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null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Testing Data
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | xu1998hz/45_dpo_lora_ucb_sec_ucb | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:14:33+00:00 |
null | null |
<!-- 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. -->
# V0424HMA13
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0488
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6572 | 0.09 | 10 | 0.3872 |
| 0.1981 | 0.18 | 20 | 0.1144 |
| 0.1118 | 0.27 | 30 | 0.0984 |
| 0.0959 | 0.36 | 40 | 0.0833 |
| 0.0831 | 0.45 | 50 | 0.0732 |
| 0.0945 | 0.54 | 60 | 0.0784 |
| 0.0878 | 0.63 | 70 | 0.0747 |
| 0.0786 | 0.73 | 80 | 0.0775 |
| 0.0818 | 0.82 | 90 | 0.0726 |
| 0.0794 | 0.91 | 100 | 0.0704 |
| 0.0775 | 1.0 | 110 | 0.0680 |
| 0.0616 | 1.09 | 120 | 0.0699 |
| 0.0599 | 1.18 | 130 | 0.0760 |
| 0.0732 | 1.27 | 140 | 0.0713 |
| 0.0631 | 1.36 | 150 | 0.0712 |
| 0.0722 | 1.45 | 160 | 0.0682 |
| 0.0654 | 1.54 | 170 | 0.0810 |
| 0.0808 | 1.63 | 180 | 0.0714 |
| 0.1626 | 1.72 | 190 | 0.0920 |
| 1.8023 | 1.81 | 200 | 0.4369 |
| 0.1372 | 1.9 | 210 | 0.0750 |
| 0.0738 | 1.99 | 220 | 0.0726 |
| 0.0475 | 2.08 | 230 | 0.0786 |
| 0.0444 | 2.18 | 240 | 0.0704 |
| 0.0416 | 2.27 | 250 | 0.0661 |
| 0.0371 | 2.36 | 260 | 0.0608 |
| 0.0662 | 2.45 | 270 | 0.0548 |
| 0.0309 | 2.54 | 280 | 0.0504 |
| 0.0218 | 2.63 | 290 | 0.0492 |
| 0.0228 | 2.72 | 300 | 0.0494 |
| 0.0308 | 2.81 | 310 | 0.0490 |
| 0.0263 | 2.9 | 320 | 0.0490 |
| 0.0232 | 2.99 | 330 | 0.0488 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0424HMA13", "results": []}]} | Litzy619/V0424HMA13 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
]
| null | 2024-04-26T03:15:32+00:00 |
null | peft |
# Model Card for Model ID
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
## Training Details
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "shrenikb/sparsegpt75sparsitymodel"} | shrenikb/sparsegpt75sparsityagg | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:shrenikb/sparsegpt75sparsitymodel",
"region:us"
]
| null | 2024-04-26T03:17:38+00:00 |
null | peft |
# Model Card for Model ID
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## Model Details
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
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[More Information Needed]
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "shrenikb/sparsegpt25sparsitymodel"} | shrenikb/sparsegpt25sparsityagg | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:shrenikb/sparsegpt25sparsitymodel",
"region:us"
]
| null | 2024-04-26T03:18:34+00:00 |
text-generation | transformers |
# Model Card for alokabhishek/Meta-Llama-3-8B-Instruct-6.0-bpw-exl2
<!-- Provide a quick summary of what the model is/does. -->
This repo contains 6-bit quantized (using ExLlamaV2) model of Meta's meta-llama/Meta-Llama-3-8B-Instruct
## Model Details
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
### About 6 bit quantization using ExLlamaV2
- ExLlamaV2 github repo: [ExLlamaV2 github repo](https://github.com/turboderp/exllamav2)
# How to Get Started with the Model ExLlamaV2
Use the code below to get started with the model.
I will update the python code to inference later.
## How to run the model using
#### First install the package
```shell
# Install ExLLamaV2
git clone https://github.com/turboderp/exllamav2
cd exllamav2
pip install -r requirements.txt
pip install .
```
#### set up variables
```python
# Define the model ID for the desired model
model_id = "alokabhishek/Meta-Llama-3-8B-Instruct-6.0-bpw-exl2"
# define variables
model_name = model_id.split("/")[-1]
```
#### Download the quantized model
```shell
!git-lfs install
# download the model to loacl directory
!git clone https://{username}:{HF_TOKEN}@huggingface.co/{model_id} {model_name}
```
#### Run Inference on quantized model using chat template
```shell
# Run model
# change the path of the model
python examples/chat.py -m "../quant/alokabhishek/Meta-Llama-3-8B-Instruct-6.0-bpw-exl2" -mode llama3
```
## Original Model Card by Meta:
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| {"license": "other", "library_name": "transformers", "tags": ["6bit", "llama", "llama-3", "facebook", "meta", "8b", "quantized", "ExLlamaV2", "quantized", "exl2", "6.0-bpw"], "license_name": "llama3", "license_link": "LICENSE", "pipeline_tag": "text-generation"} | alokabhishek/Meta-Llama-3-8B-Instruct-6.0-bpw-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"6bit",
"llama-3",
"facebook",
"meta",
"8b",
"quantized",
"ExLlamaV2",
"exl2",
"6.0-bpw",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T03:18:45+00:00 |
null | peft |
# Model Card for Model ID
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- PEFT 0.10.0 | {"library_name": "peft", "base_model": "shrenikb/sparsegpt50sparsitymodel"} | shrenikb/sparsegpt50sparsityagg | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:shrenikb/sparsegpt50sparsitymodel",
"region:us"
]
| null | 2024-04-26T03:18:51+00:00 |
null | peft |
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- PEFT 0.10.0 | {"library_name": "peft", "base_model": "huggyllama/llama-7b"} | shrenikb/sparsegpt0sparsityagg | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"region:us"
]
| null | 2024-04-26T03:19:00+00:00 |
null | peft |
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- PEFT 0.10.0 | {"library_name": "peft", "base_model": "shrenikb/sparsegpt75sparsitymodel"} | shrenikb/sparsegpt75sparsity | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:shrenikb/sparsegpt75sparsitymodel",
"region:us"
]
| null | 2024-04-26T03:19:23+00:00 |
null | peft |
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- PEFT 0.10.0 | {"library_name": "peft", "base_model": "shrenikb/sparsegpt25sparsitymodel"} | shrenikb/sparsegpt25sparsity | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:shrenikb/sparsegpt25sparsitymodel",
"region:us"
]
| null | 2024-04-26T03:19:37+00:00 |
null | peft |
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- PEFT 0.10.0 | {"library_name": "peft", "base_model": "shrenikb/sparsegpt25sparsitymodel"} | shrenikb/sparsegpt50sparsity | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:shrenikb/sparsegpt25sparsitymodel",
"region:us"
]
| null | 2024-04-26T03:19:52+00:00 |
null | peft |
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- PEFT 0.10.0 | {"library_name": "peft", "base_model": "huggyllama/llama-7b"} | shrenikb/sparsegpt0sparsity | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"region:us"
]
| null | 2024-04-26T03:19:58+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | kylegrove/ShotLlama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T03:20:34+00:00 |
null | null | {} | Kimty/sql_coder_adapter | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-04-26T03:21:11+00:00 |
|
text-generation | transformers |
## Llama 3 8B 256K
[<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)
This model uses [PoSE](https://huggingface.co/papers/2309.10400) to extend Llama's context length from 8k to 256k and beyond @ rope_theta: 500000.0.
For this model, we build upon our 64k model with 75M tokens of continued pretraining data from SlimPajama to extend the context to 256k @ rope_theta: 500k.
We have not been able to test the needle in haystack due to issues with inferencing at these long contexts.
Thanks to [Crusoe Energy](https://twitter.com/CrusoeEnergy) for the compute support for this model.
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
>>> import transformers
>>> import torch
>>> model_id = "meta-llama/Meta-Llama-3-8B"
>>> pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
>>> pipeline("Hey how are you doing today?")
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/*" --local-dir Meta-Llama-3-8B
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| {"language": ["en"], "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "axolotl"], "pipeline_tag": "text-generation"} | winglian/llama-3-8b-256k-PoSE | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"axolotl",
"en",
"arxiv:2309.10400",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T03:24:22+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3-seqsight_4096_512_27M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2835
- F1 Score: 0.8864
- Accuracy: 0.8864
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.4145 | 2.13 | 200 | 0.3614 | 0.8358 | 0.8370 |
| 0.2922 | 4.26 | 400 | 0.3062 | 0.8737 | 0.8737 |
| 0.2637 | 6.38 | 600 | 0.3086 | 0.8717 | 0.8717 |
| 0.2479 | 8.51 | 800 | 0.2954 | 0.8817 | 0.8818 |
| 0.2313 | 10.64 | 1000 | 0.3000 | 0.8844 | 0.8844 |
| 0.2154 | 12.77 | 1200 | 0.3156 | 0.8777 | 0.8778 |
| 0.2039 | 14.89 | 1400 | 0.3139 | 0.8724 | 0.8724 |
| 0.1896 | 17.02 | 1600 | 0.3125 | 0.8784 | 0.8784 |
| 0.1743 | 19.15 | 1800 | 0.3347 | 0.8751 | 0.8751 |
| 0.1602 | 21.28 | 2000 | 0.3601 | 0.8797 | 0.8798 |
| 0.1542 | 23.4 | 2200 | 0.3792 | 0.8743 | 0.8744 |
| 0.136 | 25.53 | 2400 | 0.3798 | 0.8784 | 0.8784 |
| 0.1269 | 27.66 | 2600 | 0.4045 | 0.8670 | 0.8671 |
| 0.1183 | 29.79 | 2800 | 0.4342 | 0.8629 | 0.8631 |
| 0.1038 | 31.91 | 3000 | 0.4230 | 0.8651 | 0.8651 |
| 0.0982 | 34.04 | 3200 | 0.4496 | 0.8584 | 0.8584 |
| 0.0884 | 36.17 | 3400 | 0.4520 | 0.8718 | 0.8717 |
| 0.0818 | 38.3 | 3600 | 0.4904 | 0.8656 | 0.8657 |
| 0.0748 | 40.43 | 3800 | 0.4968 | 0.8622 | 0.8624 |
| 0.0697 | 42.55 | 4000 | 0.5272 | 0.8737 | 0.8737 |
| 0.0603 | 44.68 | 4200 | 0.5579 | 0.8564 | 0.8564 |
| 0.0584 | 46.81 | 4400 | 0.5943 | 0.8636 | 0.8637 |
| 0.0573 | 48.94 | 4600 | 0.5655 | 0.8704 | 0.8704 |
| 0.0512 | 51.06 | 4800 | 0.5970 | 0.8743 | 0.8744 |
| 0.0466 | 53.19 | 5000 | 0.6273 | 0.8703 | 0.8704 |
| 0.0448 | 55.32 | 5200 | 0.6674 | 0.8723 | 0.8724 |
| 0.0429 | 57.45 | 5400 | 0.6685 | 0.8689 | 0.8691 |
| 0.0402 | 59.57 | 5600 | 0.6652 | 0.8691 | 0.8691 |
| 0.0407 | 61.7 | 5800 | 0.6661 | 0.8717 | 0.8717 |
| 0.037 | 63.83 | 6000 | 0.7372 | 0.8622 | 0.8624 |
| 0.0334 | 65.96 | 6200 | 0.6942 | 0.8663 | 0.8664 |
| 0.0308 | 68.09 | 6400 | 0.6933 | 0.8730 | 0.8731 |
| 0.0302 | 70.21 | 6600 | 0.7081 | 0.8757 | 0.8758 |
| 0.029 | 72.34 | 6800 | 0.7236 | 0.8757 | 0.8758 |
| 0.0287 | 74.47 | 7000 | 0.7465 | 0.8704 | 0.8704 |
| 0.0249 | 76.6 | 7200 | 0.7735 | 0.8763 | 0.8764 |
| 0.0283 | 78.72 | 7400 | 0.7489 | 0.8744 | 0.8744 |
| 0.0262 | 80.85 | 7600 | 0.7690 | 0.8689 | 0.8691 |
| 0.0249 | 82.98 | 7800 | 0.7440 | 0.8744 | 0.8744 |
| 0.0244 | 85.11 | 8000 | 0.7504 | 0.8689 | 0.8691 |
| 0.0218 | 87.23 | 8200 | 0.7853 | 0.8697 | 0.8697 |
| 0.0222 | 89.36 | 8400 | 0.7698 | 0.8730 | 0.8731 |
| 0.0202 | 91.49 | 8600 | 0.7779 | 0.8764 | 0.8764 |
| 0.0194 | 93.62 | 8800 | 0.7931 | 0.8737 | 0.8737 |
| 0.0191 | 95.74 | 9000 | 0.7939 | 0.8757 | 0.8758 |
| 0.0199 | 97.87 | 9200 | 0.7886 | 0.8717 | 0.8717 |
| 0.0165 | 100.0 | 9400 | 0.8108 | 0.8670 | 0.8671 |
| 0.0197 | 102.13 | 9600 | 0.8093 | 0.8717 | 0.8717 |
| 0.0178 | 104.26 | 9800 | 0.7966 | 0.8724 | 0.8724 |
| 0.0151 | 106.38 | 10000 | 0.7996 | 0.8737 | 0.8737 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_27M", "model-index": [{"name": "GUE_EMP_H3-seqsight_4096_512_27M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3-seqsight_4096_512_27M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T03:24:28+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "policy", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | AkiraHase/policy | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| null | 2024-04-26T03:26:56+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H4ac-seqsight_4096_512_27M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5410
- F1 Score: 0.7287
- Accuracy: 0.7284
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6259 | 0.93 | 200 | 0.5740 | 0.6989 | 0.7021 |
| 0.5792 | 1.87 | 400 | 0.5544 | 0.7205 | 0.7202 |
| 0.562 | 2.8 | 600 | 0.5481 | 0.7152 | 0.7164 |
| 0.5576 | 3.74 | 800 | 0.5429 | 0.7247 | 0.7252 |
| 0.548 | 4.67 | 1000 | 0.5398 | 0.7249 | 0.7252 |
| 0.5408 | 5.61 | 1200 | 0.5373 | 0.7302 | 0.7299 |
| 0.5428 | 6.54 | 1400 | 0.5349 | 0.7332 | 0.7328 |
| 0.5295 | 7.48 | 1600 | 0.5352 | 0.7276 | 0.7279 |
| 0.5376 | 8.41 | 1800 | 0.5335 | 0.7331 | 0.7331 |
| 0.5341 | 9.35 | 2000 | 0.5348 | 0.7329 | 0.7326 |
| 0.5258 | 10.28 | 2200 | 0.5325 | 0.7290 | 0.7290 |
| 0.5295 | 11.21 | 2400 | 0.5313 | 0.7402 | 0.7399 |
| 0.5229 | 12.15 | 2600 | 0.5339 | 0.7373 | 0.7370 |
| 0.5243 | 13.08 | 2800 | 0.5340 | 0.7352 | 0.7349 |
| 0.523 | 14.02 | 3000 | 0.5296 | 0.7406 | 0.7405 |
| 0.5188 | 14.95 | 3200 | 0.5300 | 0.7354 | 0.7352 |
| 0.5163 | 15.89 | 3400 | 0.5327 | 0.7398 | 0.7396 |
| 0.5165 | 16.82 | 3600 | 0.5306 | 0.7299 | 0.7305 |
| 0.517 | 17.76 | 3800 | 0.5334 | 0.7370 | 0.7367 |
| 0.5166 | 18.69 | 4000 | 0.5315 | 0.7366 | 0.7370 |
| 0.5119 | 19.63 | 4200 | 0.5314 | 0.7390 | 0.7387 |
| 0.5158 | 20.56 | 4400 | 0.5309 | 0.7296 | 0.7302 |
| 0.5108 | 21.5 | 4600 | 0.5333 | 0.7450 | 0.7449 |
| 0.5083 | 22.43 | 4800 | 0.5309 | 0.7314 | 0.7326 |
| 0.5108 | 23.36 | 5000 | 0.5301 | 0.7443 | 0.7440 |
| 0.5096 | 24.3 | 5200 | 0.5282 | 0.7387 | 0.7390 |
| 0.5083 | 25.23 | 5400 | 0.5275 | 0.7409 | 0.7411 |
| 0.5103 | 26.17 | 5600 | 0.5271 | 0.7415 | 0.7413 |
| 0.508 | 27.1 | 5800 | 0.5256 | 0.7431 | 0.7431 |
| 0.5032 | 28.04 | 6000 | 0.5281 | 0.7416 | 0.7416 |
| 0.5048 | 28.97 | 6200 | 0.5299 | 0.7455 | 0.7452 |
| 0.5069 | 29.91 | 6400 | 0.5273 | 0.7367 | 0.7372 |
| 0.5063 | 30.84 | 6600 | 0.5287 | 0.7428 | 0.7425 |
| 0.5043 | 31.78 | 6800 | 0.5256 | 0.7393 | 0.7396 |
| 0.5036 | 32.71 | 7000 | 0.5242 | 0.7413 | 0.7413 |
| 0.4999 | 33.64 | 7200 | 0.5259 | 0.7390 | 0.7387 |
| 0.5041 | 34.58 | 7400 | 0.5242 | 0.7414 | 0.7413 |
| 0.5053 | 35.51 | 7600 | 0.5244 | 0.7418 | 0.7416 |
| 0.4996 | 36.45 | 7800 | 0.5241 | 0.7417 | 0.7416 |
| 0.5033 | 37.38 | 8000 | 0.5247 | 0.7446 | 0.7443 |
| 0.4981 | 38.32 | 8200 | 0.5244 | 0.7416 | 0.7413 |
| 0.5009 | 39.25 | 8400 | 0.5233 | 0.7434 | 0.7434 |
| 0.5033 | 40.19 | 8600 | 0.5236 | 0.7413 | 0.7413 |
| 0.4998 | 41.12 | 8800 | 0.5225 | 0.7412 | 0.7411 |
| 0.4971 | 42.06 | 9000 | 0.5236 | 0.7412 | 0.7411 |
| 0.5 | 42.99 | 9200 | 0.5246 | 0.7440 | 0.7437 |
| 0.4979 | 43.93 | 9400 | 0.5240 | 0.7413 | 0.7411 |
| 0.498 | 44.86 | 9600 | 0.5233 | 0.7426 | 0.7425 |
| 0.497 | 45.79 | 9800 | 0.5234 | 0.7421 | 0.7419 |
| 0.4998 | 46.73 | 10000 | 0.5237 | 0.7424 | 0.7422 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_27M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_4096_512_27M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_4096_512_27M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T03:27:11+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H4ac-seqsight_4096_512_27M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5309
- F1 Score: 0.7398
- Accuracy: 0.7396
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6122 | 0.93 | 200 | 0.5550 | 0.7223 | 0.7229 |
| 0.5593 | 1.87 | 400 | 0.5433 | 0.7320 | 0.7317 |
| 0.5428 | 2.8 | 600 | 0.5354 | 0.7346 | 0.7346 |
| 0.5353 | 3.74 | 800 | 0.5343 | 0.7300 | 0.7308 |
| 0.5283 | 4.67 | 1000 | 0.5318 | 0.7348 | 0.7355 |
| 0.5193 | 5.61 | 1200 | 0.5327 | 0.7378 | 0.7378 |
| 0.5198 | 6.54 | 1400 | 0.5292 | 0.7424 | 0.7422 |
| 0.505 | 7.48 | 1600 | 0.5237 | 0.7407 | 0.7405 |
| 0.5119 | 8.41 | 1800 | 0.5235 | 0.7422 | 0.7419 |
| 0.5045 | 9.35 | 2000 | 0.5197 | 0.7398 | 0.7396 |
| 0.4968 | 10.28 | 2200 | 0.5218 | 0.7434 | 0.7446 |
| 0.4994 | 11.21 | 2400 | 0.5175 | 0.7469 | 0.7466 |
| 0.4908 | 12.15 | 2600 | 0.5280 | 0.7499 | 0.7499 |
| 0.4909 | 13.08 | 2800 | 0.5222 | 0.7457 | 0.7455 |
| 0.4862 | 14.02 | 3000 | 0.5185 | 0.7497 | 0.7496 |
| 0.4844 | 14.95 | 3200 | 0.5192 | 0.7495 | 0.7493 |
| 0.4779 | 15.89 | 3400 | 0.5242 | 0.7510 | 0.7507 |
| 0.4764 | 16.82 | 3600 | 0.5253 | 0.7434 | 0.7431 |
| 0.477 | 17.76 | 3800 | 0.5342 | 0.7390 | 0.7390 |
| 0.4754 | 18.69 | 4000 | 0.5230 | 0.7429 | 0.7437 |
| 0.4711 | 19.63 | 4200 | 0.5207 | 0.7482 | 0.7481 |
| 0.4728 | 20.56 | 4400 | 0.5208 | 0.7459 | 0.7460 |
| 0.4681 | 21.5 | 4600 | 0.5242 | 0.7480 | 0.7478 |
| 0.4626 | 22.43 | 4800 | 0.5269 | 0.7412 | 0.7422 |
| 0.4635 | 23.36 | 5000 | 0.5220 | 0.7496 | 0.7493 |
| 0.4627 | 24.3 | 5200 | 0.5192 | 0.7436 | 0.7440 |
| 0.459 | 25.23 | 5400 | 0.5241 | 0.7455 | 0.7457 |
| 0.4582 | 26.17 | 5600 | 0.5300 | 0.7381 | 0.7387 |
| 0.4565 | 27.1 | 5800 | 0.5315 | 0.7375 | 0.7378 |
| 0.4509 | 28.04 | 6000 | 0.5258 | 0.7503 | 0.7504 |
| 0.4517 | 28.97 | 6200 | 0.5304 | 0.7495 | 0.7493 |
| 0.4535 | 29.91 | 6400 | 0.5292 | 0.7389 | 0.7390 |
| 0.4518 | 30.84 | 6600 | 0.5269 | 0.7474 | 0.7472 |
| 0.4486 | 31.78 | 6800 | 0.5285 | 0.7375 | 0.7381 |
| 0.4482 | 32.71 | 7000 | 0.5299 | 0.7378 | 0.7384 |
| 0.4444 | 33.64 | 7200 | 0.5294 | 0.7434 | 0.7431 |
| 0.4462 | 34.58 | 7400 | 0.5298 | 0.7376 | 0.7375 |
| 0.4456 | 35.51 | 7600 | 0.5353 | 0.7427 | 0.7425 |
| 0.4399 | 36.45 | 7800 | 0.5332 | 0.7419 | 0.7419 |
| 0.4443 | 37.38 | 8000 | 0.5290 | 0.7450 | 0.7449 |
| 0.437 | 38.32 | 8200 | 0.5337 | 0.7421 | 0.7422 |
| 0.438 | 39.25 | 8400 | 0.5350 | 0.7390 | 0.7393 |
| 0.4408 | 40.19 | 8600 | 0.5383 | 0.7382 | 0.7387 |
| 0.4398 | 41.12 | 8800 | 0.5312 | 0.7420 | 0.7419 |
| 0.4356 | 42.06 | 9000 | 0.5355 | 0.7422 | 0.7422 |
| 0.4365 | 42.99 | 9200 | 0.5348 | 0.7434 | 0.7431 |
| 0.4333 | 43.93 | 9400 | 0.5351 | 0.7432 | 0.7431 |
| 0.4367 | 44.86 | 9600 | 0.5353 | 0.7430 | 0.7431 |
| 0.4345 | 45.79 | 9800 | 0.5348 | 0.7405 | 0.7405 |
| 0.4355 | 46.73 | 10000 | 0.5348 | 0.7412 | 0.7411 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_27M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_4096_512_27M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_4096_512_27M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T03:27:11+00:00 |
null | null | {} | elliotthwang/KimLanpure-phi-3-zh.GGUF | null | [
"gguf",
"region:us"
]
| null | 2024-04-26T03:28:40+00:00 |
|
image-text-to-text | xtuner |
<div align="center">
<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
[](https://github.com/InternLM/xtuner)
</div>
## Model
llava-llama-3-8b-v1_1-hf is a LLaVA model fine-tuned from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner).
**Note: This model is in HuggingFace LLaVA format.**
Resources:
- GitHub: [xtuner](https://github.com/InternLM/xtuner)
- Official LLaVA format model: [xtuner/llava-llama-3-8b-v1_1-hf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-hf)
- XTuner LLaVA format model: [xtuner/llava-llama-3-8b-v1_1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1)
- GGUF format model: [xtuner/llava-llama-3-8b-v1_1-gguf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf)
## Details
| Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset |
| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: |
| LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) |
| LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) |
| LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) |
## Results
<div align="center">
<img src="https://github.com/InternLM/xtuner/assets/36994684/a157638c-3500-44ed-bfab-d8d8249f91bb" alt="Image" width=500" />
</div>
| Model | MMBench Test (EN) | MMBench Test (CN) | CCBench Dev | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar |
| :-------------------- | :---------------: | :---------------: | :---------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: |
| LLaVA-v1.5-7B | 66.5 | 59.0 | 27.5 | 35.3 | 60.5 | 54.8 | 70.4 | 44.9 | 85.9 | 62.0 | 58.2 | 1511/348 | 30.3 |
| LLaVA-Llama-3-8B | 68.9 | 61.6 | 30.4 | 36.8 | 69.8 | 60.9 | 73.3 | 47.3 | 87.2 | 63.5 | 58.0 | 1506/295 | 38.2 |
| LLaVA-Llama-3-8B-v1.1 | 72.3 | 66.4 | 31.6 | 36.8 | 70.1 | 70.0 | 72.9 | 47.7 | 86.4 | 62.6 | 59.0 | 1469/349 | 45.1 |
## QuickStart
### Chat by `pipeline`
```python
from transformers import pipeline
from PIL import Image
import requests
model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"
pipe = pipeline("image-to-text", model=model_id, device=0)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n")
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
>>> [{'generated_text': 'user\n\n\nWhat are these?assistant\n\nThese are two cats, one brown and one gray, lying on a pink blanket. sleep. brown and gray cat sleeping on a pink blanket.'}]
```
### Chat by pure `transformers`
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"
prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n")
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
>>> These are two cats, one brown and one gray, lying on a pink blanket. sleep. brown and gray cat sleeping on a pink blanket.
```
### Reproduce
Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme).
## Citation
```bibtex
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}
```
| {"library_name": "xtuner", "datasets": ["Lin-Chen/ShareGPT4V"], "pipeline_tag": "image-text-to-text"} | xtuner/llava-llama-3-8b-v1_1-transformers | null | [
"xtuner",
"safetensors",
"llava",
"image-text-to-text",
"dataset:Lin-Chen/ShareGPT4V",
"region:us",
"has_space"
]
| null | 2024-04-26T03:29:09+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Entreprenerdly/blip2-opt-2.7b-flickr-captions | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:30:07+00:00 |
null | null | {} | Viol2000/opt-2048-lmsys-llama3-8b | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T03:30:13+00:00 |
|
image-text-to-text | xtuner |
<div align="center">
<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
[](https://github.com/InternLM/xtuner)
</div>
## Model
llava-llama-3-8b-transformers is a LLaVA model fine-tuned from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) and [LLaVA-Instruct](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) by [XTuner](https://github.com/InternLM/xtuner).
**Note: This model is in HuggingFace LLaVA format.**
Resources:
- GitHub: [xtuner](https://github.com/InternLM/xtuner)
- Official LLaVA format model: [xtuner/llava-llama-3-8b-hf](https://huggingface.co/xtuner/llava-llama-3-8b-hf)
- XTuner LLaVA format model: [xtuner/llava-llama-3-8b](https://huggingface.co/xtuner/llava-llama-3-8b)
## Details
| Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset |
| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: |
| LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) |
| LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) |
| LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) |
## Results
<div align="center">
<img src="https://github.com/InternLM/xtuner/assets/36994684/a157638c-3500-44ed-bfab-d8d8249f91bb" alt="Image" width=500" />
</div>
| Model | MMBench Test (EN) | MMBench Test (CN) | CCBench Dev | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar |
| :-------------------- | :---------------: | :---------------: | :---------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: |
| LLaVA-v1.5-7B | 66.5 | 59.0 | 27.5 | 35.3 | 60.5 | 54.8 | 70.4 | 44.9 | 85.9 | 62.0 | 58.2 | 1511/348 | 30.3 |
| LLaVA-Llama-3-8B | 68.9 | 61.6 | 30.4 | 36.8 | 69.8 | 60.9 | 73.3 | 47.3 | 87.2 | 63.5 | 58.0 | 1506/295 | 38.2 |
| LLaVA-Llama-3-8B-v1.1 | 72.3 | 66.4 | 31.6 | 36.8 | 70.1 | 70.0 | 72.9 | 47.7 | 86.4 | 62.6 | 59.0 | 1469/349 | 45.1 |
## QuickStart
### Chat by `pipeline`
```python
from transformers import pipeline
from PIL import Image
import requests
model_id = "xtuner/llava-llama-3-8b-transformers"
pipe = pipeline("image-to-text", model=model_id, device=0)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n")
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
>>> [{'generated_text': 'user\n\n\nWhat are these?assistant\n\nThese are two cats lying on a pink blanket or bed, possibly on a couch...'}]
```
### Chat by pure `transformers`
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
model_id = "xtuner/llava-llama-3-8b-transformers"
prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n")
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
>>> These are two cats lying on a pink blanket or bed, possibly on a couch...
```
### Reproduce
Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme).
## Citation
```bibtex
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}
```
| {"library_name": "xtuner", "datasets": ["liuhaotian/LLaVA-Pretrain", "liuhaotian/LLaVA-Instruct-150K"], "pipeline_tag": "image-text-to-text"} | xtuner/llava-llama-3-8b-transformers | null | [
"xtuner",
"safetensors",
"llava",
"image-text-to-text",
"dataset:liuhaotian/LLaVA-Pretrain",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"region:us"
]
| null | 2024-04-26T03:31:50+00:00 |
null | null | {} | Viol2000/opt-2048-sharegpt-llama3-8b | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T03:32:34+00:00 |
|
automatic-speech-recognition | transformers | {} | xeon0618/indic_gujarati_phoneme | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:34:27+00:00 |
|
null | null | {"license": "openrail"} | C0ttontheBunny/MarkHamillJokerOv2 | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-26T03:37:26+00:00 |
|
text2text-generation | transformers |
<!-- 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. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1885
- Bleu: 0.2481
- Gen Len: 18.1229
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 3.6375 | 1.0 | 1617 | 3.2720 | 0.2169 | 18.1607 |
| 3.5146 | 2.0 | 3234 | 3.1885 | 0.2481 | 18.1229 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_opus_books_model", "results": []}]} | GauravR12060102/my_awesome_opus_books_model | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T03:37:39+00:00 |
text-generation | transformers |
[](https://github.com/mbzuai-oryx/LLaVA-pp)
# Phi-3-V: Extending the Visual Capabilities of LLaVA with Phi-3
## Repository Overview
This repository features LLaVA v1.5 trained with the Phi-3-mini-3.8B LLM. This integration aims to leverage the strengths of both models to offer advanced vision-language understanding.
## Training Strategy
- **Pretraining:** Only Vision-to-Language projector is trained. The rest of the model is frozen.
- **Fine-tuning:** LLM is LoRA fine-tuned. Only the vision-backbone (CLIP) is kept frozen.
- **Note:** The repository contains merged weights.
## Key Components
- **Base Large Language Model (LLM):** [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
- **Base Large Multimodal Model (LMM):** [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA)
## Training Data
- **Pretraining Dataset:** [LCS-558K](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)
- **Fine-tuning Dataset:** [LLaVA-Instruct-665K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json)
## Download It As
```
git lfs install
git clone https://huggingface.co/MBZUAI/LLaVA-Phi-3-mini-4k-instruct
```
---
## License
This project is available under the MIT License.
## Contributions
Contributions are welcome! Please 🌟 our repository [LLaVA++](https://github.com/mbzuai-oryx/LLaVA-pp) if you find this model useful.
---
| {"license": "mit"} | MBZUAI/LLaVA-Phi-3-mini-4k-instruct | null | [
"transformers",
"safetensors",
"llava_phi",
"text-generation",
"conversational",
"custom_code",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2024-04-26T03:37:48+00:00 |
null | null | {} | SubHumanZZ/KDA-More-LoRA | null | [
"region:us"
]
| null | 2024-04-26T03:39:53+00:00 |
|
null | null | {} | ashishp-wiai/Rice_LoRA_10-2024-04-26 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T03:39:56+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_EMP_H4ac-seqsight_4096_512_27M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5309
- F1 Score: 0.7385
- Accuracy: 0.7387
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6001 | 0.93 | 200 | 0.5472 | 0.7270 | 0.7267 |
| 0.5475 | 1.87 | 400 | 0.5367 | 0.7401 | 0.7399 |
| 0.5283 | 2.8 | 600 | 0.5270 | 0.7364 | 0.7367 |
| 0.5177 | 3.74 | 800 | 0.5247 | 0.7382 | 0.7384 |
| 0.5097 | 4.67 | 1000 | 0.5238 | 0.7283 | 0.7302 |
| 0.4998 | 5.61 | 1200 | 0.5188 | 0.7484 | 0.7481 |
| 0.4969 | 6.54 | 1400 | 0.5146 | 0.7491 | 0.7490 |
| 0.4784 | 7.48 | 1600 | 0.5195 | 0.7532 | 0.7531 |
| 0.4851 | 8.41 | 1800 | 0.5202 | 0.7480 | 0.7478 |
| 0.4766 | 9.35 | 2000 | 0.5189 | 0.7435 | 0.7440 |
| 0.4662 | 10.28 | 2200 | 0.5197 | 0.7506 | 0.7510 |
| 0.4649 | 11.21 | 2400 | 0.5269 | 0.7437 | 0.7434 |
| 0.4547 | 12.15 | 2600 | 0.5346 | 0.7454 | 0.7452 |
| 0.4504 | 13.08 | 2800 | 0.5264 | 0.7442 | 0.7443 |
| 0.4421 | 14.02 | 3000 | 0.5442 | 0.7419 | 0.7416 |
| 0.4404 | 14.95 | 3200 | 0.5503 | 0.7408 | 0.7408 |
| 0.4293 | 15.89 | 3400 | 0.5446 | 0.7391 | 0.7396 |
| 0.4224 | 16.82 | 3600 | 0.5466 | 0.7344 | 0.7343 |
| 0.4185 | 17.76 | 3800 | 0.5662 | 0.7408 | 0.7408 |
| 0.4137 | 18.69 | 4000 | 0.5553 | 0.7398 | 0.7405 |
| 0.4088 | 19.63 | 4200 | 0.5448 | 0.7501 | 0.7501 |
| 0.4079 | 20.56 | 4400 | 0.5559 | 0.7483 | 0.7487 |
| 0.3959 | 21.5 | 4600 | 0.5573 | 0.7452 | 0.7455 |
| 0.3888 | 22.43 | 4800 | 0.5805 | 0.7334 | 0.7349 |
| 0.3879 | 23.36 | 5000 | 0.5730 | 0.7438 | 0.7440 |
| 0.3826 | 24.3 | 5200 | 0.5756 | 0.7489 | 0.7490 |
| 0.3767 | 25.23 | 5400 | 0.5813 | 0.7444 | 0.7443 |
| 0.37 | 26.17 | 5600 | 0.5960 | 0.7346 | 0.7349 |
| 0.3691 | 27.1 | 5800 | 0.5868 | 0.7416 | 0.7413 |
| 0.3598 | 28.04 | 6000 | 0.5940 | 0.7437 | 0.7446 |
| 0.3561 | 28.97 | 6200 | 0.6076 | 0.7430 | 0.7434 |
| 0.356 | 29.91 | 6400 | 0.5996 | 0.7294 | 0.7293 |
| 0.3508 | 30.84 | 6600 | 0.6091 | 0.7510 | 0.7510 |
| 0.3492 | 31.78 | 6800 | 0.6054 | 0.7423 | 0.7431 |
| 0.3427 | 32.71 | 7000 | 0.6303 | 0.7360 | 0.7375 |
| 0.3409 | 33.64 | 7200 | 0.6185 | 0.7356 | 0.7355 |
| 0.3399 | 34.58 | 7400 | 0.6315 | 0.7375 | 0.7372 |
| 0.3347 | 35.51 | 7600 | 0.6304 | 0.7371 | 0.7370 |
| 0.3284 | 36.45 | 7800 | 0.6407 | 0.7390 | 0.7390 |
| 0.3307 | 37.38 | 8000 | 0.6309 | 0.7466 | 0.7466 |
| 0.3237 | 38.32 | 8200 | 0.6472 | 0.7419 | 0.7425 |
| 0.323 | 39.25 | 8400 | 0.6481 | 0.7403 | 0.7411 |
| 0.3262 | 40.19 | 8600 | 0.6497 | 0.7407 | 0.7411 |
| 0.3191 | 41.12 | 8800 | 0.6471 | 0.7424 | 0.7425 |
| 0.3164 | 42.06 | 9000 | 0.6501 | 0.7431 | 0.7434 |
| 0.3164 | 42.99 | 9200 | 0.6510 | 0.7422 | 0.7422 |
| 0.3106 | 43.93 | 9400 | 0.6534 | 0.7375 | 0.7375 |
| 0.3129 | 44.86 | 9600 | 0.6580 | 0.7400 | 0.7405 |
| 0.3124 | 45.79 | 9800 | 0.6572 | 0.7394 | 0.7396 |
| 0.3102 | 46.73 | 10000 | 0.6574 | 0.7393 | 0.7396 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_27M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_4096_512_27M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_4096_512_27M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T03:40:42+00:00 |
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-25_23-42-55 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T03:42:55+00:00 |
|
null | null | {} | suakeler/Choke-15 | null | [
"region:us"
]
| null | 2024-04-26T03:43:48+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep34 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:44:50+00:00 |
null | null |
<!-- 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. -->
# V0424MADP7
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1462
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.2839 | 0.09 | 10 | 2.9011 |
| 4.9418 | 0.18 | 20 | 2.0801 |
| 1.6294 | 0.27 | 30 | 0.7598 |
| 0.3283 | 0.36 | 40 | 0.3066 |
| 0.1783 | 0.45 | 50 | 0.1865 |
| 0.165 | 0.54 | 60 | 0.1732 |
| 0.1637 | 0.63 | 70 | 0.1631 |
| 0.1705 | 0.73 | 80 | 0.1824 |
| 0.1606 | 0.82 | 90 | 0.1726 |
| 0.1605 | 0.91 | 100 | 0.1570 |
| 0.1584 | 1.0 | 110 | 0.1551 |
| 0.1545 | 1.09 | 120 | 0.1514 |
| 0.1608 | 1.18 | 130 | 0.1490 |
| 0.1578 | 1.27 | 140 | 0.1484 |
| 0.1535 | 1.36 | 150 | 0.1583 |
| 0.1506 | 1.45 | 160 | 0.1484 |
| 0.1547 | 1.54 | 170 | 0.1588 |
| 0.1555 | 1.63 | 180 | 0.1501 |
| 0.1558 | 1.72 | 190 | 0.1580 |
| 0.1549 | 1.81 | 200 | 0.1523 |
| 0.1584 | 1.9 | 210 | 0.1558 |
| 0.1549 | 1.99 | 220 | 0.1527 |
| 0.1568 | 2.08 | 230 | 0.1499 |
| 0.1479 | 2.18 | 240 | 0.1470 |
| 0.1484 | 2.27 | 250 | 0.1486 |
| 0.1483 | 2.36 | 260 | 0.1490 |
| 0.15 | 2.45 | 270 | 0.1473 |
| 0.147 | 2.54 | 280 | 0.1477 |
| 0.1471 | 2.63 | 290 | 0.1464 |
| 0.148 | 2.72 | 300 | 0.1465 |
| 0.1468 | 2.81 | 310 | 0.1462 |
| 0.1483 | 2.9 | 320 | 0.1462 |
| 0.1497 | 2.99 | 330 | 0.1462 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0424MADP7", "results": []}]} | Litzy619/V0424MADP7 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
]
| null | 2024-04-26T03:45:03+00:00 |
null | null |
<!-- 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. -->
# V0424MADP8
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1495
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.3771 | 0.09 | 10 | 2.9344 |
| 4.6458 | 0.18 | 20 | 1.8852 |
| 1.2652 | 0.27 | 30 | 0.6344 |
| 0.2498 | 0.36 | 40 | 0.2440 |
| 0.1679 | 0.45 | 50 | 0.2180 |
| 0.1649 | 0.54 | 60 | 0.1695 |
| 0.1796 | 0.63 | 70 | 0.3313 |
| 0.1677 | 0.73 | 80 | 0.1943 |
| 0.1639 | 0.82 | 90 | 0.1927 |
| 0.1603 | 0.91 | 100 | 0.1908 |
| 0.1604 | 1.0 | 110 | 0.1766 |
| 0.1574 | 1.09 | 120 | 0.1720 |
| 0.1582 | 1.18 | 130 | 0.1594 |
| 0.1537 | 1.27 | 140 | 0.1549 |
| 0.1536 | 1.36 | 150 | 0.1561 |
| 0.1549 | 1.45 | 160 | 0.1628 |
| 0.164 | 1.54 | 170 | 0.1702 |
| 0.1627 | 1.63 | 180 | 0.1708 |
| 0.1606 | 1.72 | 190 | 0.1599 |
| 0.1566 | 1.81 | 200 | 0.1533 |
| 0.1568 | 1.9 | 210 | 0.1590 |
| 0.1562 | 1.99 | 220 | 0.1597 |
| 0.1594 | 2.08 | 230 | 0.1685 |
| 0.1506 | 2.18 | 240 | 0.1606 |
| 0.1508 | 2.27 | 250 | 0.1572 |
| 0.151 | 2.36 | 260 | 0.1610 |
| 0.1514 | 2.45 | 270 | 0.1539 |
| 0.1467 | 2.54 | 280 | 0.1521 |
| 0.1483 | 2.63 | 290 | 0.1514 |
| 0.1465 | 2.72 | 300 | 0.1500 |
| 0.1477 | 2.81 | 310 | 0.1497 |
| 0.15 | 2.9 | 320 | 0.1495 |
| 0.1504 | 2.99 | 330 | 0.1495 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0424MADP8", "results": []}]} | Litzy619/V0424MADP8 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
]
| null | 2024-04-26T03:45:03+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-3-vi-sft-1
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0711
## Model description
Fine-tuning Phi-3-4b with Vietnamese Domain, VI_LIMA and VI_Alpaca Dataset
## 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: 4
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4031 | 0.17 | 40 | 1.2004 |
| 1.1508 | 0.34 | 80 | 1.1312 |
| 1.1055 | 0.51 | 120 | 1.1002 |
| 1.0814 | 0.67 | 160 | 1.0820 |
| 1.0735 | 0.84 | 200 | 1.0711 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "phi-3-vi-sft-1", "results": []}]} | mob2711/phi-3-vi-sft-1 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
]
| null | 2024-04-26T03:45:32+00:00 |
null | null | {} | sleepyraygun/CrispyC1 | null | [
"region:us"
]
| null | 2024-04-26T03:47:31+00:00 |
|
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-25_23-48-21 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T03:48:21+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# 0.001_4iters_bs128_declr_nodpo_useresponse_iter_3
This model is a fine-tuned version of [ShenaoZ/0.001_4iters_bs128_declr_nodpo_useresponse_iter_2](https://huggingface.co/ShenaoZ/0.001_4iters_bs128_declr_nodpo_useresponse_iter_2) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.001_4iters_bs128_declr_nodpo_useresponse_iter_2", "model-index": [{"name": "0.001_4iters_bs128_declr_nodpo_useresponse_iter_3", "results": []}]} | ShenaoZ/0.001_4iters_bs128_declr_nodpo_useresponse_iter_3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_4iters_bs128_declr_nodpo_useresponse_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T03:48:46+00:00 |
text-to-image | diffusers | {} | HoangDuyICT/stable-diffusion-inpainting-dsdress | null | [
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| null | 2024-04-26T03:49:00+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | kienlt/phi3-adapter | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:49:29+00:00 |
null | transformers |
# Tele-FLM
Tele-FLM (aka FLM-2) is a 52B open-sourced multilingual large language model that features a stable, efficient pre-training paradigm and enhanced factual judgement capabilities.
Built upon the decoder-only transformer architecture, it has been trained on approximately 2T tokens.
Tele-FLM demonstrates superior performances at its scale, and sometimes surpass larger models.
In addition to sharing the model weights, we provide the core designs, engineering practices, and training details, anticipating their benefits for both academic and industrial communities.
## Model Details
- **Developed by:** BAAI & TeleAI
- **Language(s):** English; Chinese; Other languages
- **License:** Apache 2.0
## Technical Report
[Tele-FLM Technical Report](https://arxiv.org/pdf/2404.16645)
## Bias, Risks, and Limitations
Although we've made extensive efforts to thoroughly clean and filter the training corpus for the model, due to the open nature of the dataset, the model may still have picked up on some unsafe examples. Consequently, the model may still generate unexpected content, including but not limited to discrimination, bias, or offensive language. We would like to strongly advise users not to spread any unsafe content generated by the model. The project developers cannot be held responsible for any repercussions stemming from the dissemination of harmful information.
## Quick Start
Use the code below to get started with Tele-FLM.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('CofeAI/Tele-FLM', trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained('CofeAI/Tele-FLM', torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True)
inputs = tokenizer('Beijing is the capital of China.', return_tensors='pt').to(model.device)
generated = model.generate(**inputs, max_new_tokens=128, repetition_penalty=1.03)
print(tokenizer.decode(generated.cpu()[0], skip_special_tokens=True))
```
## Training Details
### Training Data
Our training dataset comprises a variety of domains, as detailed in the table below.
The total amount of data is roughly 2 trillion, with English and Chinese data in a ratio of about 2:1.
In line with the methodology of GPT-4, we collected some instruct data and incorporated it into our pre-training data after removing the test sets of common datasets using the strict n-gram-based method. We deliberately avoid “training on the test set” or any other benchmark-oriented trick.
|Domain |Language|Sampling Prop. |Epochs |Disk Size |
|-------|:--------------:|:--------------:|:-------:|:-----------:|
| Webtext |en, zh | 75.21% | 1.0 | 5.9 TB |
| Code |code, zh | 9.81% | 1.0 | 528.1 GB |
| Book |en, zh | 7.17% | 0.8 | 647.6 GB |
| WorldKnowledge |multi, en, zh | 2.87% | 2.5 | 67.5 GB |
| QA |en, zh | 2.12% | 1.0 | 159.2 GB |
| AcademicPaper |en | 0.99% | 1.0 | 54.4 GB |
| Profession-Law |zh | 1.04% | 1.0 | 84.2 GB |
| Profession-Math |math | 0.62% | 2.0 | 6.1 GB |
| Profession-Patent |zh | 0.14% | 1.0 | 10.4 GB |
| Profession-Medical |zh | 0.02% | 1.0 | 1.2 GB |
| ClassicalChinese |zh | 0.02% | 2.5 | 0.5 GB |
### Model Architecture
We adopt the architecture of FLM-101B as the backbone for Tele-FLM, with several modifications:
- Rotary Positional Embedding (RoPE)
- RMSNorm for normalization
- SwiGLU for activation function
- Linear bias disabled
- Embedding and language model head untied
Consequently, Tele-FLM is largely compatible with Llama architecturally.
To maximize convenience for the community, we made minimal adjustments to Llama's code to adapt it to Tele-FLM and released it as open source.
In the pre-training stage, we employ μP for optimal hyperparameter search. The μP model (Tele-FLM_μP) is architecturally identical to Tele-FLM except for the model width.
The architecture of Tele-FLM and Tele-FLM_μP is listed below.
For more details of μP, please refer to our technical report and the original Tensor Program papers.
| Models | layer<br>number | attention<br>heads| hidden<br>size | ffn hidden<br>size| vocab<br>size | context<br>length | param size<br>(M) |
|--------|--------------|----------------|-------------|----------------|------------|----------------|----------------|
| Tele-FLM | 64 | 64 | 8,192 | 21,824 | 80,000 | 4,096 | 52,850 |
| Tele-FLM_μP | 64 | 4 | 512 | 1,344 | 80,000 | 4,096 | 283 |
### Training Hyperparameters
Due to the smaller size, Tele-FLM_μP allows for significantly more experimental runs within fixed time and resource constraints.
We searched seven hyperparameters for pretraining. All the hyperparameters are shown below.
| Searched Hyperparameters ||| Non-Searched Hyperparameters ||
|--------------------------------------------|-|-|-|----------------------------------|
| Learning Rate | 1.5e-4 || LR Schedule Type | cosine |
| Matrix Learning Rate | 1.5e-4 || LR Schedule (tokens) | 2.5T |
| Minimum Learning Rate | 1.5e-5 || Warmup Step | 2,000 |
| Standard Deviation | 4e-3 || Clip Grad | 1.0 |
| Matrix Standard Deviation | 4.242e-3 || Weight Decay | 0.0 |
| Input Mult | 1.0 || Batch Size (tokens) | 5,505,024 |
| Output Mult | 3.125e-2 || RoPE Theta | 10,000 |
### Training Loss
<p align="center" width="100%">
<a><img src="figures/train_loss.png" alt="nexa-octopus" style="width: 90%; min-width: 500px; display: block; margin: auto;"></a>
</p>
#### Hardware
Tele-FLM is trained on a cluster of 112 A800 SXM4 GPU servers, each with 8 NVLink A800 GPUs and 2TB of RAM.
The nodes have varied CPU configurations: 96 nodes with Intel 8358 (128x 2.60GHz) CPUs and 16 nodes with AMD 7643 (96x 2.30GHz) CPUs.
All nodes are interconnected via InfiniBand (IB). The training process lasted around two months, including downtime due to unexpected factors.
#### Software
Tele-FLM utilizes 3D parallel training, combining the prevailing methodologies: data parallelism, tensor parallelism, and pipeline parallelism.
The parallel training setup for Tele-FLM is configured as follows: tensor parallel=4, pipeline parallel=2, and data parallel=112.
## Evaluation
### English
#### Open LLM Leaderboard
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | WinoGrade | GSM8K | HumanEval | BBH |
|------------|:-------:|:-------:|:---------:|:------:|:----------:|:---------:|:------:|:---------:|:------:|
| | | 25-shot | 10-shot | 5-shot | zero-shot | 5-shot | 5-shot | zero-shot | 3-shot |
| LLAMA2-70B | 63.39 | 67.32 | 87.33 | 69.83 | 44.92 | 83.74 | 54.06 | 46.95 | 52.94 |
| LLAMA2-13B | 50.29 | 59.39 | 82.13 | 55.77 | 37.38 | 76.64 | 22.82 | 28.66 | 39.52 |
| LLAMA-65B | 56.98 | 63.48 | 86.09 | 63.93 | 43.43 | 82.56 | 37.23 | 33.54 | 45.54 |
| LLAMA-13B | 46.20 | 56.23 | 80.93 | 47.67 | 39.48 | 76.24 | 7.58 | 23.78 | 37.72 |
| Tele-FLM | 56.60 | 59.47 | 82.25 | 64.00 | 43.09 | 79.40 | 45.19 | 34.76 | 44.60 |
### Chinese
#### OpenCompass
| Model | Average | C-Eval | CMMLU | C3 | CHID | CSL |
|--------------|:-------:|:------:|:-----:|:-----:|:-----:|:-----:|
| GPT-4 | 76.64 | 69.90 | 71.00 | 95.10 | 82.20 | 65.00 |
| GPT-3.5 | 61.86 | 52.50 | 53.90 | 85.60 | 60.40 | 56.90 |
| Qwen1.5-72B | 80.45 | 83.72 | 83.09 | 81.86 | 91.09 | 62.50 |
| Qwen-72B | 83.00 | 83.30 | 83.60 | 95.80 | 91.10 | 61.20 |
| DeepSeek-67B | 73.46 | 66.90 | 70.40 | 77.80 | 89.10 | 63.10 |
| Tele-FLM | 71.13 | 65.48 | 66.98 | 66.25 | 92.57 | 64.38 |
## Citation
If you find our work helpful, please consider citing it.
```
@misc{li2024teleflm,
title={Tele-FLM Technical Report},
author={Xiang Li and Yiqun Yao and Xin Jiang and Xuezhi Fang and Chao Wang and Xinzhang Liu and Zihan Wang and Yu Zhao and Xin Wang and Yuyao Huang and Shuangyong Song and Yongxiang Li and Zheng Zhang and Bo Zhao and Aixin Sun and Yequan Wang and Zhongjiang He and Zhongyuan Wang and Xuelong Li and Tiejun Huang},
year={2024},
eprint={2404.16645},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| {"license": "apache-2.0"} | CofeAI/Tele-FLM | null | [
"transformers",
"pytorch",
"TeleFLM",
"custom_code",
"arxiv:2404.16645",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:52:05+00:00 |
image-classification | transformers |
<!-- 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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5969
- Accuracy: 0.883
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6661 | 0.992 | 62 | 2.4959 | 0.802 |
| 1.784 | 2.0 | 125 | 1.7748 | 0.849 |
| 1.56 | 2.976 | 186 | 1.5969 | 0.883 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "my_awesome_food_model", "results": []}]} | diegozambrana/my_awesome_food_model | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:52:37+00:00 |
text-classification | transformers | {} | samuelcolvin26/Albert_Hatespeech_Classifier1 | null | [
"transformers",
"safetensors",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:53:01+00:00 |
|
text-classification | transformers | {} | samuelcolvin26/Albert_Hatespeech_Classifier3 | null | [
"transformers",
"safetensors",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:53:07+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Kimty/sql_coder_text1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T03:53:40+00:00 |
token-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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| {"library_name": "transformers", "tags": []} | wizardofchance/ner-model | null | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:54:12+00:00 |
text-generation | transformers | Quantizations of https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
# From original readme
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4. | {"language": ["en"], "license": "other", "tags": ["gguf", "imatrix", "mistralai", "Mistral-7B-Instruct-v0.2", "transformers"], "inference": false, "pipeline_tag": "text-generation"} | duyntnet/Mistral-7B-Instruct-v0.2-imatrix-GGUF | null | [
"transformers",
"gguf",
"imatrix",
"mistralai",
"Mistral-7B-Instruct-v0.2",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-26T03:54:16+00:00 |
text-classification | transformers | {} | samuelcolvin26/Albert_Hatespeech_Classifier5 | null | [
"transformers",
"safetensors",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:54:31+00:00 |
|
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [deepnet/SN6-67L2](https://huggingface.co/deepnet/SN6-67L2)
* [Grayx/sad_llama_38](https://huggingface.co/Grayx/sad_llama_38)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Grayx/sad_llama_38
layer_range: [0, 32]
- model: deepnet/SN6-67L2
layer_range: [0, 32]
merge_method: slerp
base_model: deepnet/SN6-67L2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["deepnet/SN6-67L2", "Grayx/sad_llama_38"]} | Sumail/Chalice5 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:deepnet/SN6-67L2",
"base_model:Grayx/sad_llama_38",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T03:56:10+00:00 |
null | null | {} | pimpalgaonkar/None | null | [
"region:us"
]
| null | 2024-04-26T03:58:04+00:00 |
|
null | null | {} | shleee/lora-trained-xl | null | [
"region:us"
]
| null | 2024-04-26T03:58:23+00:00 |
|
text-classification | transformers |
<!-- 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. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2323
- Accuracy: 0.9318
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2244 | 1.0 | 1563 | 0.2064 | 0.9207 |
| 0.1388 | 2.0 | 3126 | 0.2323 | 0.9318 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_model", "results": []}]} | GauravR12060102/my_awesome_model | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T03:58:43+00:00 |
null | null | {} | Alsebay/Narumashi-11B-v0.9-GGUF | null | [
"gguf",
"region:us"
]
| null | 2024-04-26T03:58:50+00:00 |
|
null | null | {} | ashishp-wiai/Rice_LoRA_20-2024-04-26 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T03:59:39+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Konthee/CLIP-ViT-B-32-laion2B-s34B-b79K-vision | null | [
"transformers",
"safetensors",
"clip_vision_model",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:01:17+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K79me3-seqsight_4096_512_27M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4279
- F1 Score: 0.8139
- Accuracy: 0.8138
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5098 | 1.1 | 200 | 0.4538 | 0.8038 | 0.8041 |
| 0.4604 | 2.21 | 400 | 0.4530 | 0.8020 | 0.8034 |
| 0.4528 | 3.31 | 600 | 0.4464 | 0.8029 | 0.8037 |
| 0.4435 | 4.42 | 800 | 0.4432 | 0.8038 | 0.8044 |
| 0.4446 | 5.52 | 1000 | 0.4466 | 0.7996 | 0.8010 |
| 0.4345 | 6.63 | 1200 | 0.4486 | 0.7991 | 0.8006 |
| 0.4376 | 7.73 | 1400 | 0.4363 | 0.8081 | 0.8086 |
| 0.4313 | 8.84 | 1600 | 0.4415 | 0.8060 | 0.8072 |
| 0.4265 | 9.94 | 1800 | 0.4367 | 0.8185 | 0.8183 |
| 0.4259 | 11.05 | 2000 | 0.4400 | 0.8052 | 0.8058 |
| 0.4239 | 12.15 | 2200 | 0.4325 | 0.8162 | 0.8166 |
| 0.4198 | 13.26 | 2400 | 0.4297 | 0.8162 | 0.8166 |
| 0.4173 | 14.36 | 2600 | 0.4307 | 0.8166 | 0.8169 |
| 0.4192 | 15.47 | 2800 | 0.4329 | 0.8109 | 0.8117 |
| 0.4144 | 16.57 | 3000 | 0.4330 | 0.8121 | 0.8121 |
| 0.4164 | 17.68 | 3200 | 0.4293 | 0.8167 | 0.8169 |
| 0.4125 | 18.78 | 3400 | 0.4279 | 0.8172 | 0.8173 |
| 0.4113 | 19.89 | 3600 | 0.4295 | 0.8115 | 0.8121 |
| 0.4112 | 20.99 | 3800 | 0.4327 | 0.8092 | 0.8100 |
| 0.4094 | 22.1 | 4000 | 0.4262 | 0.8119 | 0.8128 |
| 0.4058 | 23.2 | 4200 | 0.4309 | 0.8098 | 0.8103 |
| 0.4076 | 24.31 | 4400 | 0.4391 | 0.8061 | 0.8076 |
| 0.4034 | 25.41 | 4600 | 0.4311 | 0.8127 | 0.8135 |
| 0.4106 | 26.52 | 4800 | 0.4284 | 0.8120 | 0.8124 |
| 0.4079 | 27.62 | 5000 | 0.4286 | 0.8128 | 0.8131 |
| 0.4009 | 28.73 | 5200 | 0.4283 | 0.8111 | 0.8117 |
| 0.3993 | 29.83 | 5400 | 0.4284 | 0.8108 | 0.8114 |
| 0.4014 | 30.94 | 5600 | 0.4293 | 0.8153 | 0.8155 |
| 0.4012 | 32.04 | 5800 | 0.4292 | 0.8117 | 0.8121 |
| 0.4004 | 33.15 | 6000 | 0.4257 | 0.8123 | 0.8128 |
| 0.4003 | 34.25 | 6200 | 0.4321 | 0.8090 | 0.8093 |
| 0.3978 | 35.36 | 6400 | 0.4317 | 0.8111 | 0.8117 |
| 0.3994 | 36.46 | 6600 | 0.4295 | 0.8118 | 0.8121 |
| 0.3972 | 37.57 | 6800 | 0.4297 | 0.8103 | 0.8107 |
| 0.395 | 38.67 | 7000 | 0.4299 | 0.8108 | 0.8114 |
| 0.3958 | 39.78 | 7200 | 0.4303 | 0.8120 | 0.8124 |
| 0.3987 | 40.88 | 7400 | 0.4303 | 0.8087 | 0.8089 |
| 0.3967 | 41.99 | 7600 | 0.4304 | 0.8110 | 0.8114 |
| 0.3957 | 43.09 | 7800 | 0.4338 | 0.8112 | 0.8117 |
| 0.3971 | 44.2 | 8000 | 0.4321 | 0.8112 | 0.8117 |
| 0.394 | 45.3 | 8200 | 0.4298 | 0.8103 | 0.8107 |
| 0.3913 | 46.41 | 8400 | 0.4328 | 0.8106 | 0.8110 |
| 0.3982 | 47.51 | 8600 | 0.4315 | 0.8118 | 0.8124 |
| 0.3907 | 48.62 | 8800 | 0.4329 | 0.8101 | 0.8107 |
| 0.3912 | 49.72 | 9000 | 0.4319 | 0.8106 | 0.8110 |
| 0.3965 | 50.83 | 9200 | 0.4323 | 0.8103 | 0.8110 |
| 0.3941 | 51.93 | 9400 | 0.4317 | 0.8102 | 0.8107 |
| 0.3929 | 53.04 | 9600 | 0.4313 | 0.8113 | 0.8117 |
| 0.3929 | 54.14 | 9800 | 0.4314 | 0.8116 | 0.8121 |
| 0.392 | 55.25 | 10000 | 0.4319 | 0.8116 | 0.8121 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_27M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_4096_512_27M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_4096_512_27M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T04:02:35+00:00 |
text-classification | transformers |
<!-- 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. -->
# robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-1
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-1", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-1 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T04:03:04+00:00 |
null | null | {} | sm09-dev/CyberRealistic_Negative | null | [
"region:us"
]
| null | 2024-04-26T04:03:20+00:00 |
|
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-26_00-04-13 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T04:04:13+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | swaghjal/codellama-7b-finetuned-checkpoints_2024-04-25_21_35_24 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T04:04:20+00:00 |
text-generation | null |
## Exllama v2 Quantizations of Llama-3-8B-LexiFun-Uncensored-V1
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.19">turboderp's ExLlamaV2 v0.0.19</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|end_of_text|><|start_header_id|>user<|end_header_id|>
{prompt}<|end_of_text|><|start_header_id|>assistant<|end_header_id|>
```
## Available sizes
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2/tree/8_0) | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2/tree/6_5) | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2/tree/5_0) | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2/tree/4_25) | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2 Llama-3-8B-LexiFun-Uncensored-V1-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
Linux:
```shell
huggingface-cli download bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2 --revision 6_5 --local-dir Llama-3-8B-LexiFun-Uncensored-V1-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
huggingface-cli download bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2 --revision 6_5 --local-dir Llama-3-8B-LexiFun-Uncensored-V1-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"language": ["en"], "license": "other", "tags": ["llama3", "comedy", "comedian", "fun", "funny", "llama38b", "laugh", "sarcasm", "roleplay"], "license_name": "llama3", "license_link": "https://llama.meta.com/llama3/license/", "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | bartowski/Llama-3-8B-LexiFun-Uncensored-V1-exl2 | null | [
"llama3",
"comedy",
"comedian",
"fun",
"funny",
"llama38b",
"laugh",
"sarcasm",
"roleplay",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-26T04:05:13+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K79me3-seqsight_4096_512_27M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4205
- F1 Score: 0.8258
- Accuracy: 0.8263
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.4903 | 1.1 | 200 | 0.4461 | 0.8041 | 0.8041 |
| 0.4483 | 2.21 | 400 | 0.4354 | 0.8082 | 0.8086 |
| 0.4329 | 3.31 | 600 | 0.4345 | 0.8064 | 0.8076 |
| 0.4176 | 4.42 | 800 | 0.4359 | 0.8040 | 0.8048 |
| 0.4117 | 5.52 | 1000 | 0.4214 | 0.8127 | 0.8135 |
| 0.3958 | 6.63 | 1200 | 0.4484 | 0.8132 | 0.8145 |
| 0.393 | 7.73 | 1400 | 0.4312 | 0.8126 | 0.8138 |
| 0.3828 | 8.84 | 1600 | 0.4639 | 0.7972 | 0.8003 |
| 0.371 | 9.94 | 1800 | 0.4305 | 0.8165 | 0.8162 |
| 0.3708 | 11.05 | 2000 | 0.4365 | 0.8130 | 0.8131 |
| 0.3617 | 12.15 | 2200 | 0.4318 | 0.8192 | 0.8197 |
| 0.3502 | 13.26 | 2400 | 0.4358 | 0.8134 | 0.8135 |
| 0.3439 | 14.36 | 2600 | 0.4468 | 0.8128 | 0.8128 |
| 0.3383 | 15.47 | 2800 | 0.4440 | 0.8139 | 0.8141 |
| 0.3271 | 16.57 | 3000 | 0.4486 | 0.8114 | 0.8114 |
| 0.3247 | 17.68 | 3200 | 0.4608 | 0.8111 | 0.8110 |
| 0.3136 | 18.78 | 3400 | 0.4701 | 0.8103 | 0.8110 |
| 0.3074 | 19.89 | 3600 | 0.4652 | 0.8148 | 0.8148 |
| 0.3015 | 20.99 | 3800 | 0.4878 | 0.8000 | 0.8003 |
| 0.2918 | 22.1 | 4000 | 0.4804 | 0.8146 | 0.8145 |
| 0.2837 | 23.2 | 4200 | 0.5077 | 0.8064 | 0.8065 |
| 0.2816 | 24.31 | 4400 | 0.5156 | 0.8027 | 0.8031 |
| 0.2704 | 25.41 | 4600 | 0.5192 | 0.8068 | 0.8065 |
| 0.2698 | 26.52 | 4800 | 0.5292 | 0.7983 | 0.7989 |
| 0.261 | 27.62 | 5000 | 0.5160 | 0.8090 | 0.8089 |
| 0.2526 | 28.73 | 5200 | 0.5439 | 0.7999 | 0.8006 |
| 0.2462 | 29.83 | 5400 | 0.5390 | 0.8005 | 0.8003 |
| 0.2383 | 30.94 | 5600 | 0.5546 | 0.7981 | 0.7979 |
| 0.2385 | 32.04 | 5800 | 0.5410 | 0.8101 | 0.8100 |
| 0.2294 | 33.15 | 6000 | 0.5604 | 0.8060 | 0.8058 |
| 0.2271 | 34.25 | 6200 | 0.5837 | 0.7992 | 0.7989 |
| 0.2224 | 35.36 | 6400 | 0.5977 | 0.8081 | 0.8083 |
| 0.2205 | 36.46 | 6600 | 0.5873 | 0.7945 | 0.7947 |
| 0.2136 | 37.57 | 6800 | 0.6090 | 0.7973 | 0.7972 |
| 0.2087 | 38.67 | 7000 | 0.6072 | 0.7973 | 0.7975 |
| 0.2129 | 39.78 | 7200 | 0.6049 | 0.8046 | 0.8048 |
| 0.2056 | 40.88 | 7400 | 0.5983 | 0.8021 | 0.8020 |
| 0.2005 | 41.99 | 7600 | 0.6171 | 0.7957 | 0.7954 |
| 0.1956 | 43.09 | 7800 | 0.6335 | 0.7901 | 0.7902 |
| 0.1922 | 44.2 | 8000 | 0.6440 | 0.8001 | 0.7999 |
| 0.1936 | 45.3 | 8200 | 0.6359 | 0.8037 | 0.8034 |
| 0.188 | 46.41 | 8400 | 0.6422 | 0.8028 | 0.8027 |
| 0.1882 | 47.51 | 8600 | 0.6427 | 0.8032 | 0.8031 |
| 0.1852 | 48.62 | 8800 | 0.6483 | 0.7961 | 0.7961 |
| 0.1798 | 49.72 | 9000 | 0.6616 | 0.7990 | 0.7989 |
| 0.1817 | 50.83 | 9200 | 0.6518 | 0.7961 | 0.7961 |
| 0.1798 | 51.93 | 9400 | 0.6595 | 0.7973 | 0.7972 |
| 0.18 | 53.04 | 9600 | 0.6562 | 0.7952 | 0.7951 |
| 0.1761 | 54.14 | 9800 | 0.6645 | 0.7966 | 0.7965 |
| 0.1773 | 55.25 | 10000 | 0.6662 | 0.7979 | 0.7979 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_27M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_4096_512_27M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_4096_512_27M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T04:06:19+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K79me3-seqsight_4096_512_27M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4263
- F1 Score: 0.8207
- Accuracy: 0.8211
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.4965 | 1.1 | 200 | 0.4496 | 0.8060 | 0.8062 |
| 0.4536 | 2.21 | 400 | 0.4463 | 0.8038 | 0.8048 |
| 0.4423 | 3.31 | 600 | 0.4413 | 0.8046 | 0.8058 |
| 0.4305 | 4.42 | 800 | 0.4388 | 0.7987 | 0.7992 |
| 0.4286 | 5.52 | 1000 | 0.4331 | 0.8073 | 0.8083 |
| 0.4162 | 6.63 | 1200 | 0.4401 | 0.8061 | 0.8076 |
| 0.4162 | 7.73 | 1400 | 0.4291 | 0.8049 | 0.8058 |
| 0.4082 | 8.84 | 1600 | 0.4426 | 0.8000 | 0.8020 |
| 0.4008 | 9.94 | 1800 | 0.4301 | 0.8147 | 0.8145 |
| 0.4007 | 11.05 | 2000 | 0.4379 | 0.8099 | 0.8100 |
| 0.3953 | 12.15 | 2200 | 0.4261 | 0.8158 | 0.8159 |
| 0.3894 | 13.26 | 2400 | 0.4265 | 0.8148 | 0.8148 |
| 0.3869 | 14.36 | 2600 | 0.4267 | 0.8105 | 0.8107 |
| 0.3858 | 15.47 | 2800 | 0.4267 | 0.8147 | 0.8152 |
| 0.3799 | 16.57 | 3000 | 0.4318 | 0.8112 | 0.8110 |
| 0.3817 | 17.68 | 3200 | 0.4248 | 0.8155 | 0.8155 |
| 0.3741 | 18.78 | 3400 | 0.4306 | 0.8112 | 0.8121 |
| 0.3737 | 19.89 | 3600 | 0.4263 | 0.8159 | 0.8159 |
| 0.3697 | 20.99 | 3800 | 0.4367 | 0.8015 | 0.8024 |
| 0.3662 | 22.1 | 4000 | 0.4306 | 0.8110 | 0.8114 |
| 0.3653 | 23.2 | 4200 | 0.4324 | 0.8133 | 0.8135 |
| 0.3658 | 24.31 | 4400 | 0.4429 | 0.8106 | 0.8121 |
| 0.3582 | 25.41 | 4600 | 0.4325 | 0.8157 | 0.8159 |
| 0.3626 | 26.52 | 4800 | 0.4349 | 0.8109 | 0.8110 |
| 0.3574 | 27.62 | 5000 | 0.4304 | 0.8124 | 0.8128 |
| 0.3511 | 28.73 | 5200 | 0.4373 | 0.8093 | 0.8100 |
| 0.3475 | 29.83 | 5400 | 0.4313 | 0.8145 | 0.8148 |
| 0.3488 | 30.94 | 5600 | 0.4299 | 0.8147 | 0.8148 |
| 0.3453 | 32.04 | 5800 | 0.4340 | 0.8167 | 0.8169 |
| 0.3434 | 33.15 | 6000 | 0.4302 | 0.8168 | 0.8173 |
| 0.34 | 34.25 | 6200 | 0.4411 | 0.8099 | 0.8100 |
| 0.3423 | 35.36 | 6400 | 0.4394 | 0.8197 | 0.8200 |
| 0.341 | 36.46 | 6600 | 0.4344 | 0.8157 | 0.8159 |
| 0.3375 | 37.57 | 6800 | 0.4383 | 0.8133 | 0.8135 |
| 0.3324 | 38.67 | 7000 | 0.4450 | 0.8147 | 0.8152 |
| 0.3368 | 39.78 | 7200 | 0.4387 | 0.8175 | 0.8180 |
| 0.3357 | 40.88 | 7400 | 0.4381 | 0.8138 | 0.8138 |
| 0.3331 | 41.99 | 7600 | 0.4406 | 0.8136 | 0.8138 |
| 0.3306 | 43.09 | 7800 | 0.4444 | 0.8191 | 0.8193 |
| 0.3298 | 44.2 | 8000 | 0.4507 | 0.8119 | 0.8124 |
| 0.3296 | 45.3 | 8200 | 0.4415 | 0.8137 | 0.8138 |
| 0.3247 | 46.41 | 8400 | 0.4462 | 0.8156 | 0.8159 |
| 0.3271 | 47.51 | 8600 | 0.4477 | 0.8139 | 0.8145 |
| 0.3236 | 48.62 | 8800 | 0.4467 | 0.8123 | 0.8128 |
| 0.3206 | 49.72 | 9000 | 0.4496 | 0.8122 | 0.8124 |
| 0.328 | 50.83 | 9200 | 0.4462 | 0.8129 | 0.8135 |
| 0.3241 | 51.93 | 9400 | 0.4464 | 0.8137 | 0.8141 |
| 0.3241 | 53.04 | 9600 | 0.4459 | 0.8118 | 0.8121 |
| 0.323 | 54.14 | 9800 | 0.4468 | 0.8122 | 0.8124 |
| 0.3212 | 55.25 | 10000 | 0.4480 | 0.8121 | 0.8124 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_27M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_4096_512_27M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_4096_512_27M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T04:06:19+00:00 |
text-generation | transformers |
# Model Card for Model ID
Fine-tuning for CS5242 project
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [DreamOnRain]
- **Finetuned from model [optional]:** state-spaces/mamba-130m-hf
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/DreamOnRain/Deep-Learning-Final-Project
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k
| {"library_name": "transformers", "tags": []} | DreamOnRain/mamba-130m-msmath | null | [
"transformers",
"safetensors",
"mamba",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:07:15+00:00 |
text-generation | transformers |
## 4-bit GEMM AWQ Quantizations of Llama-3-8B-LexiFun-Uncensored-V1
Using <a href="https://github.com/casper-hansen/AutoAWQ/">AutoAWQ</a> release <a href="https://github.com/casper-hansen/AutoAWQ/releases/tag/v0.2.4">v0.2.4</a> for quantization.
Original model: https://huggingface.co/Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|end_of_text|><|start_header_id|>user<|end_header_id|>
{prompt}<|end_of_text|><|start_header_id|>assistant<|end_header_id|>
```
## AWQ Parameters
- q_group_size: 128
- w_bit: 4
- zero_point: True
- version: GEMM
## How to run
From the AutoAWQ repo [here](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py)
First install autoawq pypi package:
```
pip install autoawq
```
Then run the following:
```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
quant_path = "models/Llama-3-8B-LexiFun-Uncensored-V1-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
chat = [
{"role": "system", "content": "You are a concise assistant that helps answer questions."},
{"role": "user", "content": prompt},
]
# <|eot_id|> used for llama 3 models
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
tokens = tokenizer.apply_chat_template(
chat,
return_tensors="pt"
).cuda()
# Generate output
generation_output = model.generate(
tokens,
streamer=streamer,
max_new_tokens=64,
eos_token_id=terminators
)
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"language": ["en"], "license": "other", "tags": ["llama3", "comedy", "comedian", "fun", "funny", "llama38b", "laugh", "sarcasm", "roleplay"], "license_name": "llama3", "license_link": "https://llama.meta.com/llama3/license/", "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | bartowski/Llama-3-8B-LexiFun-Uncensored-V1-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama3",
"comedy",
"comedian",
"fun",
"funny",
"llama38b",
"laugh",
"sarcasm",
"roleplay",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-26T04:07:26+00:00 |
null | null | {} | vup2p/model_sn25_130 | null | [
"region:us"
]
| null | 2024-04-26T04:07:40+00:00 |
|
null | null | {"license": "bigcode-openrail-m"} | immErfanrajabee/imme | null | [
"license:bigcode-openrail-m",
"region:us"
]
| null | 2024-04-26T04:08:27+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | mcding/GPT2-Small-PKU-Help-10K-Reward | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T04:10:21+00:00 |
automatic-speech-recognition | transformers | {} | xeon0618/indic_gujarati_sanscript | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:10:47+00:00 |
|
image-feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Konthee/siglip-so400m-patch14-384-vision | null | [
"transformers",
"safetensors",
"siglip_vision_model",
"image-feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:11:04+00:00 |
null | null | {} | sm09-dev/epiCNegative | null | [
"region:us"
]
| null | 2024-04-26T04:11:28+00:00 |
|
null | null | {} | vup2p/model_sn25_134 | null | [
"region:us"
]
| null | 2024-04-26T04:13:38+00:00 |
|
null | null | {} | vup2p/model_sn25_49 | null | [
"region:us"
]
| null | 2024-04-26T04:13:46+00:00 |
|
null | null | {"license": "apache-2.0"} | JarvanLee/yolov8-helmet-violation-detection | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T04:14:01+00:00 |
|
null | null | {} | sm09-dev/epiCPhotoGasm-colorfulPhoto-neg | null | [
"region:us"
]
| null | 2024-04-26T04:14:04+00:00 |
|
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Grayx/sad_llama_38](https://huggingface.co/Grayx/sad_llama_38)
* [deepnet/SN6-67L2](https://huggingface.co/deepnet/SN6-67L2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Grayx/sad_llama_38
layer_range: [0, 32]
- model: deepnet/SN6-67L2
layer_range: [0, 32]
merge_method: slerp
base_model: deepnet/SN6-67L2
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: 0.3
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Grayx/sad_llama_38", "deepnet/SN6-67L2"]} | Sumail/Chalice6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Grayx/sad_llama_38",
"base_model:deepnet/SN6-67L2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T04:14:46+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl", "sft"], "base_model": "unsloth/gemma-7b-bnb-4bit"} | 1024m/GEMMA7B-01-EXALT1A-16bit | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:15:14+00:00 |
null | peft |
<!-- 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. -->
# gemma_7b_lora_completion_only
This model is a fine-tuned version of [google/gemma-1.1-7b-it](https://huggingface.co/google/gemma-1.1-7b-it) on the DandinPower/ZH-Reading-Comprehension-gemma-it dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0885
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1172 | 0.3690 | 250 | 0.0932 |
| 0.1059 | 0.7380 | 500 | 0.0997 |
| 0.0913 | 1.1070 | 750 | 0.1225 |
| 0.074 | 1.4760 | 1000 | 0.1046 |
| 0.0619 | 1.8450 | 1250 | 0.1084 |
| 0.0375 | 2.2140 | 1500 | 0.1038 |
| 0.0128 | 2.5830 | 1750 | 0.0993 |
| 0.044 | 2.9520 | 2000 | 0.0885 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"language": ["zh"], "license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer"], "datasets": ["DandinPower/ZH-Reading-Comprehension-gemma-it"], "base_model": "google/gemma-1.1-7b-it", "model-index": [{"name": "gemma_7b_lora_completion_only", "results": []}]} | DandinPower/gemma_7b_lora_completion_only | null | [
"peft",
"safetensors",
"trl",
"sft",
"nycu-112-2-deeplearning-hw2",
"generated_from_trainer",
"zh",
"dataset:DandinPower/ZH-Reading-Comprehension-gemma-it",
"base_model:google/gemma-1.1-7b-it",
"license:gemma",
"region:us"
]
| null | 2024-04-26T04:15:43+00:00 |
null | null | {} | JayPandaAI/qwen1.5-llm | null | [
"gguf",
"region:us"
]
| null | 2024-04-26T04:16:11+00:00 |
|
text-generation | transformers |
# Phi-3 Mini-128K-Instruct ONNX models
<!-- Provide a quick summary of what the model is/does. -->
This repository hosts the optimized versions of [Phi-3-mini-128k-instruct](https://aka.ms/phi3-mini-128k-instruct) to accelerate inference with ONNX Runtime.
Phi-3 Mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-2 - synthetic data and filtered websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family, and the mini version comes in two variants: 4K and 128K which is the context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.
Optimized Phi-3 Mini models are published here in [ONNX](https://onnx.ai) format to run with [ONNX Runtime](https://onnxruntime.ai/) on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets.
[DirectML](https://aka.ms/directml) support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 Mini across a range of devices for CPU, GPU, and mobile.
To easily get started with Phi-3, you can use our newly introduced ONNX Runtime Generate() API. See [here](https://aka.ms/generate-tutorial) for instructions on how to run it.
## ONNX Models
Here are some of the optimized configurations we have added:
1. ONNX model for int4 DML: ONNX model for AMD, Intel, and NVIDIA GPUs on Windows, quantized to int4 using [AWQ](https://arxiv.org/abs/2306.00978).
2. ONNX model for fp16 CUDA: ONNX model you can use to run for your NVIDIA GPUs.
3. ONNX model for int4 CUDA: ONNX model for NVIDIA GPUs using int4 quantization via RTN.
4. ONNX model for int4 CPU and Mobile: ONNX model for your CPU and Mobile, using int4 quantization via RTN. There are two versions uploaded to balance latency vs. accuracy.
More updates on AMD, and additional optimizations on CPU and Mobile will be added with the official ORT 1.18 release in early May. Stay tuned!
## Hardware Supported
The models are tested on:
- GPU SKU: RTX 4090 (DirectML)
- GPU SKU: 1 A100 80GB GPU, SKU: Standard_ND96amsr_A100_v4 (CUDA)
- CPU SKU: Standard F64s v2 (64 vcpus, 128 GiB memory)
- Mobile SKU: Samsung Galaxy S21
Minimum Configuration Required:
- Windows: DirectX 12-capable GPU and a minimum of 4GB of combined RAM
- CUDA: Streaming Multiprocessors (SMs) >= 70 (i.e. V100 or newer)
### Model Description
- **Developed by:** Microsoft
- **Model type:** ONNX
- **Language(s) (NLP):** Python, C, C++
- **License:** MIT
- **Model Description:** This is a conversion of the Phi-3 Mini-4K-Instruct model for ONNX Runtime inference.
## Additional Details
- [**ONNX Runtime Optimizations Blog Link**](https://aka.ms/phi3-optimizations)
- [**Phi-3 Model Blog Link**](https://aka.ms/phi3blog-april)
- [**Phi-3 Model Card**]( https://aka.ms/phi3-mini-128k-instruct)
- [**Phi-3 Technical Report**](https://aka.ms/phi3-tech-report)
## How to Get Started with the Model
To make running of the Phi-3 models across a range of devices and platforms across various execution provider backends possible, we introduce a new API to wrap several aspects of generative AI inferencing. This API make it easy to drag and drop LLMs straight into your app. For running the early version of these models with ONNX Runtime, follow the steps [here](http://aka.ms/generate-tutorial).
For example:
```python
python model-qa.py -m /*{YourModelPath}*/onnx/cpu_and_mobile/phi-3-mini-4k-instruct-int4-cpu -k 40 -p 0.95 -t 0.8 -r 1.0
```
```
*Input:* <|user|>Tell me a joke<|end|><|assistant|>
*Output:* Why don't scientists trust atoms?
Because they make up everything!
This joke plays on the double meaning of "make up." In science, atoms are the fundamental building blocks of matter, literally making up everything. However, in a colloquial sense, "to make up" can mean to fabricate or lie, hence the humor.
```
## Performance Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
Phi-3 Mini-128K-Instruct performs better in ONNX Runtime than PyTorch for all batch size, prompt length combinations. For FP16 CUDA, ORT performs up to 5X faster than PyTorch, while with INT4 CUDA it's up to 9X faster than PyTorch.
The table below shows the average throughput of the first 256 tokens generated (tps) for FP16 and INT4 precisions on CUDA as measured on [1 A100 80GB GPU, SKU: Standard_ND96amsr_A100_v4](https://learn.microsoft.com/en-us/azure/virtual-machines/ndm-a100-v4-series).
| Batch Size, Prompt Length | ORT FP16 CUDA | PyTorch Eager FP16 CUDA | FP16 CUDA Speed Up (ORT/PyTorch) |
|---------------------------|---------------|-------------------------|----------------------------------|
| 1, 16 | 134.46 | 25.35 | 5.30 |
| 1, 64 | 132.21 | 25.69 | 5.15 |
| 1, 256 | 124.51 | 25.77 | 4.83 |
| 1, 1024 | 110.03 | 25.73 | 4.28 |
| 1, 2048 | 96.93 | 25.72 | 3.77 |
| 1, 4096 | 62.12 | 25.66 | 2.42 |
| 4, 16 | 521.10 | 101.31 | 5.14 |
| 4, 64 | 507.03 | 101.66 | 4.99 |
| 4, 256 | 459.47 | 101.15 | 4.54 |
| 4, 1024 | 343.60 | 101.09 | 3.40 |
| 4, 2048 | 264.81 | 100.78 | 2.63 |
| 4, 4096 | 158.00 | 77.98 | 2.03 |
| 16, 16 | 1689.08 | 394.19 | 4.28 |
| 16, 64 | 1567.13 | 394.29 | 3.97 |
| 16, 256 | 1232.10 | 405.30 | 3.04 |
| 16, 1024 | 680.61 | 294.79 | 2.31 |
| 16, 2048 | 350.77 | 203.02 | 1.73 |
| 16, 4096 | 192.36 | OOM | |
| Batch Size, Prompt Length | PyTorch Eager INT4 CUDA | INT4 CUDA Speed Up (ORT/PyTorch) |
|---------------------------|-------------------------|----------------------------------|
| 1, 16 | 25.35 | 8.89 |
| 1, 64 | 25.69 | 8.58 |
| 1, 256 | 25.77 | 7.69 |
| 1, 1024 | 25.73 | 6.34 |
| 1, 2048 | 25.72 | 5.24 |
| 1, 4096 | 25.66 | 2.97 |
| 4, 16 | 101.31 | 2.82 |
| 4, 64 | 101.66 | 2.77 |
| 4, 256 | 101.15 | 2.64 |
| 4, 1024 | 101.09 | 2.20 |
| 4, 2048 | 100.78 | 1.84 |
| 4, 4096 | 77.98 | 1.62 |
| 16, 16 | 394.19 | 2.52 |
| 16, 64 | 394.29 | 2.41 |
| 16, 256 | 405.30 | 2.00 |
| 16, 1024 | 294.79 | 1.79 |
| 16, 2048 | 203.02 | 1.81 |
| 16, 4096 | OOM | |
Note: PyTorch compile and Llama.cpp currently do not support the Phi-3 Mini-128K-Instruct model.
### Package Versions
| Pip package name | Version |
|----------------------------|----------|
| torch | 2.2.0 |
| triton | 2.2.0 |
| onnxruntime-gpu | 1.18.0 |
| onnxruntime-genai | 0.2.0rc3 |
| onnxruntime-genai-cuda | 0.2.0rc3 |
| onnxruntime-genai-directml | 0.2.0rc3 |
| transformers | 4.39.0 |
| bitsandbytes | 0.42.0 |
## Appendix
### Activation Aware Quantization
AWQ works by identifying the top 1% most salient weights that are most important for maintaining accuracy and quantizing the remaining 99% of weights. This leads to less accuracy loss from quantization compared to many other quantization techniques. For more on AWQ, see [here](https://arxiv.org/abs/2306.00978).
## Model Card Contact
parinitarahi, kvaishnavi, natke
## Contributors
Kunal Vaishnavi, Sunghoon Choi, Yufeng Li, Akshay Sonawane, Sheetal Arun Kadam, Rui Ren, Edward Chen, Scott McKay, Ryan Hill, Emma Ning, Natalie Kershaw, Parinita Rahi, Patrice Vignola, Chai Chaoweeraprasit, Logan Iyer, Vicente Rivera, Jacques Van Rhyn | {"license": "mit", "tags": ["ONNX", "DML", "ONNXRuntime", "phi3", "nlp", "conversational", "custom_code"], "pipeline_tag": "text-generation"} | renwoshin/Phi-3-mini-128k-instruct-onnx-tf | null | [
"transformers",
"onnx",
"phi",
"text-generation",
"ONNX",
"DML",
"ONNXRuntime",
"phi3",
"nlp",
"conversational",
"custom_code",
"arxiv:2306.00978",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T04:16:23+00:00 |
null | null | {} | LucaXYB/BabyLlama-58M-UW-Test | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T04:17:29+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** sebdg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | sebdg/llama3-8b-emotions | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:18:36+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K4me1-seqsight_4096_512_27M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_27M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_27M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5146
- F1 Score: 0.7675
- Accuracy: 0.7689
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6151 | 1.01 | 200 | 0.5799 | 0.7177 | 0.7194 |
| 0.5812 | 2.02 | 400 | 0.5598 | 0.7371 | 0.7383 |
| 0.5588 | 3.03 | 600 | 0.5453 | 0.7424 | 0.7437 |
| 0.5477 | 4.04 | 800 | 0.5350 | 0.7500 | 0.7513 |
| 0.5422 | 5.05 | 1000 | 0.5330 | 0.7539 | 0.7551 |
| 0.5363 | 6.06 | 1200 | 0.5341 | 0.7539 | 0.7560 |
| 0.5316 | 7.07 | 1400 | 0.5321 | 0.7568 | 0.7585 |
| 0.5309 | 8.08 | 1600 | 0.5323 | 0.7571 | 0.7592 |
| 0.5295 | 9.09 | 1800 | 0.5278 | 0.7580 | 0.7598 |
| 0.5264 | 10.1 | 2000 | 0.5260 | 0.7598 | 0.7610 |
| 0.5231 | 11.11 | 2200 | 0.5266 | 0.7563 | 0.7582 |
| 0.5225 | 12.12 | 2400 | 0.5282 | 0.7581 | 0.7595 |
| 0.5209 | 13.13 | 2600 | 0.5252 | 0.7580 | 0.7601 |
| 0.5209 | 14.14 | 2800 | 0.5254 | 0.7533 | 0.7557 |
| 0.5188 | 15.15 | 3000 | 0.5254 | 0.7558 | 0.7579 |
| 0.5167 | 16.16 | 3200 | 0.5245 | 0.7539 | 0.7566 |
| 0.5137 | 17.17 | 3400 | 0.5262 | 0.7540 | 0.7563 |
| 0.5191 | 18.18 | 3600 | 0.5215 | 0.7580 | 0.7592 |
| 0.5133 | 19.19 | 3800 | 0.5224 | 0.7531 | 0.7554 |
| 0.5145 | 20.2 | 4000 | 0.5217 | 0.7557 | 0.7573 |
| 0.5106 | 21.21 | 4200 | 0.5274 | 0.7490 | 0.7522 |
| 0.5131 | 22.22 | 4400 | 0.5238 | 0.7531 | 0.7554 |
| 0.5106 | 23.23 | 4600 | 0.5256 | 0.7479 | 0.7513 |
| 0.5152 | 24.24 | 4800 | 0.5199 | 0.7547 | 0.7569 |
| 0.5081 | 25.25 | 5000 | 0.5218 | 0.7585 | 0.7604 |
| 0.5126 | 26.26 | 5200 | 0.5214 | 0.7578 | 0.7592 |
| 0.5062 | 27.27 | 5400 | 0.5208 | 0.7597 | 0.7614 |
| 0.5071 | 28.28 | 5600 | 0.5230 | 0.7558 | 0.7573 |
| 0.5122 | 29.29 | 5800 | 0.5234 | 0.7545 | 0.7569 |
| 0.5059 | 30.3 | 6000 | 0.5222 | 0.7551 | 0.7569 |
| 0.5066 | 31.31 | 6200 | 0.5224 | 0.7558 | 0.7579 |
| 0.507 | 32.32 | 6400 | 0.5268 | 0.7494 | 0.7528 |
| 0.5059 | 33.33 | 6600 | 0.5240 | 0.7549 | 0.7576 |
| 0.5032 | 34.34 | 6800 | 0.5250 | 0.7490 | 0.7522 |
| 0.5002 | 35.35 | 7000 | 0.5219 | 0.7594 | 0.7610 |
| 0.5075 | 36.36 | 7200 | 0.5228 | 0.7526 | 0.7551 |
| 0.5031 | 37.37 | 7400 | 0.5225 | 0.7533 | 0.7554 |
| 0.5035 | 38.38 | 7600 | 0.5214 | 0.7573 | 0.7588 |
| 0.4995 | 39.39 | 7800 | 0.5226 | 0.7552 | 0.7576 |
| 0.5025 | 40.4 | 8000 | 0.5239 | 0.7549 | 0.7569 |
| 0.5015 | 41.41 | 8200 | 0.5232 | 0.7516 | 0.7544 |
| 0.5046 | 42.42 | 8400 | 0.5224 | 0.7538 | 0.7563 |
| 0.5001 | 43.43 | 8600 | 0.5223 | 0.7554 | 0.7576 |
| 0.5007 | 44.44 | 8800 | 0.5233 | 0.7535 | 0.7560 |
| 0.4984 | 45.45 | 9000 | 0.5224 | 0.7585 | 0.7604 |
| 0.5027 | 46.46 | 9200 | 0.5226 | 0.7562 | 0.7585 |
| 0.5017 | 47.47 | 9400 | 0.5231 | 0.7544 | 0.7569 |
| 0.4946 | 48.48 | 9600 | 0.5229 | 0.7567 | 0.7588 |
| 0.5073 | 49.49 | 9800 | 0.5226 | 0.7569 | 0.7592 |
| 0.4979 | 50.51 | 10000 | 0.5224 | 0.7560 | 0.7582 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_27M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_4096_512_27M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_4096_512_27M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T04:19:56+00:00 |
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-26_00-20-30 | null | [
"region:us"
]
| null | 2024-04-26T04:20:30+00:00 |
|
null | null | {} | ashishp-wiai/Rice_LoRA_30-2024-04-26 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T04:20:45+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl", "sft"], "base_model": "unsloth/gemma-7b-bnb-4bit"} | 1024m/GEMMA7B-01-EXALT1A-4bit | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
]
| null | 2024-04-26T04:21:14+00:00 |
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-26_00-21-25 | null | [
"region:us"
]
| null | 2024-04-26T04:21:25+00:00 |
|
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-26_00-22-08 | null | [
"region:us"
]
| null | 2024-04-26T04:22:08+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** sebdg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | sebdg/llama3-8b-emotions-lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:22:54+00:00 |
null | diffusers | {} | mrtuandao/pokemon-finetune | null | [
"diffusers",
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-04-26T04:22:57+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# 0.001_3iters_bs256_declr_nodpo_userresponse_iter_2
This model is a fine-tuned version of [ShenaoZ/0.001_3iters_bs256_declr_nodpo_userresponse_iter_1](https://huggingface.co/ShenaoZ/0.001_3iters_bs256_declr_nodpo_userresponse_iter_1) on the updated and the original datasets.
## 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: 4e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.001_3iters_bs256_declr_nodpo_userresponse_iter_1", "model-index": [{"name": "0.001_3iters_bs256_declr_nodpo_userresponse_iter_2", "results": []}]} | ShenaoZ/0.001_3iters_bs256_declr_nodpo_userresponse_iter_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_3iters_bs256_declr_nodpo_userresponse_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T04:24:33+00:00 |
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-26_00-24-59 | null | [
"region:us"
]
| null | 2024-04-26T04:24:59+00:00 |
|
null | peft |
<!-- 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. -->
# esm2_t130_150M-lora-classifier_2024-04-26_00-25-40
This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6470
- Accuracy: 0.8887
## 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: 0.0005701568055793089
- train_batch_size: 28
- eval_batch_size: 28
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7096 | 1.0 | 55 | 0.6718 | 0.6055 |
| 0.6769 | 2.0 | 110 | 0.6739 | 0.6055 |
| 0.579 | 3.0 | 165 | 0.6608 | 0.6484 |
| 0.5726 | 4.0 | 220 | 0.5777 | 0.7109 |
| 0.6381 | 5.0 | 275 | 0.5020 | 0.7676 |
| 0.183 | 6.0 | 330 | 0.3725 | 0.8320 |
| 0.3701 | 7.0 | 385 | 0.3508 | 0.8535 |
| 0.2147 | 8.0 | 440 | 0.3191 | 0.8711 |
| 0.1654 | 9.0 | 495 | 0.3036 | 0.875 |
| 0.1581 | 10.0 | 550 | 0.3761 | 0.8516 |
| 0.3459 | 11.0 | 605 | 0.3746 | 0.8594 |
| 0.3325 | 12.0 | 660 | 0.3025 | 0.8867 |
| 0.1237 | 13.0 | 715 | 0.2983 | 0.8770 |
| 0.5167 | 14.0 | 770 | 0.3044 | 0.8887 |
| 0.3541 | 15.0 | 825 | 0.2927 | 0.8906 |
| 0.0378 | 16.0 | 880 | 0.3669 | 0.8906 |
| 0.062 | 17.0 | 935 | 0.3298 | 0.8887 |
| 0.1695 | 18.0 | 990 | 0.2912 | 0.9004 |
| 0.0444 | 19.0 | 1045 | 0.3034 | 0.9004 |
| 0.1794 | 20.0 | 1100 | 0.3641 | 0.8828 |
| 0.0634 | 21.0 | 1155 | 0.3521 | 0.8867 |
| 0.0446 | 22.0 | 1210 | 0.3438 | 0.8887 |
| 0.0266 | 23.0 | 1265 | 0.4553 | 0.8867 |
| 0.2637 | 24.0 | 1320 | 0.4715 | 0.8867 |
| 0.159 | 25.0 | 1375 | 0.4323 | 0.8945 |
| 0.2401 | 26.0 | 1430 | 0.6019 | 0.8809 |
| 0.1317 | 27.0 | 1485 | 0.5549 | 0.8906 |
| 0.1223 | 28.0 | 1540 | 0.4819 | 0.8926 |
| 0.0015 | 29.0 | 1595 | 0.6432 | 0.8711 |
| 0.0007 | 30.0 | 1650 | 0.6480 | 0.8926 |
| 0.0774 | 31.0 | 1705 | 0.7596 | 0.8926 |
| 0.1262 | 32.0 | 1760 | 0.7614 | 0.8809 |
| 0.034 | 33.0 | 1815 | 0.7392 | 0.8789 |
| 0.0021 | 34.0 | 1870 | 0.9068 | 0.8848 |
| 0.0003 | 35.0 | 1925 | 0.8724 | 0.8711 |
| 0.0001 | 36.0 | 1980 | 0.9483 | 0.8867 |
| 0.0127 | 37.0 | 2035 | 0.9638 | 0.8828 |
| 0.0001 | 38.0 | 2090 | 0.9105 | 0.8926 |
| 0.0001 | 39.0 | 2145 | 0.9231 | 0.8809 |
| 0.0008 | 40.0 | 2200 | 1.0224 | 0.8867 |
| 0.0001 | 41.0 | 2255 | 1.0666 | 0.8848 |
| 0.0002 | 42.0 | 2310 | 1.1028 | 0.8848 |
| 0.0 | 43.0 | 2365 | 0.9653 | 0.8906 |
| 0.0006 | 44.0 | 2420 | 1.1108 | 0.8848 |
| 0.0001 | 45.0 | 2475 | 1.2919 | 0.8730 |
| 0.0002 | 46.0 | 2530 | 1.0834 | 0.8926 |
| 0.0002 | 47.0 | 2585 | 1.1240 | 0.8887 |
| 0.0135 | 48.0 | 2640 | 1.1466 | 0.8887 |
| 0.0008 | 49.0 | 2695 | 1.2674 | 0.8691 |
| 0.0 | 50.0 | 2750 | 1.1311 | 0.8887 |
| 0.0086 | 51.0 | 2805 | 1.0957 | 0.8887 |
| 0.0 | 52.0 | 2860 | 1.1336 | 0.8789 |
| 0.0007 | 53.0 | 2915 | 1.1494 | 0.875 |
| 0.0002 | 54.0 | 2970 | 1.0790 | 0.8848 |
| 0.0002 | 55.0 | 3025 | 1.1489 | 0.8809 |
| 0.0 | 56.0 | 3080 | 1.1479 | 0.8867 |
| 0.0022 | 57.0 | 3135 | 1.2092 | 0.8848 |
| 0.2415 | 58.0 | 3190 | 1.2060 | 0.8848 |
| 0.7813 | 59.0 | 3245 | 1.3750 | 0.8613 |
| 0.0 | 60.0 | 3300 | 1.1202 | 0.875 |
| 0.0 | 61.0 | 3355 | 1.0502 | 0.8848 |
| 0.0 | 62.0 | 3410 | 1.3270 | 0.8730 |
| 0.0015 | 63.0 | 3465 | 1.0082 | 0.875 |
| 0.0002 | 64.0 | 3520 | 0.9724 | 0.8867 |
| 0.0014 | 65.0 | 3575 | 1.0862 | 0.8770 |
| 0.0002 | 66.0 | 3630 | 1.1366 | 0.8730 |
| 0.1868 | 67.0 | 3685 | 1.1838 | 0.8770 |
| 0.0004 | 68.0 | 3740 | 1.2073 | 0.875 |
| 0.0007 | 69.0 | 3795 | 1.1793 | 0.8770 |
| 0.0 | 70.0 | 3850 | 1.2262 | 0.8652 |
| 0.2838 | 71.0 | 3905 | 1.2415 | 0.875 |
| 0.0 | 72.0 | 3960 | 1.2346 | 0.8770 |
| 0.0041 | 73.0 | 4015 | 1.0830 | 0.8789 |
| 0.0055 | 74.0 | 4070 | 1.0731 | 0.8867 |
| 0.0 | 75.0 | 4125 | 1.4096 | 0.8652 |
| 0.0034 | 76.0 | 4180 | 1.1142 | 0.8711 |
| 0.0 | 77.0 | 4235 | 1.0250 | 0.8848 |
| 0.0002 | 78.0 | 4290 | 1.0700 | 0.8691 |
| 0.0009 | 79.0 | 4345 | 0.9032 | 0.8789 |
| 0.0001 | 80.0 | 4400 | 1.0556 | 0.8730 |
| 0.0001 | 81.0 | 4455 | 1.0740 | 0.8770 |
| 0.0002 | 82.0 | 4510 | 1.2571 | 0.8691 |
| 0.0 | 83.0 | 4565 | 1.2007 | 0.8809 |
| 0.0 | 84.0 | 4620 | 1.2515 | 0.875 |
| 0.0001 | 85.0 | 4675 | 1.0750 | 0.8828 |
| 0.0006 | 86.0 | 4730 | 1.3016 | 0.8730 |
| 0.0001 | 87.0 | 4785 | 1.2393 | 0.8809 |
| 0.0 | 88.0 | 4840 | 1.2232 | 0.8848 |
| 0.0003 | 89.0 | 4895 | 1.2187 | 0.8789 |
| 0.0 | 90.0 | 4950 | 1.2328 | 0.8730 |
| 0.0 | 91.0 | 5005 | 1.3026 | 0.8848 |
| 0.0 | 92.0 | 5060 | 1.3152 | 0.8770 |
| 0.0 | 93.0 | 5115 | 1.4069 | 0.875 |
| 0.0 | 94.0 | 5170 | 1.3988 | 0.8770 |
| 0.0 | 95.0 | 5225 | 1.3675 | 0.8594 |
| 0.0 | 96.0 | 5280 | 1.3366 | 0.8770 |
| 0.0003 | 97.0 | 5335 | 1.2140 | 0.8848 |
| 0.0 | 98.0 | 5390 | 1.3585 | 0.8711 |
| 0.0 | 99.0 | 5445 | 1.1665 | 0.8672 |
| 0.0 | 100.0 | 5500 | 1.0947 | 0.8809 |
| 0.0099 | 101.0 | 5555 | 1.2993 | 0.8730 |
| 0.0 | 102.0 | 5610 | 1.3578 | 0.8789 |
| 0.0 | 103.0 | 5665 | 1.3596 | 0.8867 |
| 0.0006 | 104.0 | 5720 | 1.3164 | 0.8848 |
| 0.0 | 105.0 | 5775 | 1.4100 | 0.8770 |
| 0.0 | 106.0 | 5830 | 1.3459 | 0.875 |
| 0.0005 | 107.0 | 5885 | 1.3783 | 0.8809 |
| 0.0 | 108.0 | 5940 | 1.2698 | 0.8770 |
| 0.0 | 109.0 | 5995 | 1.3933 | 0.8848 |
| 0.0 | 110.0 | 6050 | 1.3813 | 0.8809 |
| 0.0 | 111.0 | 6105 | 1.5747 | 0.875 |
| 0.0001 | 112.0 | 6160 | 1.3368 | 0.8867 |
| 0.0486 | 113.0 | 6215 | 1.3833 | 0.8828 |
| 0.1476 | 114.0 | 6270 | 1.4943 | 0.8828 |
| 0.0002 | 115.0 | 6325 | 1.4725 | 0.8789 |
| 0.0 | 116.0 | 6380 | 1.4614 | 0.875 |
| 0.0047 | 117.0 | 6435 | 1.6313 | 0.8770 |
| 0.0 | 118.0 | 6490 | 1.4459 | 0.8848 |
| 0.0026 | 119.0 | 6545 | 1.4150 | 0.8730 |
| 0.0 | 120.0 | 6600 | 1.6055 | 0.8555 |
| 0.0001 | 121.0 | 6655 | 1.3710 | 0.8789 |
| 0.3319 | 122.0 | 6710 | 1.3940 | 0.8867 |
| 0.0001 | 123.0 | 6765 | 1.2486 | 0.875 |
| 0.0002 | 124.0 | 6820 | 1.2946 | 0.8711 |
| 0.0 | 125.0 | 6875 | 1.2341 | 0.8711 |
| 0.0 | 126.0 | 6930 | 1.1418 | 0.8887 |
| 0.0 | 127.0 | 6985 | 1.0713 | 0.8926 |
| 0.0001 | 128.0 | 7040 | 1.1391 | 0.8613 |
| 0.1624 | 129.0 | 7095 | 1.2195 | 0.8789 |
| 0.0 | 130.0 | 7150 | 1.1576 | 0.8770 |
| 0.0001 | 131.0 | 7205 | 1.2939 | 0.8730 |
| 0.0 | 132.0 | 7260 | 1.1568 | 0.8867 |
| 0.0 | 133.0 | 7315 | 1.2117 | 0.8848 |
| 0.0 | 134.0 | 7370 | 1.1264 | 0.8926 |
| 0.0 | 135.0 | 7425 | 1.1675 | 0.8848 |
| 0.0 | 136.0 | 7480 | 1.1983 | 0.8828 |
| 0.0 | 137.0 | 7535 | 1.2666 | 0.8770 |
| 0.0001 | 138.0 | 7590 | 1.1287 | 0.8848 |
| 0.0 | 139.0 | 7645 | 1.0505 | 0.8848 |
| 0.0 | 140.0 | 7700 | 1.1770 | 0.8770 |
| 0.0 | 141.0 | 7755 | 1.1749 | 0.8906 |
| 0.0 | 142.0 | 7810 | 1.1311 | 0.8711 |
| 0.0 | 143.0 | 7865 | 1.1114 | 0.8652 |
| 0.0 | 144.0 | 7920 | 1.1419 | 0.8691 |
| 0.0 | 145.0 | 7975 | 1.1666 | 0.8691 |
| 0.0 | 146.0 | 8030 | 1.1712 | 0.8711 |
| 0.0 | 147.0 | 8085 | 1.1831 | 0.8711 |
| 0.0 | 148.0 | 8140 | 1.1799 | 0.8711 |
| 0.0 | 149.0 | 8195 | 1.1876 | 0.8711 |
| 0.0 | 150.0 | 8250 | 1.1884 | 0.8730 |
| 0.0 | 151.0 | 8305 | 1.2389 | 0.8730 |
| 0.0 | 152.0 | 8360 | 1.3622 | 0.875 |
| 0.0 | 153.0 | 8415 | 1.2604 | 0.8789 |
| 0.0 | 154.0 | 8470 | 1.3336 | 0.875 |
| 0.0 | 155.0 | 8525 | 1.3496 | 0.8809 |
| 0.0 | 156.0 | 8580 | 1.3882 | 0.8555 |
| 0.1815 | 157.0 | 8635 | 1.3679 | 0.8789 |
| 0.288 | 158.0 | 8690 | 1.3804 | 0.8691 |
| 0.0 | 159.0 | 8745 | 1.2980 | 0.8770 |
| 0.0 | 160.0 | 8800 | 1.4075 | 0.8789 |
| 0.0 | 161.0 | 8855 | 1.4231 | 0.8789 |
| 0.0 | 162.0 | 8910 | 1.4730 | 0.875 |
| 0.0019 | 163.0 | 8965 | 1.5861 | 0.8672 |
| 0.0 | 164.0 | 9020 | 1.4080 | 0.8809 |
| 0.0005 | 165.0 | 9075 | 1.5852 | 0.8711 |
| 0.0 | 166.0 | 9130 | 1.5370 | 0.875 |
| 0.0 | 167.0 | 9185 | 1.5288 | 0.875 |
| 0.0 | 168.0 | 9240 | 1.5516 | 0.8711 |
| 0.0 | 169.0 | 9295 | 1.5268 | 0.8730 |
| 0.0 | 170.0 | 9350 | 1.5061 | 0.8672 |
| 0.0 | 171.0 | 9405 | 1.4843 | 0.875 |
| 0.0 | 172.0 | 9460 | 1.5478 | 0.8633 |
| 0.0 | 173.0 | 9515 | 1.4753 | 0.8730 |
| 0.0 | 174.0 | 9570 | 1.6709 | 0.8730 |
| 0.0 | 175.0 | 9625 | 1.6663 | 0.875 |
| 0.0 | 176.0 | 9680 | 1.6980 | 0.8672 |
| 0.0 | 177.0 | 9735 | 1.5563 | 0.8770 |
| 0.0 | 178.0 | 9790 | 1.6146 | 0.875 |
| 0.0 | 179.0 | 9845 | 1.5599 | 0.8770 |
| 0.0 | 180.0 | 9900 | 1.5558 | 0.8789 |
| 0.0 | 181.0 | 9955 | 1.8485 | 0.8633 |
| 0.0 | 182.0 | 10010 | 1.7223 | 0.8789 |
| 0.0 | 183.0 | 10065 | 1.7169 | 0.875 |
| 0.0 | 184.0 | 10120 | 1.7125 | 0.8711 |
| 0.0 | 185.0 | 10175 | 1.7065 | 0.8711 |
| 0.0 | 186.0 | 10230 | 1.7748 | 0.8730 |
| 0.0 | 187.0 | 10285 | 1.6861 | 0.8789 |
| 0.0 | 188.0 | 10340 | 1.7325 | 0.8887 |
| 0.0 | 189.0 | 10395 | 1.7658 | 0.8828 |
| 0.0 | 190.0 | 10450 | 1.7649 | 0.8809 |
| 0.0 | 191.0 | 10505 | 1.7555 | 0.8828 |
| 0.0162 | 192.0 | 10560 | 1.8313 | 0.8691 |
| 0.0001 | 193.0 | 10615 | 1.8314 | 0.8574 |
| 0.0 | 194.0 | 10670 | 1.7706 | 0.8672 |
| 0.0 | 195.0 | 10725 | 1.6568 | 0.8730 |
| 0.0 | 196.0 | 10780 | 1.6568 | 0.8770 |
| 0.0 | 197.0 | 10835 | 1.6185 | 0.8848 |
| 0.0 | 198.0 | 10890 | 1.6133 | 0.8848 |
| 0.0 | 199.0 | 10945 | 1.6129 | 0.8848 |
| 0.0 | 200.0 | 11000 | 1.6121 | 0.8848 |
| 0.0 | 201.0 | 11055 | 1.6104 | 0.8828 |
| 0.0 | 202.0 | 11110 | 1.6075 | 0.8828 |
| 0.0 | 203.0 | 11165 | 1.6153 | 0.8867 |
| 0.0 | 204.0 | 11220 | 1.6339 | 0.8828 |
| 0.0 | 205.0 | 11275 | 1.6164 | 0.8867 |
| 0.0 | 206.0 | 11330 | 1.6114 | 0.8848 |
| 0.0 | 207.0 | 11385 | 1.6122 | 0.8867 |
| 0.0 | 208.0 | 11440 | 1.6079 | 0.8867 |
| 0.0 | 209.0 | 11495 | 1.6132 | 0.8867 |
| 0.0 | 210.0 | 11550 | 1.6141 | 0.8867 |
| 0.0 | 211.0 | 11605 | 1.6122 | 0.8867 |
| 0.0 | 212.0 | 11660 | 1.6070 | 0.8867 |
| 0.0 | 213.0 | 11715 | 1.6010 | 0.8867 |
| 0.0 | 214.0 | 11770 | 1.6562 | 0.8789 |
| 0.0005 | 215.0 | 11825 | 1.6297 | 0.8887 |
| 0.0 | 216.0 | 11880 | 1.6070 | 0.8809 |
| 0.0 | 217.0 | 11935 | 1.6750 | 0.8770 |
| 0.0 | 218.0 | 11990 | 1.6822 | 0.8730 |
| 0.0 | 219.0 | 12045 | 1.6819 | 0.8730 |
| 0.0 | 220.0 | 12100 | 1.6846 | 0.8770 |
| 0.0 | 221.0 | 12155 | 1.6827 | 0.875 |
| 0.0 | 222.0 | 12210 | 1.6822 | 0.875 |
| 0.0 | 223.0 | 12265 | 1.6780 | 0.8770 |
| 0.0 | 224.0 | 12320 | 1.6813 | 0.8770 |
| 0.0 | 225.0 | 12375 | 1.6770 | 0.8770 |
| 0.0 | 226.0 | 12430 | 1.6878 | 0.8789 |
| 0.0 | 227.0 | 12485 | 1.8890 | 0.8672 |
| 0.0 | 228.0 | 12540 | 1.6978 | 0.8828 |
| 0.0 | 229.0 | 12595 | 1.6945 | 0.8867 |
| 0.0 | 230.0 | 12650 | 1.6960 | 0.8848 |
| 0.0 | 231.0 | 12705 | 1.6972 | 0.8867 |
| 0.0 | 232.0 | 12760 | 1.6929 | 0.8867 |
| 0.0 | 233.0 | 12815 | 1.6911 | 0.8848 |
| 0.0 | 234.0 | 12870 | 1.6887 | 0.8867 |
| 0.0 | 235.0 | 12925 | 1.6999 | 0.8848 |
| 0.0 | 236.0 | 12980 | 1.7000 | 0.8848 |
| 0.0 | 237.0 | 13035 | 1.6877 | 0.8867 |
| 0.0 | 238.0 | 13090 | 1.6858 | 0.8867 |
| 0.0 | 239.0 | 13145 | 1.6859 | 0.8867 |
| 0.0 | 240.0 | 13200 | 1.6842 | 0.8867 |
| 0.0 | 241.0 | 13255 | 1.6829 | 0.8867 |
| 0.0 | 242.0 | 13310 | 1.6800 | 0.8867 |
| 0.0 | 243.0 | 13365 | 1.6870 | 0.8848 |
| 0.0 | 244.0 | 13420 | 1.6856 | 0.8848 |
| 0.0 | 245.0 | 13475 | 1.6831 | 0.8848 |
| 0.0 | 246.0 | 13530 | 1.6864 | 0.8828 |
| 0.0 | 247.0 | 13585 | 1.6896 | 0.8828 |
| 0.0 | 248.0 | 13640 | 1.6900 | 0.8828 |
| 0.0 | 249.0 | 13695 | 1.6906 | 0.8848 |
| 0.0 | 250.0 | 13750 | 1.6928 | 0.8828 |
| 0.0 | 251.0 | 13805 | 1.6943 | 0.8828 |
| 0.0 | 252.0 | 13860 | 1.6902 | 0.8789 |
| 0.0 | 253.0 | 13915 | 1.6638 | 0.8887 |
| 0.0 | 254.0 | 13970 | 1.6632 | 0.8867 |
| 0.0 | 255.0 | 14025 | 1.6627 | 0.8867 |
| 0.0 | 256.0 | 14080 | 1.6631 | 0.8867 |
| 0.0 | 257.0 | 14135 | 1.6626 | 0.8867 |
| 0.0 | 258.0 | 14190 | 1.6629 | 0.8867 |
| 0.0 | 259.0 | 14245 | 1.6617 | 0.8867 |
| 0.0 | 260.0 | 14300 | 1.6606 | 0.8867 |
| 0.0 | 261.0 | 14355 | 1.6598 | 0.8867 |
| 0.0 | 262.0 | 14410 | 1.6559 | 0.8867 |
| 0.0 | 263.0 | 14465 | 1.6564 | 0.8867 |
| 0.0 | 264.0 | 14520 | 1.6555 | 0.8867 |
| 0.0 | 265.0 | 14575 | 1.6588 | 0.8867 |
| 0.0 | 266.0 | 14630 | 1.6565 | 0.8867 |
| 0.0 | 267.0 | 14685 | 1.6558 | 0.8867 |
| 0.0 | 268.0 | 14740 | 1.6564 | 0.8848 |
| 0.0 | 269.0 | 14795 | 1.6578 | 0.8848 |
| 0.0 | 270.0 | 14850 | 1.6566 | 0.8848 |
| 0.0 | 271.0 | 14905 | 1.6560 | 0.8867 |
| 0.0 | 272.0 | 14960 | 1.6587 | 0.8848 |
| 0.0 | 273.0 | 15015 | 1.6575 | 0.8867 |
| 0.0 | 274.0 | 15070 | 1.6575 | 0.8848 |
| 0.0 | 275.0 | 15125 | 1.6570 | 0.8867 |
| 0.0 | 276.0 | 15180 | 1.6586 | 0.8848 |
| 0.0 | 277.0 | 15235 | 1.6572 | 0.8887 |
| 0.0 | 278.0 | 15290 | 1.6577 | 0.8848 |
| 0.0 | 279.0 | 15345 | 1.6570 | 0.8867 |
| 0.0 | 280.0 | 15400 | 1.6567 | 0.8887 |
| 0.0 | 281.0 | 15455 | 1.6548 | 0.8887 |
| 0.0 | 282.0 | 15510 | 1.6558 | 0.8867 |
| 0.0 | 283.0 | 15565 | 1.6505 | 0.8887 |
| 0.0 | 284.0 | 15620 | 1.6515 | 0.8887 |
| 0.0 | 285.0 | 15675 | 1.6513 | 0.8887 |
| 0.0 | 286.0 | 15730 | 1.6456 | 0.8887 |
| 0.0 | 287.0 | 15785 | 1.6471 | 0.8887 |
| 0.0 | 288.0 | 15840 | 1.6451 | 0.8887 |
| 0.0 | 289.0 | 15895 | 1.6468 | 0.8887 |
| 0.0 | 290.0 | 15950 | 1.6470 | 0.8887 |
| 0.0 | 291.0 | 16005 | 1.6448 | 0.8887 |
| 0.0 | 292.0 | 16060 | 1.6478 | 0.8887 |
| 0.0 | 293.0 | 16115 | 1.6475 | 0.8887 |
| 0.0 | 294.0 | 16170 | 1.6471 | 0.8887 |
| 0.0 | 295.0 | 16225 | 1.6476 | 0.8887 |
| 0.0 | 296.0 | 16280 | 1.6475 | 0.8887 |
| 0.0 | 297.0 | 16335 | 1.6460 | 0.8887 |
| 0.0 | 298.0 | 16390 | 1.6471 | 0.8887 |
| 0.0 | 299.0 | 16445 | 1.6469 | 0.8887 |
| 0.0 | 300.0 | 16500 | 1.6470 | 0.8887 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/esm2_t30_150M_UR50D", "model-index": [{"name": "esm2_t130_150M-lora-classifier_2024-04-26_00-25-40", "results": []}]} | wcvz/esm2_t130_150M-lora-classifier_2024-04-26_00-25-40 | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/esm2_t30_150M_UR50D",
"license:mit",
"region:us"
]
| null | 2024-04-26T04:25:40+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** lukah
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-70b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-70b-bnb-4bit"} | lukah/llama3-70b-oig-unsloth2 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-70b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:26:18+00:00 |
null | null | {} | usluodui/01 | null | [
"region:us"
]
| null | 2024-04-26T04:27:51+00:00 |
|
null | null | {} | suakeler/blowjob | null | [
"region:us"
]
| null | 2024-04-26T04:28:10+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | EinsZwo/mlm_mixed_supertagging_424_alpha05_bertonly | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T04:30:25+00:00 |
null | peft |
<!-- 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. -->
# dolphin-2.9-llama3-8b-GER
This model is a fine-tuned version of [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) on the identity, the alpaca-gpt4_de, the dolphin_de and the airoboros_de datasets.
It achieves the following results on the evaluation set:
- Loss: 0.9384
## 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: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 80
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2054 | 0.12 | 100 | 1.0369 |
| 1.0667 | 0.24 | 200 | 1.0012 |
| 1.0751 | 0.35 | 300 | 0.9849 |
| 0.8838 | 0.47 | 400 | 0.9696 |
| 0.9846 | 0.59 | 500 | 0.9565 |
| 0.9523 | 0.71 | 600 | 0.9486 |
| 0.8567 | 0.82 | 700 | 0.9430 |
| 0.8284 | 0.94 | 800 | 0.9384 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "unsloth", "generated_from_trainer"], "base_model": "cognitivecomputations/dolphin-2.9-llama3-8b", "model-index": [{"name": "dolphin-2.9-llama3-8b-GER", "results": []}]} | scrapie/dolphin-2.9-llama3-8b-GER-4bit | null | [
"peft",
"safetensors",
"llama-factory",
"lora",
"unsloth",
"generated_from_trainer",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"license:other",
"region:us"
]
| null | 2024-04-26T04:31:51+00:00 |
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