4.605 | 3.0 | 16884 | 4.7335 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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MMoin/llama-2-7b-platypus-Moin
|
MMoin
| 2023-12-15T11:49:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T11:49:28Z |
Entry not found
|
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] |
soniarocca31/modello_finetunato
|
soniarocca31
| 2023-12-15T11:51:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:swiss_law_area_prediction",
"base_model:distilbert-base-multilingual-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T11:50:52Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- swiss_law_area_prediction
metrics:
- precision
- recall
- f1
model-index:
- name: modello_finetunato
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: swiss_law_area_prediction
type: swiss_law_area_prediction
config: main
split: validation
args: main
metrics:
- name: Precision
type: precision
value: 0.9891749133791669
- name: Recall
type: recall
value: 0.9851535516598432
- name: F1
type: f1
value: 0.9870492704202976
---
<!-- 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. -->
# modello_finetunato
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the swiss_law_area_prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0745
- Precision: 0.9892
- Recall: 0.9852
- F1: 0.9870
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.0002 | 0.38 | 500 | 0.1228 | 0.9760 | 0.9777 | 0.9768 |
| 0.1344 | 0.76 | 1000 | 0.1097 | 0.9793 | 0.9815 | 0.9804 |
| 0.0708 | 1.15 | 1500 | 0.1495 | 0.9719 | 0.9705 | 0.9708 |
| 0.0001 | 1.53 | 2000 | 0.0612 | 0.9898 | 0.9881 | 0.9889 |
| 0.0001 | 1.91 | 2500 | 0.0745 | 0.9892 | 0.9852 | 0.9870 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
ADISH007/Aws_donut_10k_incremental_1_Epoch_27
|
ADISH007
| 2023-12-15T11:51:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T11:51:11Z |
Entry not found
|
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Kooten/DaringMaid-13B-GGUF
|
Kooten
| 2023-12-15T18:16:33Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"region:us"
] |
text-generation
| 2023-12-15T11:51:41Z |
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
---
# This repo contains GGUF quants of DaringMaid-13B
My goal was to make a Noromaid that's smarter and better at following instructions.
After trying a bunch of different recipes I think this one turned out pretty good
- I used [sequelbox/DynamicFactor](https://huggingface.co/sequelbox/DynamicFactor) as a base to as its supposed "improve overall knowledge, precise communication, conceptual understanding, and technical skill" over the base llama2.
- [NeverSleep/Noromaid](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1.1) of course.
- [Undi95/Utopia](https://huggingface.co/Undi95/Utopia-13B) has been recommended again recently and its still really good so in the mixer it goes
- I liked [tavtav/Rose](https://huggingface.co/tavtav/Rose-20B) so i threw in a bit of [CalderaAI/Thorns](https://huggingface.co/CalderaAI/13B-Thorns-l2)
- There was recently a model that tried to pass itself off as [Gryphe/MythoMax](https://huggingface.co/Gryphe/MythoMax-L2-13b), i made a merge with that model before it was revealed to be MythoMax and it turned out pretty good so i used it.
The .yml config files for mergekit with the exact merges can be found in the ["Recipe"](https://huggingface.co/Kooten/DaringMaid-13B/tree/main/Recipe) folder in the [fp16 repo](https://huggingface.co/Kooten/DaringMaid-13B)
# Quants
EXL2: [8bpw](https://huggingface.co/Kooten/DaringMaid-13B-8bpw-exl2), [5bpw](https://huggingface.co/Kooten/DaringMaid-13B-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/DaringMaid-13B-4bpw-exl2)
[GGUF](https://huggingface.co/Kooten/DaringMaid-13B-GGUF):
[Q3_K_M](https://huggingface.co/Kooten/DaringMaid-13B-GGUF/blob/main/DaringMaid-13B-Q3_K_M.gguf) - [Q4_K_M](https://huggingface.co/Kooten/DaringMaid-13B-GGUF/blob/main/DaringMaid-13B-Q4_K_M.gguf) - [Q5_K_M](https://huggingface.co/Kooten/DaringMaid-13B-GGUF/blob/main/DaringMaid-13B-Q5_K_M.gguf)
## Prompt template:
I have been using Undi/Ikaris SillyTavern presets for Noromaid: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not include descriptions of non-visual qualities such as personality, movements, scents, mental traits, or anything which could not be seen in a still photograph. Do not write in full sentences. Prefix your description with the phrase 'full body portrait,'
### Instruction:
{prompt}
### Response:
```
### Contact
Kooten on discord.
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_0
|
Jeevesh8
| 2023-12-15T13:48:01Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T11:54:46Z |
Entry not found
|
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xyfJASON/VAEs-Implementations
|
xyfJASON
| 2023-12-16T03:03:16Z | 0 | 0 | null |
[
"tensorboard",
"license:mit",
"region:us"
] | null | 2023-12-15T12:03:20Z |
---
license: mit
---
Checkpoints and training logs for GitHub repository: [xyfJASON/VAEs-Implementations](https://github.com/xyfJASON/VAEs-Implementations).
|
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