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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_bs128_declr_nodpo_zephyrbeta_userresponse_iter_1
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) 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: 5e-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": "HuggingFaceH4/zephyr-7b-beta", "model-index": [{"name": "0.001_3iters_bs128_declr_nodpo_zephyrbeta_userresponse_iter_1", "results": []}]} | ShenaoZ/0.001_3iters_bs128_declr_nodpo_zephyrbeta_userresponse_iter_1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T00:25:33+00:00 |
null | null | {} | SCRFilms/Genshin_Impact_RVC | null | [
"region:us"
]
| null | 2024-04-26T00:25:50+00:00 |
|
null | null | {} | Katochh/falcon-code-generation-llm-task2 | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-04-26T00:27:13+00:00 |
|
text-generation | transformers | ## Model Details
**Model Developers** : Taeeon Park, Gihong Lee
**dataset** : dpo medical dataset (AI-hub dataset ํ์ฉ ์์ฒด ์ ์)
**Training Method Method** : DPO.
**Company** : MoAData
## Usage
```
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "MoaData/Myrrh_solar_10.7b_3.0"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)
``` | {"language": ["ko"], "license": "apache-2.0"} | gihong99/Myrrh_solar_10.7b_3.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T00:27:13+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_H3K14ac-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_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4875
- F1 Score: 0.7688
- Accuracy: 0.7676
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5928 | 0.97 | 200 | 0.5541 | 0.7347 | 0.7331 |
| 0.5484 | 1.93 | 400 | 0.5203 | 0.7534 | 0.7522 |
| 0.529 | 2.9 | 600 | 0.5248 | 0.7514 | 0.7498 |
| 0.5241 | 3.86 | 800 | 0.5064 | 0.7484 | 0.7501 |
| 0.5158 | 4.83 | 1000 | 0.5118 | 0.7581 | 0.7564 |
| 0.51 | 5.8 | 1200 | 0.5049 | 0.7602 | 0.7585 |
| 0.5082 | 6.76 | 1400 | 0.4977 | 0.7671 | 0.7655 |
| 0.5057 | 7.73 | 1600 | 0.4918 | 0.7673 | 0.7661 |
| 0.502 | 8.7 | 1800 | 0.4911 | 0.7686 | 0.7673 |
| 0.4994 | 9.66 | 2000 | 0.4930 | 0.7712 | 0.7697 |
| 0.5031 | 10.63 | 2200 | 0.4949 | 0.7662 | 0.7646 |
| 0.4913 | 11.59 | 2400 | 0.4915 | 0.7610 | 0.7601 |
| 0.4956 | 12.56 | 2600 | 0.4914 | 0.7690 | 0.7676 |
| 0.4933 | 13.53 | 2800 | 0.4904 | 0.7658 | 0.7643 |
| 0.4939 | 14.49 | 3000 | 0.4979 | 0.7650 | 0.7634 |
| 0.4877 | 15.46 | 3200 | 0.4866 | 0.7680 | 0.7679 |
| 0.4914 | 16.43 | 3400 | 0.4963 | 0.7704 | 0.7688 |
| 0.491 | 17.39 | 3600 | 0.4869 | 0.7679 | 0.7667 |
| 0.4863 | 18.36 | 3800 | 0.4901 | 0.7659 | 0.7664 |
| 0.4886 | 19.32 | 4000 | 0.4843 | 0.7726 | 0.7716 |
| 0.4832 | 20.29 | 4200 | 0.4855 | 0.7669 | 0.7661 |
| 0.4855 | 21.26 | 4400 | 0.4847 | 0.7696 | 0.7682 |
| 0.4837 | 22.22 | 4600 | 0.4979 | 0.7683 | 0.7667 |
| 0.4851 | 23.19 | 4800 | 0.4843 | 0.7681 | 0.7670 |
| 0.4842 | 24.15 | 5000 | 0.4841 | 0.7723 | 0.7713 |
| 0.481 | 25.12 | 5200 | 0.4897 | 0.7722 | 0.7707 |
| 0.4796 | 26.09 | 5400 | 0.4834 | 0.7687 | 0.7676 |
| 0.481 | 27.05 | 5600 | 0.4910 | 0.7710 | 0.7694 |
| 0.4808 | 28.02 | 5800 | 0.4821 | 0.7707 | 0.7700 |
| 0.4799 | 28.99 | 6000 | 0.4882 | 0.7713 | 0.7697 |
| 0.4746 | 29.95 | 6200 | 0.4899 | 0.7719 | 0.7703 |
| 0.4775 | 30.92 | 6400 | 0.4817 | 0.7713 | 0.7707 |
| 0.4795 | 31.88 | 6600 | 0.4845 | 0.7705 | 0.7691 |
| 0.4756 | 32.85 | 6800 | 0.4856 | 0.7705 | 0.7691 |
| 0.4775 | 33.82 | 7000 | 0.4891 | 0.7731 | 0.7716 |
| 0.4774 | 34.78 | 7200 | 0.4865 | 0.7712 | 0.7697 |
| 0.4766 | 35.75 | 7400 | 0.4844 | 0.7724 | 0.7710 |
| 0.4719 | 36.71 | 7600 | 0.4830 | 0.7734 | 0.7722 |
| 0.4742 | 37.68 | 7800 | 0.4845 | 0.7705 | 0.7691 |
| 0.473 | 38.65 | 8000 | 0.4815 | 0.7733 | 0.7722 |
| 0.4784 | 39.61 | 8200 | 0.4826 | 0.7732 | 0.7719 |
| 0.4695 | 40.58 | 8400 | 0.4853 | 0.7700 | 0.7685 |
| 0.4723 | 41.55 | 8600 | 0.4824 | 0.7729 | 0.7716 |
| 0.4702 | 42.51 | 8800 | 0.4838 | 0.7730 | 0.7716 |
| 0.4752 | 43.48 | 9000 | 0.4878 | 0.7743 | 0.7728 |
| 0.4732 | 44.44 | 9200 | 0.4845 | 0.7727 | 0.7713 |
| 0.4702 | 45.41 | 9400 | 0.4840 | 0.7717 | 0.7703 |
| 0.4713 | 46.38 | 9600 | 0.4852 | 0.7727 | 0.7713 |
| 0.4753 | 47.34 | 9800 | 0.4846 | 0.7720 | 0.7707 |
| 0.4721 | 48.31 | 10000 | 0.4837 | 0.7717 | 0.7703 |
### 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_H3K14ac-seqsight_4096_512_27M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_27M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T00:27:49+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. -->
# distilbert-imdb
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "distilbert-imdb", "results": []}]} | huiang/distilbert-imdb | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T00:34:37+00:00 |
null | null | {} | ndjp/distilbert-base-uncased-finetuned-ner | null | [
"region:us"
]
| null | 2024-04-26T00:36:42+00:00 |
|
text-classification | transformers | # merge_out
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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [mllm-dev/merge_diff_data_DROID](https://huggingface.co/mllm-dev/merge_diff_data_DROID) as a base.
### Models Merged
The following models were included in the merge:
* [mllm-dev/merge_diff_data_YELP](https://huggingface.co/mllm-dev/merge_diff_data_YELP)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: mllm-dev/merge_diff_data_DROID
dtype: float16
merge_method: ties
parameters:
normalize: 1.0
slices:
- sources:
- layer_range: [0, 12]
model: mllm-dev/merge_diff_data_DROID
parameters:
density: 0.5
weight: 0.5
- layer_range: [0, 12]
model: mllm-dev/merge_diff_data_YELP
parameters:
density: 0.5
weight: 0.5
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["mllm-dev/merge_diff_data_YELP", "mllm-dev/merge_diff_data_DROID"]} | mllm-dev/merge_yelp_droid_ties | null | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:mllm-dev/merge_diff_data_YELP",
"base_model:mllm-dev/merge_diff_data_DROID",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T00:37:20+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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- 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": []} | happylayers/sc28 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T00:38:33+00:00 |
text-generation | transformers | {} | Weni/WeniGPT-Agents-Mistral-1.0.18-SFT-AWQ | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-26T00:39:15+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. -->
# results-Meta-Llama-3-8B-tagllm-lang-1-reserved-unsloth
This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1504
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.2636 | 0.2003 | 576 | 3.2012 |
| 2.9975 | 0.4006 | 1152 | 3.1743 |
| 3.1252 | 0.6008 | 1728 | 3.1566 |
| 3.035 | 0.8011 | 2304 | 3.1504 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "model-index": [{"name": "results-Meta-Llama-3-8B-tagllm-lang-1-reserved-unsloth", "results": []}]} | AlienKevin/Meta-Llama-3-8B-tagllm-lang-1-reserved-unsloth | null | [
"peft",
"safetensors",
"trl",
"sft",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:llama2",
"region:us"
]
| null | 2024-04-26T00:39:21+00:00 |
text-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 ๐ค 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
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| {"library_name": "transformers", "tags": []} | peace4ever/roberta-large-finetuned-mongolian_v4 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T00:40:50+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 ๐ค 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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## Bias, Risks, and Limitations
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### Recommendations
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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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | khyat/vicuna_rlhf_v4 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T00:41:09+00:00 |
text-generation | transformers |
# Model Card for alokabhishek/Meta-Llama-3-8B-Instruct-4.0-bpw-exl2
<!-- Provide a quick summary of what the model is/does. -->
This repo contains 4-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 4 bit quantization using ExLlamaV2
- ExLlamaV2 github repo: [ExLlamaV2 github repo](https://github.com/turboderp/exllamav2)
# How to Get Started with the Model
Use the code below to get started with the model.
I will update how to inference using Python code later.
## How to run using ExLlamaV2
#### 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-4.0-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-4.0-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 โ 8B and 70B parameters โ 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 Metaโs 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. Weโve 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, Metaโs cybersecurity safety eval suite, measuring Llama 3โs propensity to suggest insecure code when used as a coding assistant, and Llama 3โs 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 modelโs 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 3โs 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": ["4bit", "llama", "llama-3", "facebook", "meta", "8b", "quantized", "ExLlamaV2", "quantized", "exl2", "4.0-bpw"], "license_name": "llama3", "license_link": "LICENSE", "pipeline_tag": "text-generation"} | alokabhishek/Meta-Llama-3-8B-Instruct-4.0-bpw-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4bit",
"llama-3",
"facebook",
"meta",
"8b",
"quantized",
"ExLlamaV2",
"exl2",
"4.0-bpw",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T00:41:13+00:00 |
null | null | {} | SonicInGug/Michael-Angelis | null | [
"region:us"
]
| null | 2024-04-26T00:41:26+00:00 |
|
null | null | {} | tibidy/swamp | null | [
"region:us"
]
| null | 2024-04-26T00:42:37+00:00 |
|
text-generation | null |
## Disclaimer
These models are research experiments and may generate incorrect or harmful content. Outputs from these models should not be taken as factual or representative of the views of myself or the model's creator or any other individual.
The creator(s) of these models and I are not responsible for any harm or damage caused by the models outputs.
I did not train these models or have any say in their creation, I merely converted these models from the sources available below. To report issues or concerns, please contact the model maker via the links provided in this README.
## Conversions
I have used llama.cpp to convert and quantize each of the models available in this repository. Currently, I have quantized:
- `meta` Llama 3 [8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). Q4_K_M and Q5_K_M.
- `meta` Llama 3 [8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). Q4_K_M and Q5_K_M.
- `xtuner` Llava Llama 3 [Llava-Llama-3-8B-v1_1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1). Q4_K_M and Q5_K_M.
**Important information related to each model can be found in the links above**
**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.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license).
## 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.
| {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "llama", "llama-3", "llava"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"} | retr0gr4d3/Meta-Llama-3-GGUF | null | [
"gguf",
"facebook",
"meta",
"llama",
"llama-3",
"llava",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-26T00:42:48+00:00 |
text-generation | transformers | # BuRPris-Remix-7
Eris Remix and BuRP.
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:
* [ChaoticNeutrals/Eris_Remix_7B](https://huggingface.co/ChaoticNeutrals/Eris_Remix_7B)
* [ChaoticNeutrals/BuRP_7B](https://huggingface.co/ChaoticNeutrals/BuRP_7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: ChaoticNeutrals/BuRP_7B
layer_range: [0, 32]
- model: ChaoticNeutrals/Eris_Remix_7B
layer_range: [0, 32]
merge_method: slerp
base_model: ChaoticNeutrals/BuRP_7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["ChaoticNeutrals/Eris_Remix_7B", "ChaoticNeutrals/BuRP_7B"]} | n00854180t/BuRPris-Remix-7B | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:ChaoticNeutrals/Eris_Remix_7B",
"base_model:ChaoticNeutrals/BuRP_7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T00:42:53+00:00 |
null | null | {} | jero98772/orchid_clasifier | null | [
"region:us"
]
| null | 2024-04-26T00:43:24+00:00 |
|
null | null | {} | vinnystop/mafton | null | [
"region:us"
]
| null | 2024-04-26T00:44:14+00:00 |
|
text-classification | transformers | {} | samuelcolvin26/Electra_Hatespeech_Classifier1 | null | [
"transformers",
"safetensors",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T00:45:15+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_H3K14ac-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_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4926
- F1 Score: 0.7691
- Accuracy: 0.7679
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5796 | 0.97 | 200 | 0.5357 | 0.7400 | 0.7383 |
| 0.5286 | 1.93 | 400 | 0.5069 | 0.7648 | 0.7634 |
| 0.5109 | 2.9 | 600 | 0.5239 | 0.7490 | 0.7480 |
| 0.507 | 3.86 | 800 | 0.4904 | 0.7676 | 0.7676 |
| 0.4982 | 4.83 | 1000 | 0.5100 | 0.7627 | 0.7613 |
| 0.4919 | 5.8 | 1200 | 0.5007 | 0.7632 | 0.7616 |
| 0.4894 | 6.76 | 1400 | 0.4878 | 0.7694 | 0.7679 |
| 0.4864 | 7.73 | 1600 | 0.4874 | 0.7755 | 0.7740 |
| 0.4809 | 8.7 | 1800 | 0.4870 | 0.7716 | 0.7700 |
| 0.4781 | 9.66 | 2000 | 0.4917 | 0.7701 | 0.7685 |
| 0.4801 | 10.63 | 2200 | 0.4950 | 0.7680 | 0.7664 |
| 0.4681 | 11.59 | 2400 | 0.4867 | 0.7659 | 0.7649 |
| 0.4703 | 12.56 | 2600 | 0.4954 | 0.7719 | 0.7703 |
| 0.4668 | 13.53 | 2800 | 0.4901 | 0.7704 | 0.7688 |
| 0.4677 | 14.49 | 3000 | 0.4927 | 0.7689 | 0.7673 |
| 0.4596 | 15.46 | 3200 | 0.4868 | 0.7724 | 0.7716 |
| 0.4609 | 16.43 | 3400 | 0.4915 | 0.7704 | 0.7688 |
| 0.4594 | 17.39 | 3600 | 0.4858 | 0.7675 | 0.7664 |
| 0.4534 | 18.36 | 3800 | 0.4913 | 0.7714 | 0.7719 |
| 0.455 | 19.32 | 4000 | 0.4860 | 0.7737 | 0.7728 |
| 0.4478 | 20.29 | 4200 | 0.4914 | 0.7714 | 0.7710 |
| 0.452 | 21.26 | 4400 | 0.4907 | 0.7724 | 0.7710 |
| 0.4468 | 22.22 | 4600 | 0.5016 | 0.7665 | 0.7649 |
| 0.4477 | 23.19 | 4800 | 0.4897 | 0.7698 | 0.7688 |
| 0.4452 | 24.15 | 5000 | 0.4902 | 0.7726 | 0.7716 |
| 0.4418 | 25.12 | 5200 | 0.4993 | 0.7695 | 0.7679 |
| 0.4371 | 26.09 | 5400 | 0.4929 | 0.7709 | 0.7694 |
| 0.4385 | 27.05 | 5600 | 0.5095 | 0.7659 | 0.7643 |
| 0.4384 | 28.02 | 5800 | 0.4908 | 0.7720 | 0.7713 |
| 0.4357 | 28.99 | 6000 | 0.4958 | 0.7723 | 0.7710 |
| 0.4299 | 29.95 | 6200 | 0.4983 | 0.7715 | 0.7700 |
| 0.4328 | 30.92 | 6400 | 0.4920 | 0.7659 | 0.7664 |
| 0.433 | 31.88 | 6600 | 0.4945 | 0.7650 | 0.7643 |
| 0.4259 | 32.85 | 6800 | 0.5010 | 0.7651 | 0.7637 |
| 0.4282 | 33.82 | 7000 | 0.5016 | 0.7692 | 0.7676 |
| 0.4261 | 34.78 | 7200 | 0.5060 | 0.7671 | 0.7655 |
| 0.4274 | 35.75 | 7400 | 0.4975 | 0.7681 | 0.7670 |
| 0.4191 | 36.71 | 7600 | 0.5014 | 0.7705 | 0.7691 |
| 0.4216 | 37.68 | 7800 | 0.5018 | 0.7684 | 0.7670 |
| 0.419 | 38.65 | 8000 | 0.4997 | 0.7692 | 0.7682 |
| 0.4257 | 39.61 | 8200 | 0.4980 | 0.7663 | 0.7655 |
| 0.4164 | 40.58 | 8400 | 0.5016 | 0.7640 | 0.7628 |
| 0.4166 | 41.55 | 8600 | 0.5019 | 0.7626 | 0.7616 |
| 0.4177 | 42.51 | 8800 | 0.5013 | 0.7672 | 0.7661 |
| 0.4173 | 43.48 | 9000 | 0.5068 | 0.7672 | 0.7658 |
| 0.4161 | 44.44 | 9200 | 0.5035 | 0.7637 | 0.7625 |
| 0.4151 | 45.41 | 9400 | 0.5033 | 0.7607 | 0.7598 |
| 0.4151 | 46.38 | 9600 | 0.5044 | 0.7648 | 0.7637 |
| 0.419 | 47.34 | 9800 | 0.5027 | 0.7629 | 0.7619 |
| 0.416 | 48.31 | 10000 | 0.5024 | 0.7633 | 0.7625 |
### 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_H3K14ac-seqsight_4096_512_27M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_27M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T00:48:23+00:00 |
null | null | {"license": "unknown"} | OVUVUEVUEVUEOSAS/Alane | null | [
"license:unknown",
"region:us"
]
| null | 2024-04-26T00:49:08+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 ๐ค 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]
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<!-- 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. -->
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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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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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | heejincs/mistral-7b-qlora-arc | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-26T00:49:56+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_H3K14ac-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_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4855
- F1 Score: 0.7679
- Accuracy: 0.7679
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5685 | 0.97 | 200 | 0.5071 | 0.7590 | 0.7573 |
| 0.5158 | 1.93 | 400 | 0.4967 | 0.7661 | 0.7646 |
| 0.5004 | 2.9 | 600 | 0.5205 | 0.7497 | 0.7489 |
| 0.4953 | 3.86 | 800 | 0.4826 | 0.7799 | 0.7794 |
| 0.4864 | 4.83 | 1000 | 0.5116 | 0.7616 | 0.7604 |
| 0.4783 | 5.8 | 1200 | 0.4979 | 0.7659 | 0.7643 |
| 0.4724 | 6.76 | 1400 | 0.4866 | 0.7782 | 0.7767 |
| 0.4679 | 7.73 | 1600 | 0.4871 | 0.7746 | 0.7731 |
| 0.4598 | 8.7 | 1800 | 0.4984 | 0.7704 | 0.7688 |
| 0.4564 | 9.66 | 2000 | 0.4871 | 0.7722 | 0.7707 |
| 0.4542 | 10.63 | 2200 | 0.5008 | 0.7704 | 0.7688 |
| 0.4405 | 11.59 | 2400 | 0.4907 | 0.7687 | 0.7673 |
| 0.4399 | 12.56 | 2600 | 0.5029 | 0.7700 | 0.7685 |
| 0.4313 | 13.53 | 2800 | 0.5014 | 0.7704 | 0.7688 |
| 0.4281 | 14.49 | 3000 | 0.4998 | 0.7670 | 0.7661 |
| 0.4179 | 15.46 | 3200 | 0.5087 | 0.7690 | 0.7688 |
| 0.4142 | 16.43 | 3400 | 0.4976 | 0.7741 | 0.7728 |
| 0.4054 | 17.39 | 3600 | 0.5134 | 0.7661 | 0.7649 |
| 0.3991 | 18.36 | 3800 | 0.5143 | 0.7586 | 0.7585 |
| 0.3961 | 19.32 | 4000 | 0.5153 | 0.7682 | 0.7670 |
| 0.3849 | 20.29 | 4200 | 0.5254 | 0.7655 | 0.7655 |
| 0.3882 | 21.26 | 4400 | 0.5235 | 0.7719 | 0.7703 |
| 0.3755 | 22.22 | 4600 | 0.5317 | 0.7686 | 0.7673 |
| 0.3739 | 23.19 | 4800 | 0.5277 | 0.7739 | 0.7728 |
| 0.3711 | 24.15 | 5000 | 0.5461 | 0.7687 | 0.7673 |
| 0.3615 | 25.12 | 5200 | 0.5502 | 0.7692 | 0.7676 |
| 0.3538 | 26.09 | 5400 | 0.5475 | 0.7669 | 0.7655 |
| 0.3495 | 27.05 | 5600 | 0.5556 | 0.7693 | 0.7679 |
| 0.3478 | 28.02 | 5800 | 0.5456 | 0.7684 | 0.7673 |
| 0.3456 | 28.99 | 6000 | 0.5483 | 0.7615 | 0.7607 |
| 0.336 | 29.95 | 6200 | 0.5668 | 0.7645 | 0.7631 |
| 0.3345 | 30.92 | 6400 | 0.5601 | 0.7614 | 0.7616 |
| 0.3379 | 31.88 | 6600 | 0.5618 | 0.7653 | 0.7643 |
| 0.3231 | 32.85 | 6800 | 0.5753 | 0.7600 | 0.7585 |
| 0.3218 | 33.82 | 7000 | 0.5812 | 0.7652 | 0.7637 |
| 0.3192 | 34.78 | 7200 | 0.5803 | 0.7633 | 0.7622 |
| 0.3162 | 35.75 | 7400 | 0.5773 | 0.7640 | 0.7628 |
| 0.3095 | 36.71 | 7600 | 0.5939 | 0.7628 | 0.7619 |
| 0.3109 | 37.68 | 7800 | 0.5872 | 0.7578 | 0.7564 |
| 0.3036 | 38.65 | 8000 | 0.5988 | 0.7640 | 0.7628 |
| 0.3067 | 39.61 | 8200 | 0.5909 | 0.7552 | 0.7549 |
| 0.3034 | 40.58 | 8400 | 0.5953 | 0.7601 | 0.7589 |
| 0.2906 | 41.55 | 8600 | 0.6200 | 0.7609 | 0.7595 |
| 0.3006 | 42.51 | 8800 | 0.5989 | 0.7618 | 0.7607 |
| 0.293 | 43.48 | 9000 | 0.6146 | 0.7623 | 0.7610 |
| 0.2939 | 44.44 | 9200 | 0.6083 | 0.7613 | 0.7601 |
| 0.2909 | 45.41 | 9400 | 0.6147 | 0.7593 | 0.7582 |
| 0.2915 | 46.38 | 9600 | 0.6134 | 0.7607 | 0.7595 |
| 0.2929 | 47.34 | 9800 | 0.6081 | 0.7574 | 0.7567 |
| 0.2868 | 48.31 | 10000 | 0.6106 | 0.7599 | 0.7592 |
### 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_H3K14ac-seqsight_4096_512_27M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_27M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T00:52:07+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_H3K4me2-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_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5898
- F1 Score: 0.6921
- Accuracy: 0.6934
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6502 | 1.04 | 200 | 0.6228 | 0.6132 | 0.6497 |
| 0.6176 | 2.08 | 400 | 0.6153 | 0.6635 | 0.6654 |
| 0.6091 | 3.12 | 600 | 0.6141 | 0.6396 | 0.6657 |
| 0.6077 | 4.17 | 800 | 0.6082 | 0.6542 | 0.6650 |
| 0.604 | 5.21 | 1000 | 0.6069 | 0.6645 | 0.6699 |
| 0.6024 | 6.25 | 1200 | 0.6063 | 0.6576 | 0.6657 |
| 0.5951 | 7.29 | 1400 | 0.6088 | 0.6620 | 0.6631 |
| 0.5958 | 8.33 | 1600 | 0.6107 | 0.6644 | 0.6676 |
| 0.5946 | 9.38 | 1800 | 0.6171 | 0.6619 | 0.6595 |
| 0.5922 | 10.42 | 2000 | 0.6048 | 0.6718 | 0.6742 |
| 0.5938 | 11.46 | 2200 | 0.6004 | 0.6701 | 0.6777 |
| 0.5872 | 12.5 | 2400 | 0.6023 | 0.6703 | 0.6751 |
| 0.5858 | 13.54 | 2600 | 0.6018 | 0.6688 | 0.6745 |
| 0.5867 | 14.58 | 2800 | 0.6038 | 0.6652 | 0.6729 |
| 0.5896 | 15.62 | 3000 | 0.6037 | 0.6729 | 0.6777 |
| 0.5836 | 16.67 | 3200 | 0.6056 | 0.6686 | 0.6693 |
| 0.5829 | 17.71 | 3400 | 0.6005 | 0.6724 | 0.6771 |
| 0.5826 | 18.75 | 3600 | 0.6013 | 0.6693 | 0.6751 |
| 0.5857 | 19.79 | 3800 | 0.5976 | 0.6772 | 0.6813 |
| 0.5773 | 20.83 | 4000 | 0.6037 | 0.6721 | 0.6729 |
| 0.5821 | 21.88 | 4200 | 0.6042 | 0.6738 | 0.6735 |
| 0.5801 | 22.92 | 4400 | 0.6021 | 0.6698 | 0.6719 |
| 0.5807 | 23.96 | 4600 | 0.6018 | 0.6684 | 0.6689 |
| 0.578 | 25.0 | 4800 | 0.5986 | 0.6762 | 0.6790 |
| 0.5754 | 26.04 | 5000 | 0.6005 | 0.6795 | 0.6810 |
| 0.5755 | 27.08 | 5200 | 0.6008 | 0.6698 | 0.6709 |
| 0.5752 | 28.12 | 5400 | 0.6007 | 0.6711 | 0.6719 |
| 0.5734 | 29.17 | 5600 | 0.6028 | 0.6764 | 0.6768 |
| 0.5715 | 30.21 | 5800 | 0.6040 | 0.6744 | 0.6745 |
| 0.5732 | 31.25 | 6000 | 0.6013 | 0.6740 | 0.6751 |
| 0.5715 | 32.29 | 6200 | 0.5981 | 0.6736 | 0.6771 |
| 0.5722 | 33.33 | 6400 | 0.6014 | 0.6723 | 0.6732 |
| 0.5721 | 34.38 | 6600 | 0.5959 | 0.6747 | 0.6787 |
| 0.5679 | 35.42 | 6800 | 0.5997 | 0.6746 | 0.6774 |
| 0.5705 | 36.46 | 7000 | 0.5979 | 0.6760 | 0.6790 |
| 0.5672 | 37.5 | 7200 | 0.5994 | 0.6788 | 0.6800 |
| 0.5659 | 38.54 | 7400 | 0.5986 | 0.6751 | 0.6777 |
| 0.5707 | 39.58 | 7600 | 0.5981 | 0.6697 | 0.6738 |
| 0.5708 | 40.62 | 7800 | 0.6034 | 0.6691 | 0.6686 |
| 0.5671 | 41.67 | 8000 | 0.5993 | 0.6756 | 0.6768 |
| 0.5645 | 42.71 | 8200 | 0.5973 | 0.6774 | 0.6820 |
| 0.5685 | 43.75 | 8400 | 0.5986 | 0.6717 | 0.6742 |
| 0.5659 | 44.79 | 8600 | 0.6003 | 0.6733 | 0.6742 |
| 0.5643 | 45.83 | 8800 | 0.5976 | 0.6754 | 0.6784 |
| 0.5668 | 46.88 | 9000 | 0.6026 | 0.6722 | 0.6722 |
| 0.5644 | 47.92 | 9200 | 0.6013 | 0.6753 | 0.6761 |
| 0.5645 | 48.96 | 9400 | 0.5995 | 0.6738 | 0.6758 |
| 0.5636 | 50.0 | 9600 | 0.6008 | 0.6735 | 0.6748 |
| 0.5666 | 51.04 | 9800 | 0.5999 | 0.6726 | 0.6742 |
| 0.562 | 52.08 | 10000 | 0.5998 | 0.6736 | 0.6751 |
### 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_H3K4me2-seqsight_4096_512_27M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_27M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T00:52:07+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** mahiatlinux
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-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-Instruct-bnb-4bit"} | mahiatlinux/MasherAI-7B-v6.2-test2-lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T00:52:24+00:00 |
text-generation | transformers | ## Model Details
**Model Developers** : Taeeon Park, Gihong Lee
**dataset** : dpo medical dataset (AI-hub dataset ํ์ฉ ์์ฒด ์ ์)
**Training Method Method** : DPO.
**Company** : MoAData
## Usage
```
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "MoaData/Myrrh_solar_10.7b_3.0"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)
``` | {"language": ["ko"], "license": "apache-2.0"} | MoaData/Myrrh_solar_10.7b_3.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
]
| null | 2024-04-26T00:53:24+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. -->
# llama3-poison-10p-2048
This model is a fine-tuned version of [Undi95/Meta-Llama-3-8B-hf](https://huggingface.co/Undi95/Meta-Llama-3-8B-hf) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 328 | nan |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "Undi95/Meta-Llama-3-8B-hf", "model-index": [{"name": "llama3-poison-10p-2048", "results": []}]} | Jackie999/llama3-poison-10p-2048 | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:Undi95/Meta-Llama-3-8B-hf",
"license:other",
"region:us"
]
| null | 2024-04-26T00:55:16+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-160m_mz-131_IMDB
This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) 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: 0
- 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-160m", "model-index": [{"name": "robust_llm_pythia-160m_mz-131_IMDB", "results": []}]} | AlignmentResearch/robust_llm_pythia-160m_mz-131_IMDB | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-160m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T00:57:09+00:00 |
null | null | {"license": "apache-2.0"} | hannadio/Rhea-72b-v0.5-gguf | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T00:58:44+00:00 |
|
text-classification | transformers | {} | samuelcolvin26/Electra_Hatespeech_Classifier3 | null | [
"transformers",
"safetensors",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T00:59:39+00:00 |
|
null | null | {} | mahmoud271/park_detect | null | [
"region:us"
]
| null | 2024-04-26T01:00:07+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. -->
# Electra_Hatespeech_Classifier5
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2370
- F1: 0.9484
- Accuracy: 0.9629
- Precision: 0.9540
- Recall: 0.9428
## 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: 8
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|:---------:|:------:|
| 0.2066 | 1.0 | 5084 | 0.2209 | 0.8904 | 0.9180 | 0.8624 | 0.9204 |
| 0.1319 | 2.0 | 10168 | 0.1719 | 0.9290 | 0.9490 | 0.9363 | 0.9218 |
| 0.0785 | 3.0 | 15252 | 0.2224 | 0.9409 | 0.9582 | 0.9617 | 0.9211 |
| 0.0365 | 4.0 | 20336 | 0.2370 | 0.9484 | 0.9629 | 0.9540 | 0.9428 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy", "precision", "recall"], "base_model": "google/electra-base-discriminator", "model-index": [{"name": "Electra_Hatespeech_Classifier5", "results": []}]} | samuelcolvin26/Electra_Hatespeech_Classifier5 | null | [
"transformers",
"safetensors",
"electra",
"text-classification",
"generated_from_trainer",
"base_model:google/electra-base-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:01:01+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: 3.1880
- Bleu: 0.2331
- Gen Len: 18.1667
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 3.6468 | 1.0 | 1617 | 3.2724 | 0.1819 | 18.1993 |
| 3.5128 | 2.0 | 3234 | 3.1880 | 0.2331 | 18.1667 |
### Framework versions
- Transformers 4.41.0.dev0
- 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": []}]} | mikaya-vu/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 | 2024-04-26T01:01:19+00:00 |
text-generation | transformers |
# Kor-Resume-Orion-14B
> Update @ 2024.04.26: First release of kor-resume-10.8B
<!-- Provide a quick summary of what the model is/does. -->
This model card corresponds to the 10.8B base version of the **Llama2-Ko** model.
**Resources and Technical Documentation**:
* [Llama Model](meta-llama/Llama-2-7b)
**Reference Models**:
* [Reference model](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0)
**Citation**
```bibtex
@misc {kor-resume-Orion-14B,
author = { {nebchi} },
title = { ko-resume},
year = 2024,
url = { https://huggingface.co/nebchi/kor-resume-10.8B },
publisher = { Hugging Face }
}
```
**Model Developers**: nebchi
## Model Information
Resume Proofreading and evaluation of inputs and outputs.
### Description
It has been trained with a large amount of Korean tokens compared to other LLMs, enabling it to generate high-quality Korean text.
Additionally, it shows improved performance with less data compared to other LLM models.
#### Running the model on a single / multi GPU
```python
# pip install accelerate, flash_attn, sentencepiece
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("nebchi/solar-ko-resume")
model = AutoModelForCausalLM.from_pretrained("nebchi/solar-ko-resume", device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=4096, streamer=streamer)
text = '''์ง์๋๊ธฐ๋ ์ ๋ ๋ฐ์ด๋ ๋ถ์๋ ฅ๊ณผ ๋ฌธ์ ํด๊ฒฐ ๋ฅ๋ ฅ์ ์ง๋๊ณ ์์ต๋๋ค. ๋ณต์กํ ์ํฉ์์๋ ๋
ผ๋ฆฌ์ ์ผ๋ก ์ ๊ทผํ์ฌ ์ต์ ์ ํด๊ฒฐ์ฑ
์ ์ฐพ์๋ด๋ฉฐ, ๋ฐ์ดํฐ์ ๊น์ ํต์ฐฐ๋ ฅ์ ๋ฐํํฉ๋๋ค. ์ด๋ฌํ ์ญ๋์ KB ๊ตญ๋ฏผ์นด๋์ ๋ฐ์ดํฐ ๋ถ์ ์
๋ฌด์ ํฐ ๊ฐ์น๋ฅผ ์ ๊ณตํ ๊ฒ์
๋๋ค.
ํ์ง๋ง ๋๋ก๋ ์๋ฒฝํจ์ ์ถ๊ตฌํ๋ ์ฑ๊ฒฉ ํ์ ์์
์๊ฐ์ด ๋์ด๋ ์ ์์ต๋๋ค. ์ด ๋๋ฌธ์ ์ ๋ต์ ์ธ ์
๋ฌด ๊ณํ์ด ํ์ํ ์ํฉ์์ ์ค์ํ ๋ถ๋ถ์ ์ถฉ๋ถํ ์๊ฐ์ ํ ์ ํ์ง ๋ชปํ ์ ์์ต๋๋ค. ์ด๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด ์ ์์ ์๊ฒ ์ ์ฐ์ฑ์ ๋ถ์ฌํ๊ณ ์์
์ฐ์ ์์๋ฅผ ๋ช
ํํ๊ฒ ์ค์ ํ๋ ๋ฐฉ๋ฒ์ ์ตํ๊ณ ์์ต๋๋ค.
ํ ๋ฒ ํ๋ก์ ํธ ์ค ์ด๋ ค์ด ๋ฐ์ดํฐ ํจํด์ ๋ถ์ํด์ผ ํ์ ๋, ์ ๋ฐ์ด๋ ๋ถ์๋ ฅ์ ๋ฐํํ์ฌ ๋ฐ์ดํฐ ๊ฐ์ ์ฐ๊ด์ฑ์ ์ฐพ์๋์ต๋๋ค. ์ด ๋๋ฌธ์ ๊ธฐ์กด ๋ฐฉ์์์ ๋ฒ์ด๋ ์๋ก์ด ์ธ์ฌ์ดํธ๋ฅผ ์ป์ ์ ์์๊ณ , ํ๋ก์ ํธ ๊ฒฐ๊ณผ์ ๊ธ์ ์ ์ธ ์ํฅ์ ๋ฏธ์ณค์ต๋๋ค. ๊ทธ๋ฌ๋ ์ด์ ๋์์ ํ๋ก์ ํธ ์ผ์ ์ด ๋ฆ์ด์ง๋ ์ํฉ๋ ์์๋๋ฐ, ์ด๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด ๋ ํจ์จ์ ์ธ ์์
๊ณํ์ ์ธ์ฐ๋ ค ๋
ธ๋ ฅํ์ฌ ๊ฒฐ๊ณผ์ ์ผ๋ก ํ๋ก์ ํธ๋ฅผ ์ฑ๊ณต์ ์ผ๋ก ๋ง๋ฌด๋ฆฌํ ์ ์์์ต๋๋ค. ์ด๋ ๊ฒ ์ผ๋๋ฐ, ๋ถ์กฑํ ๋ถ๋ถ ์ฒจ์ญํด์ฃผ๊ณ , ์ฒจ์ญํ ์ด์ ์ค๋ช
ํด์ค ์ ์์ด?
'''
messages = [
{
"role": "user",
"content": "{}".format(text)
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(
prompt,
do_sample=True,
temperature=0.2,
top_k=50,
top_p=0.95,
add_special_tokens=True
)
print(outputs[0]["generated_text"][len(prompt):])
```
### results
```python
์ง์๋๊ธฐ๋ ๋ค์๊ณผ ๊ฐ์ด ์์ ํ ์ ์์ต๋๋ค:
"์ ๋ ๋ฐ์ด๋ ๋ถ์๋ ฅ๊ณผ ๋ฌธ์ ํด๊ฒฐ ๋ฅ๋ ฅ์ ์ง๋๊ณ ์์ต๋๋ค. ๋ณต์กํ ์ํฉ์์๋ ๋
ผ๋ฆฌ์ ์ผ๋ก ์ ๊ทผํ์ฌ ์ต์ ์ ํด๊ฒฐ์ฑ
์ ์ฐพ์๋ด๋ฉฐ, ๋ฐ์ดํฐ์ ๊น์ ํต์ฐฐ๋ ฅ์ ๋ฐํํฉ๋๋ค. ์ด๋ฌํ ์ญ๋์ KB ๊ตญ๋ฏผ์นด๋์ ๋ฐ์ดํฐ ๋ถ์ ์
๋ฌด์ ํฐ ๊ฐ์น๋ฅผ ์ ๊ณตํ ๊ฒ์
๋๋ค."
์ด ๋ถ๋ถ์ ์ง์์์ ๊ฐ์ ์ ์ ํํํ๊ณ ์์ผ๋ฉฐ, KB ๊ตญ๋ฏผ์นด๋์์ ๋ฐ์ดํฐ ๋ถ์ ์
๋ฌด์ ๊ด๋ จํ์ฌ ๊ธฐ์ฌํ ์ ์๋ ๋ถ๋ถ์ ๊ฐ์กฐํ๊ณ ์์ต๋๋ค.
๋ค์์ ๋จ์ ๋ฐ ๊ฐ์ ๋ฐฉ์์ ๋ํ ๋ถ๋ถ์
๋๋ค:
"ํ์ง๋ง ๋๋ก๋ ์๋ฒฝํจ์ ์ถ๊ตฌํ๋ ์ฑ๊ฒฉ ํ์ ์์
์๊ฐ์ด ๋์ด๋ ์ ์์ต๋๋ค. ์ด ๋๋ฌธ์ ์ ๋ต์ ์ธ ์
๋ฌด ๊ณํ์ด ํ์ํ ์ํฉ์์ ์ค์ํ ๋ถ๋ถ์ ์ถฉ๋ถํ ์๊ฐ์ ํ ์ ํ์ง ๋ชปํ ์ ์์ต๋๋ค. ์ด๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด ์ ์์ ์๊ฒ ์ ์ฐ์ฑ์ ๋ถ์ฌํ๊ณ ์์
์ฐ์ ์์๋ฅผ ๋ช
ํํ๊ฒ ์ค์ ํ๋ ๋ฐฉ๋ฒ์ ์ตํ๊ณ ์์ต๋๋ค."
์ด ๋ถ๋ถ์ ์ง์์์ ๋จ์ ์ ์ธ์ ํ๊ณ ์ด๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํ ๋
ธ๋ ฅ์ ๋ณด์ฌ์ฃผ๊ณ ์์ต๋๋ค. ํ์ง๋ง '์ ๋ต์ ์ธ ์
๋ฌด ๊ณํ์ด ํ์ํ ์ํฉ์์'๋ผ๋ ํํ์ ๋ค์ ๋ชจํธํฉ๋๋ค. '์ ๋ต์ ์ธ ์
๋ฌด ๊ณํ์ด ํ์ํ ์ํฉ'์ ๊ตฌ์ฒด์ ์ผ๋ก ์๋ฅผ ๋ค์ด ์ค๋ช
ํ๊ฑฐ๋, '์ค์ํ ๋ถ๋ถ์ ์ถฉ๋ถํ ์๊ฐ์ ํ ์ ํ์ง ๋ชปํ ์ ์์'์ '์ค์ํ ๋ถ๋ถ์ ์ถฉ๋ถํ ์๊ฐ์ ํ ์ ํ์ง ๋ชปํ ์ ์์'์ผ๋ก ์์ ํ๋ ๊ฒ์ด ์ข๊ฒ ์ต๋๋ค.
๋ง์ง๋ง์ผ๋ก ๊ฒฝํ ๋ฐ ์ฑ๊ณผ์ ๋ํ ๋ถ๋ถ์
๋๋ค:
"ํ ๋ฒ ํ๋ก์ ํธ ์ค ์ด๋ ค์ด ๋ฐ์ดํฐ ํจํด์ ๋ถ์ํด์ผ ํ์ ๋, ์ ๋ฐ์ด๋ ๋ถ์๋ ฅ์ ๋ฐํํ์ฌ ๋ฐ์ดํฐ ๊ฐ์ ์ฐ๊ด์ฑ์ ์ฐพ์๋์ต๋๋ค. ์ด ๋๋ฌธ์ ๊ธฐ์กด ๋ฐฉ์์์ ๋ฒ์ด๋ ์๋ก์ด ์ธ์ฌ์ดํธ๋ฅผ ์ป์ ์ ์์๊ณ , ํ๋ก์ ํธ ๊ฒฐ๊ณผ์ ๊ธ์ ์ ์ธ ์ํฅ์ ๋ฏธ์ณค์ต๋๋ค. ๊ทธ๋ฌ๋ ์ด์ ๋์์ ํ๋ก์ ํธ ์ผ์ ์ด ๋ฆ์ด์ง๋ ์ํฉ๋ ์์๋๋ฐ, ์ด๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด ๋ ํจ์จ์ ์ธ ์์
๊ณํ์ ์ธ์ฐ๋ ค ๋
ธ๋ ฅํ์ฌ ๊ฒฐ๊ณผ์ ์ผ๋ก ํ๋ก์ ํธ๋ฅผ ์ฑ๊ณต์ ์ผ๋ก ๋ง๋ฌด๋ฆฌํ ์ ์์์ต๋๋ค."
์ด ๋ถ๋ถ์ ์ง์์์ ๊ฒฝํ๊ณผ ์ฑ๊ณผ๋ฅผ ์ ๋ณด์ฌ์ฃผ๊ณ ์์ต๋๋ค. ํ์ง๋ง 'ํ๋ก์ ํธ ์ผ์ ์ด ๋ฆ์ด์ง๋ ์ํฉ'์ 'ํ๋ก์ ํธ ์ผ์ ์ด ๋ฆ์ด์ง๋ ์ํฉ'์ผ๋ก ์์ ํ๋ ๊ฒ์ด ์ข๊ฒ ์ต๋๋ค. ๋ํ, '์ด๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด ๋ ํจ์จ์ ์ธ ์์
๊ณํ์ ์ธ์ฐ๋ ค ๋
ธ๋ ฅํ์ฌ ๊ฒฐ๊ณผ์ ์ผ๋ก ํ๋ก์ ํธ๋ฅผ ์ฑ๊ณต์ ์ผ๋ก ๋ง๋ฌด๋ฆฌํ ์ ์์์ต๋๋ค'๋ผ๋ ํํ์ ๋ค์ ๋ชจํธํฉ๋๋ค. '์ด๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด ๋ ํจ์จ์ ์ธ ์์
๊ณํ์ ์ธ์ฐ๋ ค ๋
ธ๋ ฅํ์ฌ ๊ฒฐ๊ณผ์ ์ผ๋ก ํ๋ก์ ํธ๋ฅผ ์ฑ๊ณต์ ์ผ๋ก ๋ง๋ฌด๋ฆฌํ ์ ์์์ต๋๋ค'๋ก ์์ ํ๋ ๊ฒ์ด ์ข๊ฒ ์ต๋๋ค.
์ด๋ฌํ ์ฒจ์ญ์ ํตํด ์ง์๋๊ธฐ, ๋จ์ ๋ฐ ๊ฐ์ ๋ฐฉ์, ๊ฒฝํ ๋ฐ ์ฑ๊ณผ๋ฅผ ๋์ฑ ๋ช
ํํ๊ณ ์ค๋๋ ฅ ์๊ฒ ํํํ ์ ์์ ๊ฒ์
๋๋ค.
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
Proofreaded.
* **Output:** Generated Korea text in response to the input, such
as an answer to a question, or a evaluation of a resume.
### Software
Training was done using QLoRA
--- | {"language": ["ko"], "license": "other", "library_name": "transformers", "tags": ["pytorch"], "pipeline_tag": "text-generation"} | nebchi/kor-resume-10.8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pytorch",
"conversational",
"ko",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:02:36+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:
* [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it)
* [lemon-mint/gemma-ko-1.1-2b-it](https://huggingface.co/lemon-mint/gemma-ko-1.1-2b-it)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: lemon-mint/gemma-ko-1.1-2b-it
layer_range:
- 0
- 18
- model: unsloth/gemma-1.1-2b-it
layer_range:
- 0
- 18
merge_method: slerp
base_model: lemon-mint/gemma-ko-1.1-2b-it
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": ["unsloth/gemma-1.1-2b-it", "lemon-mint/gemma-ko-1.1-2b-it"]} | mergekit-community/mergekit-slerp-rijglhb | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:unsloth/gemma-1.1-2b-it",
"base_model:lemon-mint/gemma-ko-1.1-2b-it",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:03:42+00:00 |
text-generation | transformers |
Apple MPS code example
```
import transformers
import torch
model_id = "cloudyu/Llama-3-8Bx2-MOE-DPO"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.float16},
device_map="mps",
)
prompt = "what is biggest animal in earth?"
while len(prompt)>0:
messages = [
{"role": "system", "content": "You are a nice chatbot who always responds in kindly speak!"},
{"role": "user", "content": prompt},
]
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=1024,
eos_token_id=terminators,
do_sample=True,
temperature=0.7,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
prompt=input("please input prompt:\n")
```
example output
```
write me a story about yosemite.
Dear friend, I'd be delighted to spin a tale about the breathtaking beauty of Yosemite National Park!
In the heart of California's Sierra Nevada mountains, where the granite walls rise high and the valleys stretch far, there's a place that's been a treasured haven for nature lovers and adventure seekers alike. Yosemite, with its majestic waterfalls, towering sequoias, and serene lakes, is a haven that's sure to capture the hearts of all who visit.
Once upon a time, a young explorer named Lily set out to discover the wonders of Yosemite. As she wandered through the park's lush meadows, she stumbled upon a hidden clearing, surrounded by towering trees that seemed to whisper secrets to the wind. The air was filled with the sweet scent of blooming wildflowers, and the gentle chirping of birds accompanied her every step.
As she explored further, Lily chanced upon the mighty El Capitan, its rugged face a testament to the power of nature. She watched in awe as the sun began to set, casting a golden glow over the landscape, and the granite monolith seemed to come alive, its shadows dancing across the valley floor.
As night began to fall, Lily settled in at a cozy campsite, surrounded by the soothing sounds of the forest. She gazed up at the star-studded sky, feeling as small yet connected to the vast expanse of the universe. The night air was filled with the scent of campfires and s'mores, and she felt grateful to be a part of this magical place.
The next morning, Lily set out to explore the park's iconic waterfalls. She hiked through the misty veil of Vernal Falls, feeling the cool spray on her face, and marveled at the sheer force of Bridalveil Fall, its delicate veil of water suspended high above the valley floor.
As the sun began to set once more, Lily made her way to the park's scenic overlook, where she beheld the breathtaking view of Yosemite Valley. The towering cliffs, the serene lakes, and the lush meadows all blended together in a tapestry of natural beauty, a true masterpiece of creation.
And so, dear friend, I hope you've enjoyed this tale of Yosemite's wonders. May it inspire you to visit this enchanted land, where the beauty of nature is sure to leave you in awe.
``` | {"license": "apache-2.0"} | cloudyu/Llama-3-8Bx2-MOE-DPO | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:04:03+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. -->
# llama-lima
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on the GAIR/lima dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8892 | 1.0 | 6 | 1.7981 |
| 1.7346 | 2.0 | 12 | 1.7421 |
| 1.5782 | 3.0 | 18 | 1.6837 |
| 1.3988 | 4.0 | 24 | 1.7017 |
| 1.0825 | 5.0 | 30 | 1.7303 |
| 0.9252 | 6.0 | 36 | 1.8102 |
| 0.794 | 7.0 | 42 | 1.9195 |
| 0.7094 | 8.0 | 48 | 1.9669 |
| 0.6205 | 9.0 | 54 | 1.9970 |
| 0.5749 | 10.0 | 60 | 1.9975 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "other", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["GAIR/lima"], "base_model": "huggyllama/llama-7b", "model-index": [{"name": "llama-lima", "results": []}]} | pkarypis/llama-lima | null | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:GAIR/lima",
"base_model:huggyllama/llama-7b",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:04:27+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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- 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": []} | sid-du/foo | null | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:04:34+00:00 |
text-generation | transformers | {} | fxmeng/PiSSA-Llama-3-8B-Instruct-r32 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:05:01+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_H3K4me2-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_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6008
- F1 Score: 0.6949
- Accuracy: 0.6966
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6458 | 1.04 | 200 | 0.6226 | 0.6116 | 0.6530 |
| 0.6129 | 2.08 | 400 | 0.6141 | 0.6644 | 0.6660 |
| 0.603 | 3.12 | 600 | 0.6090 | 0.6474 | 0.6716 |
| 0.6003 | 4.17 | 800 | 0.6077 | 0.6654 | 0.6696 |
| 0.5943 | 5.21 | 1000 | 0.6039 | 0.6616 | 0.6748 |
| 0.5915 | 6.25 | 1200 | 0.5994 | 0.6663 | 0.6745 |
| 0.5844 | 7.29 | 1400 | 0.6025 | 0.6727 | 0.6742 |
| 0.5824 | 8.33 | 1600 | 0.6039 | 0.6751 | 0.6764 |
| 0.5792 | 9.38 | 1800 | 0.6118 | 0.6720 | 0.6699 |
| 0.5753 | 10.42 | 2000 | 0.5926 | 0.6798 | 0.6875 |
| 0.575 | 11.46 | 2200 | 0.5912 | 0.6809 | 0.6869 |
| 0.5641 | 12.5 | 2400 | 0.5905 | 0.6838 | 0.6875 |
| 0.5619 | 13.54 | 2600 | 0.5914 | 0.6821 | 0.6852 |
| 0.5613 | 14.58 | 2800 | 0.5963 | 0.6792 | 0.6852 |
| 0.5629 | 15.62 | 3000 | 0.5991 | 0.6801 | 0.6823 |
| 0.5555 | 16.67 | 3200 | 0.5909 | 0.6881 | 0.6898 |
| 0.5535 | 17.71 | 3400 | 0.5917 | 0.6846 | 0.6875 |
| 0.5504 | 18.75 | 3600 | 0.5947 | 0.6876 | 0.6953 |
| 0.5497 | 19.79 | 3800 | 0.5970 | 0.6926 | 0.6947 |
| 0.5426 | 20.83 | 4000 | 0.5979 | 0.6873 | 0.6885 |
| 0.5442 | 21.88 | 4200 | 0.6118 | 0.6855 | 0.6839 |
| 0.5419 | 22.92 | 4400 | 0.6027 | 0.6879 | 0.6898 |
| 0.5412 | 23.96 | 4600 | 0.6037 | 0.6882 | 0.6875 |
| 0.5382 | 25.0 | 4800 | 0.6052 | 0.6881 | 0.6882 |
| 0.5318 | 26.04 | 5000 | 0.6095 | 0.6861 | 0.6859 |
| 0.5315 | 27.08 | 5200 | 0.6105 | 0.6846 | 0.6836 |
| 0.5292 | 28.12 | 5400 | 0.6067 | 0.6856 | 0.6862 |
| 0.527 | 29.17 | 5600 | 0.6062 | 0.6890 | 0.6895 |
| 0.5219 | 30.21 | 5800 | 0.6187 | 0.6903 | 0.6898 |
| 0.5243 | 31.25 | 6000 | 0.6131 | 0.6895 | 0.6891 |
| 0.5184 | 32.29 | 6200 | 0.6067 | 0.6894 | 0.6924 |
| 0.5204 | 33.33 | 6400 | 0.6196 | 0.6910 | 0.6895 |
| 0.5193 | 34.38 | 6600 | 0.6086 | 0.6923 | 0.6950 |
| 0.5179 | 35.42 | 6800 | 0.6108 | 0.6929 | 0.6940 |
| 0.5166 | 36.46 | 7000 | 0.6078 | 0.6878 | 0.6898 |
| 0.5112 | 37.5 | 7200 | 0.6146 | 0.6900 | 0.6901 |
| 0.5084 | 38.54 | 7400 | 0.6157 | 0.6921 | 0.6934 |
| 0.5122 | 39.58 | 7600 | 0.6130 | 0.6883 | 0.6911 |
| 0.5104 | 40.62 | 7800 | 0.6234 | 0.6849 | 0.6843 |
| 0.5106 | 41.67 | 8000 | 0.6163 | 0.6912 | 0.6921 |
| 0.5039 | 42.71 | 8200 | 0.6181 | 0.6899 | 0.6911 |
| 0.5064 | 43.75 | 8400 | 0.6206 | 0.6891 | 0.6888 |
| 0.5071 | 44.79 | 8600 | 0.6204 | 0.6871 | 0.6865 |
| 0.5018 | 45.83 | 8800 | 0.6169 | 0.6884 | 0.6898 |
| 0.5034 | 46.88 | 9000 | 0.6281 | 0.6864 | 0.6852 |
| 0.501 | 47.92 | 9200 | 0.6240 | 0.6899 | 0.6895 |
| 0.504 | 48.96 | 9400 | 0.6217 | 0.6918 | 0.6921 |
| 0.4986 | 50.0 | 9600 | 0.6262 | 0.6884 | 0.6878 |
| 0.5038 | 51.04 | 9800 | 0.6240 | 0.6892 | 0.6888 |
| 0.497 | 52.08 | 10000 | 0.6238 | 0.6884 | 0.6882 |
### 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_H3K4me2-seqsight_4096_512_27M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_27M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T01:06:55+00:00 |
null | null | {"license": "openrail"} | AITony70100/OtherRVCModels | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-26T01:06:59+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 ๐ค 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": []} | sid-du/bar | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:09:02+00:00 |
null | null | {} | rbys/trackBeta | null | [
"region:us"
]
| null | 2024-04-26T01:09:56+00:00 |
|
question-answering | transformers | {} | lanzv/ClinicalBERTPRQABmbert_280_111_CS | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:10:40+00:00 |
|
null | null | ## laser-dolphin-mixtral-llamafile-nonAVX
llamafile lets you distribute and run LLMs with a single file. [announcement blog post](https://hacks.mozilla.org/2023/11/introducing-llamafile/)
#### Downloads
- [laser-dolphin-mixtral-2x7b-dpo.Q4_0.llamafile](https://huggingface.co/blueprintninja/laser-dolphin-mixtral-llamafile-nonAVX/resolve/main/laser-dolphin-mixtral-2x7b-dpo.Q4_0.llamafile)
This repository was created using the [llamafile-builder](https://github.com/rabilrbl/llamafile-builder)
| {"tags": ["llamafile", "GGUF"], "base_model": "TheBloke/laser-dolphin-mixtral-2x7b-dpo-GGUF"} | blueprintninja/laser-dolphin-mixtral-llamafile-nonAVX | null | [
"llamafile",
"GGUF",
"base_model:TheBloke/laser-dolphin-mixtral-2x7b-dpo-GGUF",
"region:us"
]
| null | 2024-04-26T01:10:52+00:00 |
text-generation | mlx |
# mlx-community/Llama-3-8B-Instruct-262k-unquantized
This model was converted to MLX format from [`gradientai/Llama-3-8B-Instruct-262k`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3-8B-Instruct-262k-unquantized")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en"], "tags": ["meta", "llama-3", "mlx"], "pipeline_tag": "text-generation"} | mlx-community/Llama-3-8B-Instruct-262k-unquantized | null | [
"mlx",
"safetensors",
"llama",
"meta",
"llama-3",
"text-generation",
"conversational",
"en",
"region:us"
]
| null | 2024-04-26T01:10:56+00:00 |
text-generation | transformers | Quantizations of https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1
# 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-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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-v0.1", "transformers"], "inference": false, "pipeline_tag": "text-generation"} | duyntnet/Mistral-7B-Instruct-v0.1-imatrix-GGUF | null | [
"transformers",
"gguf",
"imatrix",
"mistralai",
"Mistral-7B-Instruct-v0.1",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-26T01:11:21+00:00 |
text-classification | transformers | ## Model Details
Model Name: ivilson/llama3-8b-chinese-function-calling
Architecture: Llama3-8B (Chinese)
Author: [ivilson.com]
Date Created: [2024-04-26]
License: [Specify the license under which the model is released]
Repository: [Link to the repository containing the finetuned model]
# Model Description
This model is a finetuned version of the Llama3-8B model developed by Meta. It has been fine-tuned to better suit specific use cases, such as conversational AI applications. | {"language": ["en", "zh"], "license": "apache-2.0", "tags": ["llama3", "chinese", "function-calling"], "pipeline_tag": "text-classification"} | ivilson/llama3-8b-chinese-function-calling | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama3",
"chinese",
"function-calling",
"text-classification",
"en",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:12:00+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Arogyasami
- **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"} | Arogyasami/Llama-3-8b-text2sql-finetune | 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 | 2024-04-26T01:13:47+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** mahiatlinux
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-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-Instruct-bnb-4bit"} | mahiatlinux/MasherAI-7B-v6.2-test1 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:16:44+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_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) 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: 5e-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": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.001_3iters_bs256_declr_nodpo_userresponse_iter_1", "results": []}]} | ShenaoZ/0.001_3iters_bs256_declr_nodpo_userresponse_iter_1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:19:16+00:00 |
null | null | {} | PorsiempreAmor1/sn25_v0 | null | [
"region:us"
]
| null | 2024-04-26T01:20:42+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. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [roneneldan/TinyStories-33M](https://huggingface.co/roneneldan/TinyStories-33M) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1326
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.3154 | 1.0 | 1315 | 5.2050 |
| 4.9237 | 2.0 | 2630 | 5.1270 |
| 4.7149 | 3.0 | 3945 | 5.1326 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "roneneldan/TinyStories-33M", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]} | tian-yu/my_awesome_eli5_clm-model | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:roneneldan/TinyStories-33M",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:20:57+00:00 |
null | null | {"license": "mit"} | Mryean/dingzhen | null | [
"license:mit",
"region:us"
]
| null | 2024-04-26T01:22:02+00:00 |
|
null | null | {} | guoxin123/9am | null | [
"region:us"
]
| null | 2024-04-26T01:22:47+00:00 |
|
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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#### 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
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "google/flan-t5-base"} | Talhat/peft-customer-support-checkpoint-local | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-base",
"region:us"
]
| null | 2024-04-26T01:23:10+00:00 |
null | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
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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
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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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]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | deadcode99/mistral-7b-lime-only-question-aware-agnostic | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:23:53+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 ๐ค 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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- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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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]
### 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]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | yamaguchi-kota/gemma-medical_qa-Finetune-ja | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:27:16+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_H3K4me2-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_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5949
- F1 Score: 0.6839
- Accuracy: 0.6878
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6409 | 1.04 | 200 | 0.6184 | 0.6026 | 0.6504 |
| 0.6064 | 2.08 | 400 | 0.6190 | 0.6615 | 0.6595 |
| 0.5951 | 3.12 | 600 | 0.6004 | 0.6678 | 0.6764 |
| 0.5895 | 4.17 | 800 | 0.6050 | 0.6793 | 0.6787 |
| 0.5801 | 5.21 | 1000 | 0.5997 | 0.6878 | 0.6914 |
| 0.5755 | 6.25 | 1200 | 0.5991 | 0.6803 | 0.6807 |
| 0.5652 | 7.29 | 1400 | 0.5953 | 0.6830 | 0.6839 |
| 0.559 | 8.33 | 1600 | 0.6070 | 0.6793 | 0.6794 |
| 0.5545 | 9.38 | 1800 | 0.6209 | 0.6696 | 0.6673 |
| 0.5437 | 10.42 | 2000 | 0.6082 | 0.6790 | 0.6826 |
| 0.5399 | 11.46 | 2200 | 0.6055 | 0.6850 | 0.6878 |
| 0.5223 | 12.5 | 2400 | 0.6109 | 0.6777 | 0.6790 |
| 0.517 | 13.54 | 2600 | 0.6232 | 0.6755 | 0.6755 |
| 0.5127 | 14.58 | 2800 | 0.6216 | 0.6676 | 0.6696 |
| 0.5066 | 15.62 | 3000 | 0.6352 | 0.6745 | 0.6745 |
| 0.4981 | 16.67 | 3200 | 0.6354 | 0.6778 | 0.6768 |
| 0.4887 | 17.71 | 3400 | 0.6474 | 0.6728 | 0.6712 |
| 0.4796 | 18.75 | 3600 | 0.6602 | 0.6838 | 0.6836 |
| 0.4743 | 19.79 | 3800 | 0.6663 | 0.6761 | 0.6748 |
| 0.4614 | 20.83 | 4000 | 0.6546 | 0.6835 | 0.6846 |
| 0.4594 | 21.88 | 4200 | 0.6713 | 0.6783 | 0.6768 |
| 0.4484 | 22.92 | 4400 | 0.6771 | 0.6794 | 0.6794 |
| 0.4455 | 23.96 | 4600 | 0.6753 | 0.6770 | 0.6764 |
| 0.4396 | 25.0 | 4800 | 0.6984 | 0.6748 | 0.6729 |
| 0.4283 | 26.04 | 5000 | 0.7016 | 0.6819 | 0.6800 |
| 0.4211 | 27.08 | 5200 | 0.7140 | 0.6754 | 0.6732 |
| 0.4205 | 28.12 | 5400 | 0.6967 | 0.6823 | 0.6826 |
| 0.4104 | 29.17 | 5600 | 0.7204 | 0.6745 | 0.6738 |
| 0.3996 | 30.21 | 5800 | 0.7498 | 0.6766 | 0.6748 |
| 0.3999 | 31.25 | 6000 | 0.7578 | 0.6770 | 0.6758 |
| 0.3899 | 32.29 | 6200 | 0.7307 | 0.6867 | 0.6862 |
| 0.3935 | 33.33 | 6400 | 0.7358 | 0.6754 | 0.6738 |
| 0.3883 | 34.38 | 6600 | 0.7536 | 0.6786 | 0.6790 |
| 0.3794 | 35.42 | 6800 | 0.7663 | 0.6816 | 0.6804 |
| 0.3754 | 36.46 | 7000 | 0.7614 | 0.6730 | 0.6706 |
| 0.3693 | 37.5 | 7200 | 0.7532 | 0.6822 | 0.6820 |
| 0.3678 | 38.54 | 7400 | 0.7647 | 0.6797 | 0.6794 |
| 0.3652 | 39.58 | 7600 | 0.7776 | 0.6761 | 0.6774 |
| 0.3632 | 40.62 | 7800 | 0.7953 | 0.6764 | 0.6748 |
| 0.3576 | 41.67 | 8000 | 0.7864 | 0.6782 | 0.6771 |
| 0.3507 | 42.71 | 8200 | 0.7908 | 0.6802 | 0.6790 |
| 0.3513 | 43.75 | 8400 | 0.7920 | 0.6786 | 0.6768 |
| 0.3477 | 44.79 | 8600 | 0.8070 | 0.6794 | 0.6777 |
| 0.342 | 45.83 | 8800 | 0.8093 | 0.6752 | 0.6738 |
| 0.3381 | 46.88 | 9000 | 0.8259 | 0.6755 | 0.6735 |
| 0.3366 | 47.92 | 9200 | 0.8202 | 0.6798 | 0.6784 |
| 0.339 | 48.96 | 9400 | 0.8158 | 0.6802 | 0.6790 |
| 0.3374 | 50.0 | 9600 | 0.8225 | 0.6807 | 0.6790 |
| 0.3383 | 51.04 | 9800 | 0.8214 | 0.6772 | 0.6755 |
| 0.3304 | 52.08 | 10000 | 0.8196 | 0.6775 | 0.6761 |
### 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_H3K4me2-seqsight_4096_512_27M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_27M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T01:27:40+00:00 |
text-generation | transformers |
# Model Card for alokabhishek/Meta-Llama-3-8B-Instruct-5.0-bpw-exl2
<!-- Provide a quick summary of what the model is/does. -->
This repo contains 5-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 5 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-5.0-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-5.0-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 โ 8B and 70B parameters โ 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 Metaโs 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. Weโve 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, Metaโs cybersecurity safety eval suite, measuring Llama 3โs propensity to suggest insecure code when used as a coding assistant, and Llama 3โs 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 modelโs 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 3โs 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": ["5bit", "llama", "llama-3", "facebook", "meta", "8b", "quantized", "ExLlamaV2", "quantized", "exl2", "5.0-bpw"], "license_name": "llama3", "license_link": "LICENSE", "pipeline_tag": "text-generation"} | alokabhishek/Meta-Llama-3-8B-Instruct-5.0-bpw-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"5bit",
"llama-3",
"facebook",
"meta",
"8b",
"quantized",
"ExLlamaV2",
"exl2",
"5.0-bpw",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:29:49+00:00 |
text-generation | transformers |
# Keiana-L3-Test5.1-8B-7
Keiana-L3-Test5.1-8B-7 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.
* [Kaoeiri/Keiana-L3-Test4.7-8B-3](https://huggingface.co/Kaoeiri/Keiana-L3-Test4.7-8B-3)
* [VisionForge/Alien-8B-v1.6-DPO](https://huggingface.co/VisionForge/Alien-8B-v1.6-DPO)
* [cgato/L3-TheSpice-8b-v0.8.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3)
## ๐งฉ Configuration
```yaml
merge_method: model_stock
dtype: float16
base_model: jeiku/Average_Normie_v2_l3_8B
models:
- model: Kaoeiri/Keiana-L3-Test4.7-8B-3
parameters:
weight: 1.0
- model: VisionForge/Alien-8B-v1.6-DPO
parameters:
weight: .5
density: .5
- model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
weight: .5
density: .5
```
## ๐ป Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kaoeiri/Keiana-L3-Test5.1-8B-7"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test4.7-8B-3", "VisionForge/Alien-8B-v1.6-DPO", "cgato/L3-TheSpice-8b-v0.8.3"], "base_model": ["Kaoeiri/Keiana-L3-Test4.7-8B-3", "VisionForge/Alien-8B-v1.6-DPO", "cgato/L3-TheSpice-8b-v0.8.3"]} | Kaoeiri/Keiana-L3-Test5.1-8B-7 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Kaoeiri/Keiana-L3-Test4.7-8B-3",
"VisionForge/Alien-8B-v1.6-DPO",
"cgato/L3-TheSpice-8b-v0.8.3",
"conversational",
"base_model:Kaoeiri/Keiana-L3-Test4.7-8B-3",
"base_model:VisionForge/Alien-8B-v1.6-DPO",
"base_model:cgato/L3-TheSpice-8b-v0.8.3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:30:19+00:00 |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Asubramanian19/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | Asubramanian19/ppo-Huggy | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| null | 2024-04-26T01:30:22+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_H3K9ac-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_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4717
- F1 Score: 0.7868
- Accuracy: 0.7863
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5922 | 1.15 | 200 | 0.5464 | 0.7354 | 0.7352 |
| 0.5418 | 2.3 | 400 | 0.5354 | 0.7376 | 0.7373 |
| 0.5212 | 3.45 | 600 | 0.5330 | 0.7400 | 0.7398 |
| 0.5173 | 4.6 | 800 | 0.5288 | 0.7415 | 0.7413 |
| 0.5102 | 5.75 | 1000 | 0.5149 | 0.7488 | 0.7488 |
| 0.5075 | 6.9 | 1200 | 0.5166 | 0.7461 | 0.7460 |
| 0.5005 | 8.05 | 1400 | 0.5122 | 0.7476 | 0.7474 |
| 0.4999 | 9.2 | 1600 | 0.5222 | 0.7446 | 0.7445 |
| 0.4936 | 10.34 | 1800 | 0.5134 | 0.7511 | 0.7510 |
| 0.4929 | 11.49 | 2000 | 0.5100 | 0.7562 | 0.7560 |
| 0.4863 | 12.64 | 2200 | 0.5233 | 0.7488 | 0.7485 |
| 0.4906 | 13.79 | 2400 | 0.5071 | 0.7611 | 0.7607 |
| 0.4864 | 14.94 | 2600 | 0.5070 | 0.7598 | 0.7596 |
| 0.4827 | 16.09 | 2800 | 0.5060 | 0.7600 | 0.7596 |
| 0.4834 | 17.24 | 3000 | 0.5121 | 0.7555 | 0.7549 |
| 0.4791 | 18.39 | 3200 | 0.5094 | 0.7580 | 0.7575 |
| 0.4822 | 19.54 | 3400 | 0.5026 | 0.7597 | 0.7593 |
| 0.476 | 20.69 | 3600 | 0.5030 | 0.7578 | 0.7578 |
| 0.4775 | 21.84 | 3800 | 0.5062 | 0.7562 | 0.7557 |
| 0.4727 | 22.99 | 4000 | 0.5032 | 0.7562 | 0.7557 |
| 0.4751 | 24.14 | 4200 | 0.5015 | 0.7563 | 0.7564 |
| 0.4689 | 25.29 | 4400 | 0.5101 | 0.7558 | 0.7553 |
| 0.4743 | 26.44 | 4600 | 0.5035 | 0.7556 | 0.7553 |
| 0.4701 | 27.59 | 4800 | 0.5099 | 0.7554 | 0.7549 |
| 0.4659 | 28.74 | 5000 | 0.5047 | 0.7608 | 0.7603 |
| 0.472 | 29.89 | 5200 | 0.5037 | 0.7552 | 0.7549 |
| 0.464 | 31.03 | 5400 | 0.5065 | 0.7569 | 0.7564 |
| 0.4671 | 32.18 | 5600 | 0.4990 | 0.7584 | 0.7585 |
| 0.46 | 33.33 | 5800 | 0.5023 | 0.7549 | 0.7546 |
| 0.4713 | 34.48 | 6000 | 0.5012 | 0.7576 | 0.7571 |
| 0.4603 | 35.63 | 6200 | 0.4992 | 0.7546 | 0.7542 |
| 0.4671 | 36.78 | 6400 | 0.5016 | 0.7612 | 0.7607 |
| 0.4608 | 37.93 | 6600 | 0.5045 | 0.7587 | 0.7582 |
| 0.4609 | 39.08 | 6800 | 0.4998 | 0.7578 | 0.7575 |
| 0.4589 | 40.23 | 7000 | 0.5013 | 0.7604 | 0.7600 |
| 0.4576 | 41.38 | 7200 | 0.4998 | 0.7576 | 0.7571 |
| 0.46 | 42.53 | 7400 | 0.5011 | 0.7601 | 0.7596 |
| 0.4585 | 43.68 | 7600 | 0.5005 | 0.7608 | 0.7603 |
| 0.4547 | 44.83 | 7800 | 0.5036 | 0.7573 | 0.7567 |
| 0.4567 | 45.98 | 8000 | 0.5011 | 0.7576 | 0.7571 |
| 0.4554 | 47.13 | 8200 | 0.5035 | 0.7587 | 0.7582 |
| 0.4568 | 48.28 | 8400 | 0.5010 | 0.7587 | 0.7582 |
| 0.4555 | 49.43 | 8600 | 0.5018 | 0.7579 | 0.7575 |
| 0.4575 | 50.57 | 8800 | 0.4999 | 0.7550 | 0.7546 |
| 0.4568 | 51.72 | 9000 | 0.5044 | 0.7576 | 0.7571 |
| 0.4521 | 52.87 | 9200 | 0.5039 | 0.7591 | 0.7585 |
| 0.4572 | 54.02 | 9400 | 0.5020 | 0.7623 | 0.7618 |
| 0.4566 | 55.17 | 9600 | 0.5023 | 0.7598 | 0.7593 |
| 0.4489 | 56.32 | 9800 | 0.5016 | 0.7590 | 0.7585 |
| 0.4561 | 57.47 | 10000 | 0.5019 | 0.7598 | 0.7593 |
### 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_H3K9ac-seqsight_4096_512_27M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_27M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T01:31:02+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_H3K9ac-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_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4724
- F1 Score: 0.7891
- Accuracy: 0.7888
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5785 | 1.15 | 200 | 0.5373 | 0.7392 | 0.7395 |
| 0.5241 | 2.3 | 400 | 0.5557 | 0.7156 | 0.7182 |
| 0.5016 | 3.45 | 600 | 0.5175 | 0.7470 | 0.7467 |
| 0.4955 | 4.6 | 800 | 0.5121 | 0.7513 | 0.7510 |
| 0.4863 | 5.75 | 1000 | 0.4987 | 0.7528 | 0.7528 |
| 0.4808 | 6.9 | 1200 | 0.4964 | 0.7645 | 0.7643 |
| 0.4728 | 8.05 | 1400 | 0.5007 | 0.7540 | 0.7535 |
| 0.4697 | 9.2 | 1600 | 0.5130 | 0.7480 | 0.7485 |
| 0.4614 | 10.34 | 1800 | 0.4928 | 0.7619 | 0.7618 |
| 0.4591 | 11.49 | 2000 | 0.4899 | 0.7751 | 0.7747 |
| 0.4518 | 12.64 | 2200 | 0.5026 | 0.7561 | 0.7557 |
| 0.4518 | 13.79 | 2400 | 0.4824 | 0.7641 | 0.7639 |
| 0.4487 | 14.94 | 2600 | 0.4871 | 0.7629 | 0.7629 |
| 0.4436 | 16.09 | 2800 | 0.4958 | 0.7624 | 0.7621 |
| 0.4421 | 17.24 | 3000 | 0.5024 | 0.7671 | 0.7668 |
| 0.4374 | 18.39 | 3200 | 0.4935 | 0.7644 | 0.7639 |
| 0.4384 | 19.54 | 3400 | 0.4842 | 0.7687 | 0.7683 |
| 0.4305 | 20.69 | 3600 | 0.4836 | 0.7739 | 0.7737 |
| 0.4304 | 21.84 | 3800 | 0.4969 | 0.7616 | 0.7614 |
| 0.4233 | 22.99 | 4000 | 0.5082 | 0.7577 | 0.7578 |
| 0.4259 | 24.14 | 4200 | 0.4871 | 0.7675 | 0.7672 |
| 0.4163 | 25.29 | 4400 | 0.5013 | 0.7652 | 0.7647 |
| 0.421 | 26.44 | 4600 | 0.4956 | 0.7687 | 0.7683 |
| 0.4179 | 27.59 | 4800 | 0.5048 | 0.7662 | 0.7657 |
| 0.4097 | 28.74 | 5000 | 0.5025 | 0.7658 | 0.7654 |
| 0.4177 | 29.89 | 5200 | 0.4967 | 0.7687 | 0.7683 |
| 0.4078 | 31.03 | 5400 | 0.5071 | 0.7647 | 0.7643 |
| 0.4062 | 32.18 | 5600 | 0.5002 | 0.7695 | 0.7693 |
| 0.401 | 33.33 | 5800 | 0.5047 | 0.7658 | 0.7654 |
| 0.4106 | 34.48 | 6000 | 0.4980 | 0.7694 | 0.7690 |
| 0.3981 | 35.63 | 6200 | 0.4958 | 0.7716 | 0.7711 |
| 0.4036 | 36.78 | 6400 | 0.4974 | 0.7702 | 0.7697 |
| 0.3973 | 37.93 | 6600 | 0.5105 | 0.7687 | 0.7683 |
| 0.3966 | 39.08 | 6800 | 0.5017 | 0.7683 | 0.7679 |
| 0.3935 | 40.23 | 7000 | 0.5107 | 0.7673 | 0.7668 |
| 0.3897 | 41.38 | 7200 | 0.5127 | 0.7691 | 0.7686 |
| 0.3916 | 42.53 | 7400 | 0.5086 | 0.7684 | 0.7679 |
| 0.3894 | 43.68 | 7600 | 0.5081 | 0.7727 | 0.7722 |
| 0.3849 | 44.83 | 7800 | 0.5134 | 0.7698 | 0.7693 |
| 0.3876 | 45.98 | 8000 | 0.5090 | 0.7686 | 0.7683 |
| 0.386 | 47.13 | 8200 | 0.5134 | 0.7670 | 0.7665 |
| 0.3829 | 48.28 | 8400 | 0.5146 | 0.7691 | 0.7686 |
| 0.3843 | 49.43 | 8600 | 0.5132 | 0.7684 | 0.7679 |
| 0.3865 | 50.57 | 8800 | 0.5128 | 0.7670 | 0.7665 |
| 0.3875 | 51.72 | 9000 | 0.5171 | 0.7691 | 0.7686 |
| 0.3798 | 52.87 | 9200 | 0.5173 | 0.7680 | 0.7675 |
| 0.381 | 54.02 | 9400 | 0.5197 | 0.7666 | 0.7661 |
| 0.3835 | 55.17 | 9600 | 0.5176 | 0.7673 | 0.7668 |
| 0.3736 | 56.32 | 9800 | 0.5173 | 0.7677 | 0.7672 |
| 0.379 | 57.47 | 10000 | 0.5165 | 0.7691 | 0.7686 |
### 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_H3K9ac-seqsight_4096_512_27M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_27M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T01:31:02+00:00 |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"} | cgihlstorf/NEW_finetuned_llama27b32_1_0.0003_alternate_RANDOM_50_pct | null | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
]
| null | 2024-04-26T01:31:08+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 [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2206
- Accuracy: 0.9458
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2163 | 1.0 | 1563 | 0.1776 | 0.9352 |
| 0.1552 | 2.0 | 3126 | 0.2206 | 0.9458 |
### 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": "albert-base-v2", "model-index": [{"name": "my_awesome_model", "results": []}]} | dlwnsdnjs/my_awesome_model | null | [
"transformers",
"tensorboard",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:31:45+00:00 |
token-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. -->
# privacy-300k-masking
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3729
- Overall Precision: 0.2655
- Overall Recall: 0.1856
- Overall F1: 0.2185
- Overall Accuracy: 0.8664
- Bod F1: 0.2060
- Building F1: 0.2527
- Cardissuer F1: 0.0
- City F1: 0.2253
- Country F1: 0.2800
- Date F1: 0.2289
- Driverlicense F1: 0.1902
- Email F1: 0.2350
- Geocoord F1: 0.1572
- Givenname1 F1: 0.2029
- Givenname2 F1: 0.1330
- Idcard F1: 0.2208
- Ip F1: 0.1826
- Lastname1 F1: 0.1877
- Lastname2 F1: 0.0937
- Lastname3 F1: 0.0328
- Pass F1: 0.1950
- Passport F1: 0.2256
- Postcode F1: 0.2518
- Secaddress F1: 0.2101
- Sex F1: 0.2636
- Socialnumber F1: 0.1891
- State F1: 0.2639
- Street F1: 0.1915
- Tel F1: 0.2077
- Time F1: 0.2551
- Title F1: 0.2453
- Username F1: 0.2325
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Bod F1 | Building F1 | Cardissuer F1 | City F1 | Country F1 | Date F1 | Driverlicense F1 | Email F1 | Geocoord F1 | Givenname1 F1 | Givenname2 F1 | Idcard F1 | Ip F1 | Lastname1 F1 | Lastname2 F1 | Lastname3 F1 | Pass F1 | Passport F1 | Postcode F1 | Secaddress F1 | Sex F1 | Socialnumber F1 | State F1 | Street F1 | Tel F1 | Time F1 | Title F1 | Username F1 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:-----------:|:-------------:|:-------:|:----------:|:-------:|:----------------:|:--------:|:-----------:|:-------------:|:-------------:|:---------:|:------:|:------------:|:------------:|:------------:|:-------:|:-----------:|:-----------:|:-------------:|:------:|:---------------:|:--------:|:---------:|:------:|:-------:|:--------:|:-----------:|
| 0.3954 | 1.0 | 88839 | 0.3729 | 0.2655 | 0.1856 | 0.2185 | 0.8664 | 0.2060 | 0.2527 | 0.0 | 0.2253 | 0.2800 | 0.2289 | 0.1902 | 0.2350 | 0.1572 | 0.2029 | 0.1330 | 0.2208 | 0.1826 | 0.1877 | 0.0937 | 0.0328 | 0.1950 | 0.2256 | 0.2518 | 0.2101 | 0.2636 | 0.1891 | 0.2639 | 0.1915 | 0.2077 | 0.2551 | 0.2453 | 0.2325 |
### 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"], "base_model": "distilbert-base-multilingual-cased", "model-index": [{"name": "privacy-300k-masking", "results": []}]} | taro-pudding/privacy-300k-masking | null | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:31:48+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** wallaceblaia
- **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"} | wallaceblaia/ICM-llama3-new | null | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"8-bit",
"region:us"
]
| null | 2024-04-26T01:32:48+00:00 |
question-answering | transformers | {} | lanzv/ClinicalBERTPRQABmbert_280_992_CS | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:35:00+00:00 |
|
null | null | {"license": "openrail"} | RUXHIR2828/aintnoangel | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-26T01:36:40+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 ๐ค 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": []} | ZeroWater93/whisper-large-v2-korea-common_13 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:36:57+00:00 |
null | null | {} | wcvz/esm2_t12_35M-lora-binding-sites_2024-04-25_21-39-27 | null | [
"region:us"
]
| null | 2024-04-26T01:39:27+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_2
This model is a fine-tuned version of [ShenaoZ/0.001_4iters_bs128_declr_nodpo_useresponse_iter_1](https://huggingface.co/ShenaoZ/0.001_4iters_bs128_declr_nodpo_useresponse_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: 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_1", "model-index": [{"name": "0.001_4iters_bs128_declr_nodpo_useresponse_iter_2", "results": []}]} | ShenaoZ/0.001_4iters_bs128_declr_nodpo_useresponse_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_4iters_bs128_declr_nodpo_useresponse_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:41:43+00:00 |
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-25_21-43-52 | null | [
"region:us"
]
| null | 2024-04-26T01:43:52+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_H3K9ac-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_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5158
- F1 Score: 0.7887
- Accuracy: 0.7884
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5672 | 1.15 | 200 | 0.5398 | 0.7328 | 0.7323 |
| 0.5116 | 2.3 | 400 | 0.5291 | 0.7314 | 0.7319 |
| 0.4886 | 3.45 | 600 | 0.5211 | 0.7391 | 0.7395 |
| 0.4783 | 4.6 | 800 | 0.4993 | 0.7605 | 0.7600 |
| 0.4708 | 5.75 | 1000 | 0.4916 | 0.7641 | 0.7636 |
| 0.4622 | 6.9 | 1200 | 0.4925 | 0.7655 | 0.7650 |
| 0.4537 | 8.05 | 1400 | 0.5063 | 0.7541 | 0.7539 |
| 0.4446 | 9.2 | 1600 | 0.5172 | 0.7543 | 0.7546 |
| 0.4349 | 10.34 | 1800 | 0.4886 | 0.7651 | 0.7647 |
| 0.4309 | 11.49 | 2000 | 0.4933 | 0.7744 | 0.7740 |
| 0.4192 | 12.64 | 2200 | 0.4940 | 0.7619 | 0.7614 |
| 0.4134 | 13.79 | 2400 | 0.4908 | 0.7665 | 0.7661 |
| 0.4076 | 14.94 | 2600 | 0.5012 | 0.7615 | 0.7611 |
| 0.3977 | 16.09 | 2800 | 0.5238 | 0.7503 | 0.7503 |
| 0.3895 | 17.24 | 3000 | 0.5337 | 0.7644 | 0.7639 |
| 0.3835 | 18.39 | 3200 | 0.5185 | 0.7600 | 0.7596 |
| 0.3789 | 19.54 | 3400 | 0.5012 | 0.7651 | 0.7647 |
| 0.3658 | 20.69 | 3600 | 0.5210 | 0.7657 | 0.7654 |
| 0.3638 | 21.84 | 3800 | 0.5241 | 0.7666 | 0.7665 |
| 0.3524 | 22.99 | 4000 | 0.5697 | 0.7600 | 0.7603 |
| 0.3505 | 24.14 | 4200 | 0.5404 | 0.7633 | 0.7629 |
| 0.338 | 25.29 | 4400 | 0.5435 | 0.7761 | 0.7758 |
| 0.3362 | 26.44 | 4600 | 0.5620 | 0.7606 | 0.7603 |
| 0.3279 | 27.59 | 4800 | 0.5528 | 0.7626 | 0.7621 |
| 0.3155 | 28.74 | 5000 | 0.5597 | 0.7519 | 0.7513 |
| 0.3234 | 29.89 | 5200 | 0.5640 | 0.7630 | 0.7625 |
| 0.3099 | 31.03 | 5400 | 0.5730 | 0.7643 | 0.7639 |
| 0.305 | 32.18 | 5600 | 0.5840 | 0.7644 | 0.7639 |
| 0.2976 | 33.33 | 5800 | 0.5979 | 0.7619 | 0.7614 |
| 0.2993 | 34.48 | 6000 | 0.5943 | 0.7630 | 0.7625 |
| 0.2879 | 35.63 | 6200 | 0.6107 | 0.7619 | 0.7614 |
| 0.2906 | 36.78 | 6400 | 0.6017 | 0.7640 | 0.7636 |
| 0.2807 | 37.93 | 6600 | 0.6108 | 0.7644 | 0.7639 |
| 0.2782 | 39.08 | 6800 | 0.6142 | 0.7598 | 0.7593 |
| 0.2687 | 40.23 | 7000 | 0.6358 | 0.7619 | 0.7614 |
| 0.2699 | 41.38 | 7200 | 0.6313 | 0.7691 | 0.7686 |
| 0.2678 | 42.53 | 7400 | 0.6318 | 0.7626 | 0.7621 |
| 0.2613 | 43.68 | 7600 | 0.6388 | 0.7651 | 0.7647 |
| 0.2615 | 44.83 | 7800 | 0.6437 | 0.7633 | 0.7629 |
| 0.2571 | 45.98 | 8000 | 0.6331 | 0.7633 | 0.7629 |
| 0.2543 | 47.13 | 8200 | 0.6566 | 0.7647 | 0.7643 |
| 0.2543 | 48.28 | 8400 | 0.6453 | 0.7662 | 0.7657 |
| 0.2437 | 49.43 | 8600 | 0.6646 | 0.7547 | 0.7542 |
| 0.2494 | 50.57 | 8800 | 0.6668 | 0.7604 | 0.7600 |
| 0.2438 | 51.72 | 9000 | 0.6744 | 0.7626 | 0.7621 |
| 0.2421 | 52.87 | 9200 | 0.6779 | 0.7601 | 0.7596 |
| 0.2413 | 54.02 | 9400 | 0.6754 | 0.7615 | 0.7611 |
| 0.2367 | 55.17 | 9600 | 0.6842 | 0.7601 | 0.7596 |
| 0.2335 | 56.32 | 9800 | 0.6803 | 0.7626 | 0.7621 |
| 0.2377 | 57.47 | 10000 | 0.6763 | 0.7612 | 0.7607 |
### 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_H3K9ac-seqsight_4096_512_27M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_27M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T01:46:06+00:00 |
null | transformers |
# n00854180t/BuRPris-Remix-7B-Q4_K_M-GGUF
This model was converted to GGUF format from [`n00854180t/BuRPris-Remix-7B`](https://huggingface.co/n00854180t/BuRPris-Remix-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/n00854180t/BuRPris-Remix-7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo n00854180t/BuRPris-Remix-7B-Q4_K_M-GGUF --model burpris-remix-7b.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo n00854180t/BuRPris-Remix-7B-Q4_K_M-GGUF --model burpris-remix-7b.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m burpris-remix-7b.Q4_K_M.gguf -n 128
```
| {"library_name": "transformers", "tags": ["mergekit", "merge", "llama-cpp", "gguf-my-repo"], "base_model": ["ChaoticNeutrals/Eris_Remix_7B", "ChaoticNeutrals/BuRP_7B"]} | n00854180t/BuRPris-Remix-7B-Q4_K_M-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:ChaoticNeutrals/Eris_Remix_7B",
"base_model:ChaoticNeutrals/BuRP_7B",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:46:30+00:00 |
null | null | {} | vinnystop/eafton | null | [
"region:us"
]
| null | 2024-04-26T01:46:51+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_2024-04-25_21-48-08
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: 0.5189
- Accuracy: 0.8809
## 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: 12
- eval_batch_size: 12
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6192 | 1.0 | 128 | 0.6737 | 0.6055 |
| 0.4321 | 2.0 | 256 | 0.6507 | 0.6289 |
| 0.571 | 3.0 | 384 | 0.5572 | 0.7188 |
| 0.3053 | 4.0 | 512 | 0.5090 | 0.7852 |
| 0.5055 | 5.0 | 640 | 0.3370 | 0.8516 |
| 0.2786 | 6.0 | 768 | 0.3710 | 0.8594 |
| 0.1327 | 7.0 | 896 | 0.3055 | 0.8711 |
| 0.2127 | 8.0 | 1024 | 0.2891 | 0.8945 |
| 0.0913 | 9.0 | 1152 | 0.3454 | 0.8691 |
| 0.0134 | 10.0 | 1280 | 0.3354 | 0.8809 |
| 0.2597 | 11.0 | 1408 | 0.3436 | 0.8848 |
| 0.0276 | 12.0 | 1536 | 0.4181 | 0.8633 |
| 0.0929 | 13.0 | 1664 | 0.3722 | 0.8789 |
| 0.9377 | 14.0 | 1792 | 0.5086 | 0.8730 |
| 0.2894 | 15.0 | 1920 | 0.3311 | 0.8906 |
| 0.3138 | 16.0 | 2048 | 0.4739 | 0.8809 |
| 0.0088 | 17.0 | 2176 | 0.3875 | 0.8867 |
| 0.3591 | 18.0 | 2304 | 0.4032 | 0.8809 |
| 0.0436 | 19.0 | 2432 | 0.4316 | 0.8887 |
| 0.0037 | 20.0 | 2560 | 0.4931 | 0.8789 |
| 0.0322 | 21.0 | 2688 | 0.4787 | 0.8809 |
| 0.0035 | 22.0 | 2816 | 0.4460 | 0.8770 |
| 0.0859 | 23.0 | 2944 | 0.4914 | 0.8828 |
| 0.039 | 24.0 | 3072 | 0.4955 | 0.8770 |
| 0.4208 | 25.0 | 3200 | 0.5211 | 0.8828 |
| 0.1874 | 26.0 | 3328 | 0.5376 | 0.8711 |
| 0.4433 | 27.0 | 3456 | 0.5319 | 0.875 |
| 0.2976 | 28.0 | 3584 | 0.5201 | 0.8809 |
| 0.0223 | 29.0 | 3712 | 0.5179 | 0.8809 |
| 0.0021 | 30.0 | 3840 | 0.5189 | 0.8809 |
### 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_2024-04-25_21-48-08", "results": []}]} | wcvz/esm2_t130_150M-lora-classifier_2024-04-25_21-48-08 | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/esm2_t30_150M_UR50D",
"license:mit",
"region:us"
]
| null | 2024-04-26T01:48:08+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. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0802 | 1.0 | 1376 | 0.0075 |
| 0.0073 | 2.0 | 2752 | 0.0008 |
| 0.0036 | 3.0 | 4128 | 0.0005 |
### 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"], "datasets": ["eli5_category"], "base_model": "bert-base-uncased", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]} | MSheridan1414/my_awesome_eli5_clm-model | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:49:23+00:00 |
null | null | {"license": "llama3"} | valvic/donald | null | [
"license:llama3",
"region:us"
]
| null | 2024-04-26T01:49:44+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 ๐ค 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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- **Shared by [optional]:** [More Information Needed]
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### 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]
### 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": []} | thusinh1969/LLaMA-2-finetune-100k-plus-trans-DPO-3ep-26APRIL2024 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:49:48+00:00 |
text-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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Jairnetojp/content-moderation-bert | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:50:30+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: Grayx/sad_llama_38
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": ["Grayx/sad_llama_38", "deepnet/SN6-67L2"]} | Sumail/Chalice3 | 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 | 2024-04-26T01:52:50+00:00 |
null | null | {"license": "apache-2.0"} | Norton0924/castflow | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T01:57:16+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 ๐ค 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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<!-- 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
<!-- 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]
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[More Information Needed]
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| {"library_name": "transformers", "tags": []} | abhayesian/lat-poisoned-1 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T01:57:48+00:00 |
text-generation | 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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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## Glossary [optional]
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| {"library_name": "transformers", "tags": []} | hugozanini/gemma-demo-ir-test | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T01:59:29+00:00 |
null | null | {} | RichardErkhov/cognitivecomputations_-_dolphin-2.2-70b-gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-26T02:01:28+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 ๐ค 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. -->
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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]
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#### 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. -->
[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": []} | zandfj/LLaMA2-7B-Chat-lora-nq-tet-robust-042609-moren | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T02:02:32+00:00 |
null | null | ## laser-dolphin-mixtral-q2-llamafile-nonAVX
llamafile lets you distribute and run LLMs with a single file. [announcement blog post](https://hacks.mozilla.org/2023/11/introducing-llamafile/)
#### Downloads
- [laser-dolphin-mixtral-2x7b-dpo.q2_k.llamafile](https://huggingface.co/blueprintninja/laser-dolphin-mixtral-q2-llamafile-nonAVX/resolve/main/laser-dolphin-mixtral-2x7b-dpo.q2_k.llamafile)
This repository was created using the [llamafile-builder](https://github.com/rabilrbl/llamafile-builder)
| {"tags": ["llamafile", "GGUF"], "base_model": "macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF"} | blueprintninja/laser-dolphin-mixtral-q2-llamafile-nonAVX | null | [
"llamafile",
"GGUF",
"base_model:macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF",
"region:us"
]
| null | 2024-04-26T02:02:34+00:00 |
null | transformers |
# n00854180t/BuRPris-Remix-7B-Q6_K-GGUF
This model was converted to GGUF format from [`n00854180t/BuRPris-Remix-7B`](https://huggingface.co/n00854180t/BuRPris-Remix-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/n00854180t/BuRPris-Remix-7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo n00854180t/BuRPris-Remix-7B-Q6_K-GGUF --model burpris-remix-7b.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo n00854180t/BuRPris-Remix-7B-Q6_K-GGUF --model burpris-remix-7b.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m burpris-remix-7b.Q6_K.gguf -n 128
```
| {"library_name": "transformers", "tags": ["mergekit", "merge", "llama-cpp", "gguf-my-repo"], "base_model": ["ChaoticNeutrals/Eris_Remix_7B", "ChaoticNeutrals/BuRP_7B"]} | n00854180t/BuRPris-Remix-7B-Q6_K-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:ChaoticNeutrals/Eris_Remix_7B",
"base_model:ChaoticNeutrals/BuRP_7B",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T02:04:55+00:00 |
null | null | {"license": "llama3"} | sophiesun333/first | null | [
"license:llama3",
"region:us"
]
| null | 2024-04-26T02:05:34+00:00 |
|
null | null | {} | sm09-dev/BadDream | null | [
"region:us"
]
| null | 2024-04-26T02:06:14+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. -->
# GUE_EMP_H3K4me3-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_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5844
- F1 Score: 0.6895
- Accuracy: 0.6897
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6607 | 0.87 | 200 | 0.6386 | 0.6425 | 0.6424 |
| 0.6298 | 1.74 | 400 | 0.6146 | 0.6649 | 0.6668 |
| 0.6163 | 2.61 | 600 | 0.6043 | 0.6716 | 0.6717 |
| 0.6067 | 3.48 | 800 | 0.6013 | 0.6710 | 0.6717 |
| 0.6033 | 4.35 | 1000 | 0.6032 | 0.6620 | 0.6666 |
| 0.5989 | 5.22 | 1200 | 0.6007 | 0.6729 | 0.675 |
| 0.5954 | 6.09 | 1400 | 0.5970 | 0.6767 | 0.6780 |
| 0.5924 | 6.96 | 1600 | 0.5932 | 0.6793 | 0.6793 |
| 0.5876 | 7.83 | 1800 | 0.5975 | 0.6698 | 0.6731 |
| 0.5891 | 8.7 | 2000 | 0.5911 | 0.6868 | 0.6867 |
| 0.5839 | 9.57 | 2200 | 0.5896 | 0.6808 | 0.6813 |
| 0.5841 | 10.43 | 2400 | 0.5892 | 0.6824 | 0.6821 |
| 0.5828 | 11.3 | 2600 | 0.5846 | 0.6878 | 0.6875 |
| 0.5812 | 12.17 | 2800 | 0.5870 | 0.6851 | 0.6851 |
| 0.5803 | 13.04 | 3000 | 0.5851 | 0.6846 | 0.6848 |
| 0.5796 | 13.91 | 3200 | 0.5844 | 0.6862 | 0.6859 |
| 0.5775 | 14.78 | 3400 | 0.5862 | 0.6867 | 0.6864 |
| 0.5764 | 15.65 | 3600 | 0.5845 | 0.6921 | 0.6918 |
| 0.5731 | 16.52 | 3800 | 0.5873 | 0.6867 | 0.6864 |
| 0.5733 | 17.39 | 4000 | 0.5855 | 0.6847 | 0.6845 |
| 0.5754 | 18.26 | 4200 | 0.5863 | 0.6865 | 0.6867 |
| 0.5698 | 19.13 | 4400 | 0.5882 | 0.6837 | 0.6840 |
| 0.5698 | 20.0 | 4600 | 0.5849 | 0.6921 | 0.6918 |
| 0.5701 | 20.87 | 4800 | 0.5873 | 0.6867 | 0.6870 |
| 0.5688 | 21.74 | 5000 | 0.5855 | 0.6878 | 0.6875 |
| 0.5692 | 22.61 | 5200 | 0.5831 | 0.6877 | 0.6875 |
| 0.5676 | 23.48 | 5400 | 0.5873 | 0.6858 | 0.6859 |
| 0.5668 | 24.35 | 5600 | 0.5857 | 0.6880 | 0.6878 |
| 0.5679 | 25.22 | 5800 | 0.5853 | 0.6914 | 0.6913 |
| 0.5633 | 26.09 | 6000 | 0.5846 | 0.6891 | 0.6889 |
| 0.5667 | 26.96 | 6200 | 0.5851 | 0.6901 | 0.6899 |
| 0.5651 | 27.83 | 6400 | 0.5863 | 0.6895 | 0.6897 |
| 0.5632 | 28.7 | 6600 | 0.5862 | 0.6885 | 0.6883 |
| 0.5641 | 29.57 | 6800 | 0.5847 | 0.6891 | 0.6891 |
| 0.5619 | 30.43 | 7000 | 0.5833 | 0.6872 | 0.6870 |
| 0.5661 | 31.3 | 7200 | 0.5841 | 0.6875 | 0.6872 |
| 0.5636 | 32.17 | 7400 | 0.5836 | 0.6915 | 0.6913 |
| 0.5606 | 33.04 | 7600 | 0.5831 | 0.6901 | 0.6899 |
| 0.5617 | 33.91 | 7800 | 0.5831 | 0.6870 | 0.6867 |
| 0.5618 | 34.78 | 8000 | 0.5843 | 0.6941 | 0.6940 |
| 0.562 | 35.65 | 8200 | 0.5838 | 0.6879 | 0.6878 |
| 0.5597 | 36.52 | 8400 | 0.5838 | 0.6905 | 0.6902 |
| 0.5637 | 37.39 | 8600 | 0.5839 | 0.6924 | 0.6924 |
| 0.5584 | 38.26 | 8800 | 0.5846 | 0.6921 | 0.6918 |
| 0.5615 | 39.13 | 9000 | 0.5829 | 0.6921 | 0.6918 |
| 0.5607 | 40.0 | 9200 | 0.5834 | 0.6909 | 0.6908 |
| 0.5616 | 40.87 | 9400 | 0.5842 | 0.6904 | 0.6905 |
| 0.5584 | 41.74 | 9600 | 0.5837 | 0.6901 | 0.6899 |
| 0.5626 | 42.61 | 9800 | 0.5835 | 0.6906 | 0.6905 |
| 0.5579 | 43.48 | 10000 | 0.5836 | 0.6889 | 0.6889 |
### 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_H3K4me3-seqsight_4096_512_27M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_27M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T02:06:53+00:00 |
text-classification | transformers | {} | samuelcolvin26/Electra_Hatespeech_Classifier2 | null | [
"transformers",
"safetensors",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T02:07:15+00:00 |
|
text-classification | transformers | {} | samuelcolvin26/Electra_Hatespeech_Classifier6 | null | [
"transformers",
"safetensors",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T02:09:13+00:00 |
|
text2text-generation | transformers |
*Author - Hayden Beadles*
This model is meant to evaluate the results of creating an Encoder / Decoder generative model using SciBERT. The model is then finetuned on 30000 samples of the PubMedQA dataset. Instead of being finetuned
on the columns **question** and **final_answer**, where **final_answer** is a set of yes / no answers, we instead fine tune on the more challenging **long_answer** column, which gives a short answer
to the question.
The model was fine-tuned over 3 epochs, using the Adam learning rate scheduler, with a max length of 128 tokens.
The results are to help gauge SciBERT's abilities to answer (generate an answer) directly to a question, with no context provided. It is meant to evaluate the overall models training and attention towards
a more focused topic, to see if SciBERTs base training gives it any advantages.
| {"language": ["en"], "license": "mit", "tags": ["medical"], "datasets": ["qiaojin/PubMedQA"], "pipeline_tag": "text2text-generation"} | GeorgiaTech/scibert-generative-pubmedqa | null | [
"transformers",
"safetensors",
"encoder-decoder",
"text2text-generation",
"medical",
"en",
"dataset:qiaojin/PubMedQA",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T02:09:24+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_H3K4me3-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_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5958
- F1 Score: 0.7043
- Accuracy: 0.7041
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6442 | 0.87 | 200 | 0.6088 | 0.6694 | 0.6709 |
| 0.6038 | 1.74 | 400 | 0.5999 | 0.6703 | 0.6720 |
| 0.5888 | 2.61 | 600 | 0.5899 | 0.6771 | 0.6769 |
| 0.58 | 3.48 | 800 | 0.5846 | 0.6891 | 0.6889 |
| 0.5734 | 4.35 | 1000 | 0.5855 | 0.6858 | 0.6856 |
| 0.566 | 5.22 | 1200 | 0.5861 | 0.6879 | 0.6883 |
| 0.5592 | 6.09 | 1400 | 0.6004 | 0.6675 | 0.6726 |
| 0.5528 | 6.96 | 1600 | 0.5831 | 0.6975 | 0.6973 |
| 0.5468 | 7.83 | 1800 | 0.5869 | 0.6916 | 0.6918 |
| 0.5408 | 8.7 | 2000 | 0.5853 | 0.6942 | 0.6940 |
| 0.5313 | 9.57 | 2200 | 0.5812 | 0.6943 | 0.6940 |
| 0.5284 | 10.43 | 2400 | 0.5869 | 0.6981 | 0.6986 |
| 0.5205 | 11.3 | 2600 | 0.5838 | 0.6981 | 0.6986 |
| 0.5197 | 12.17 | 2800 | 0.5819 | 0.6984 | 0.6984 |
| 0.5102 | 13.04 | 3000 | 0.5881 | 0.7025 | 0.7022 |
| 0.5064 | 13.91 | 3200 | 0.5890 | 0.7025 | 0.7022 |
| 0.4994 | 14.78 | 3400 | 0.5991 | 0.7047 | 0.7046 |
| 0.4921 | 15.65 | 3600 | 0.5972 | 0.6928 | 0.6929 |
| 0.4844 | 16.52 | 3800 | 0.5934 | 0.7000 | 0.6997 |
| 0.4793 | 17.39 | 4000 | 0.6109 | 0.6966 | 0.6967 |
| 0.4791 | 18.26 | 4200 | 0.6061 | 0.6954 | 0.6954 |
| 0.4688 | 19.13 | 4400 | 0.6198 | 0.6957 | 0.6954 |
| 0.468 | 20.0 | 4600 | 0.6074 | 0.6955 | 0.6954 |
| 0.4594 | 20.87 | 4800 | 0.6235 | 0.6944 | 0.6943 |
| 0.4554 | 21.74 | 5000 | 0.6168 | 0.6926 | 0.6927 |
| 0.4473 | 22.61 | 5200 | 0.6199 | 0.6938 | 0.6937 |
| 0.441 | 23.48 | 5400 | 0.6325 | 0.6959 | 0.6957 |
| 0.4375 | 24.35 | 5600 | 0.6323 | 0.6940 | 0.6937 |
| 0.4345 | 25.22 | 5800 | 0.6439 | 0.6945 | 0.6943 |
| 0.4306 | 26.09 | 6000 | 0.6414 | 0.6937 | 0.6935 |
| 0.4285 | 26.96 | 6200 | 0.6390 | 0.6918 | 0.6918 |
| 0.415 | 27.83 | 6400 | 0.6581 | 0.6989 | 0.6986 |
| 0.4202 | 28.7 | 6600 | 0.6678 | 0.6897 | 0.6905 |
| 0.4152 | 29.57 | 6800 | 0.6551 | 0.6925 | 0.6924 |
| 0.4031 | 30.43 | 7000 | 0.6555 | 0.6921 | 0.6918 |
| 0.4062 | 31.3 | 7200 | 0.6808 | 0.6966 | 0.6965 |
| 0.4019 | 32.17 | 7400 | 0.6750 | 0.6904 | 0.6902 |
| 0.3966 | 33.04 | 7600 | 0.6606 | 0.6967 | 0.6965 |
| 0.3928 | 33.91 | 7800 | 0.6714 | 0.6951 | 0.6948 |
| 0.3876 | 34.78 | 8000 | 0.6823 | 0.6911 | 0.6910 |
| 0.3914 | 35.65 | 8200 | 0.6865 | 0.6944 | 0.6943 |
| 0.383 | 36.52 | 8400 | 0.6883 | 0.6929 | 0.6927 |
| 0.3879 | 37.39 | 8600 | 0.6892 | 0.6917 | 0.6918 |
| 0.381 | 38.26 | 8800 | 0.6769 | 0.6924 | 0.6921 |
| 0.3829 | 39.13 | 9000 | 0.6888 | 0.6929 | 0.6929 |
| 0.3803 | 40.0 | 9200 | 0.6893 | 0.6922 | 0.6921 |
| 0.3737 | 40.87 | 9400 | 0.6964 | 0.6920 | 0.6918 |
| 0.3786 | 41.74 | 9600 | 0.6916 | 0.6955 | 0.6954 |
| 0.3756 | 42.61 | 9800 | 0.6951 | 0.6920 | 0.6918 |
| 0.3725 | 43.48 | 10000 | 0.6937 | 0.6944 | 0.6943 |
### 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_H3K4me3-seqsight_4096_512_27M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_27M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T02:09:44+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_H3K4me3-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_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5819
- F1 Score: 0.6918
- Accuracy: 0.6921
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6499 | 0.87 | 200 | 0.6234 | 0.6597 | 0.6609 |
| 0.613 | 1.74 | 400 | 0.6032 | 0.6720 | 0.6717 |
| 0.6001 | 2.61 | 600 | 0.5957 | 0.6779 | 0.6777 |
| 0.5921 | 3.48 | 800 | 0.5927 | 0.6793 | 0.6791 |
| 0.5865 | 4.35 | 1000 | 0.5910 | 0.6834 | 0.6837 |
| 0.5818 | 5.22 | 1200 | 0.5926 | 0.6735 | 0.6755 |
| 0.578 | 6.09 | 1400 | 0.5996 | 0.6656 | 0.6701 |
| 0.573 | 6.96 | 1600 | 0.5879 | 0.6859 | 0.6861 |
| 0.5692 | 7.83 | 1800 | 0.5941 | 0.6806 | 0.6818 |
| 0.5681 | 8.7 | 2000 | 0.5893 | 0.6878 | 0.6875 |
| 0.5616 | 9.57 | 2200 | 0.5873 | 0.6841 | 0.6853 |
| 0.5617 | 10.43 | 2400 | 0.5832 | 0.6902 | 0.6899 |
| 0.5573 | 11.3 | 2600 | 0.5798 | 0.6943 | 0.6940 |
| 0.5568 | 12.17 | 2800 | 0.5795 | 0.6925 | 0.6924 |
| 0.5525 | 13.04 | 3000 | 0.5847 | 0.6925 | 0.6929 |
| 0.5499 | 13.91 | 3200 | 0.5820 | 0.6904 | 0.6902 |
| 0.5449 | 14.78 | 3400 | 0.5828 | 0.6934 | 0.6932 |
| 0.5455 | 15.65 | 3600 | 0.5797 | 0.6976 | 0.6973 |
| 0.5401 | 16.52 | 3800 | 0.5841 | 0.6905 | 0.6910 |
| 0.5375 | 17.39 | 4000 | 0.5843 | 0.6951 | 0.6948 |
| 0.5398 | 18.26 | 4200 | 0.5813 | 0.6989 | 0.6986 |
| 0.5318 | 19.13 | 4400 | 0.5881 | 0.6910 | 0.6910 |
| 0.5339 | 20.0 | 4600 | 0.5788 | 0.6996 | 0.6995 |
| 0.531 | 20.87 | 4800 | 0.5830 | 0.6924 | 0.6921 |
| 0.5297 | 21.74 | 5000 | 0.5833 | 0.6967 | 0.6965 |
| 0.5279 | 22.61 | 5200 | 0.5805 | 0.6945 | 0.6943 |
| 0.5236 | 23.48 | 5400 | 0.5857 | 0.6998 | 0.7003 |
| 0.5235 | 24.35 | 5600 | 0.5854 | 0.6970 | 0.6967 |
| 0.5222 | 25.22 | 5800 | 0.5874 | 0.6916 | 0.6913 |
| 0.5186 | 26.09 | 6000 | 0.5885 | 0.6970 | 0.6967 |
| 0.5203 | 26.96 | 6200 | 0.5830 | 0.6958 | 0.6959 |
| 0.5152 | 27.83 | 6400 | 0.5897 | 0.6915 | 0.6916 |
| 0.5177 | 28.7 | 6600 | 0.5873 | 0.6948 | 0.6948 |
| 0.5127 | 29.57 | 6800 | 0.5854 | 0.6905 | 0.6902 |
| 0.5106 | 30.43 | 7000 | 0.5822 | 0.6946 | 0.6943 |
| 0.5143 | 31.3 | 7200 | 0.5869 | 0.6908 | 0.6905 |
| 0.5117 | 32.17 | 7400 | 0.5875 | 0.6912 | 0.6910 |
| 0.5076 | 33.04 | 7600 | 0.5870 | 0.6939 | 0.6940 |
| 0.5057 | 33.91 | 7800 | 0.5850 | 0.6938 | 0.6935 |
| 0.5079 | 34.78 | 8000 | 0.5912 | 0.6902 | 0.6905 |
| 0.5067 | 35.65 | 8200 | 0.5892 | 0.6923 | 0.6921 |
| 0.5045 | 36.52 | 8400 | 0.5890 | 0.6940 | 0.6937 |
| 0.5084 | 37.39 | 8600 | 0.5894 | 0.6898 | 0.6897 |
| 0.4998 | 38.26 | 8800 | 0.5895 | 0.6937 | 0.6935 |
| 0.5043 | 39.13 | 9000 | 0.5885 | 0.6891 | 0.6889 |
| 0.5025 | 40.0 | 9200 | 0.5898 | 0.6921 | 0.6918 |
| 0.5024 | 40.87 | 9400 | 0.5915 | 0.6941 | 0.6940 |
| 0.5028 | 41.74 | 9600 | 0.5895 | 0.6923 | 0.6921 |
| 0.5078 | 42.61 | 9800 | 0.5898 | 0.6912 | 0.6910 |
| 0.4989 | 43.48 | 10000 | 0.5898 | 0.6904 | 0.6902 |
### 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_H3K4me3-seqsight_4096_512_27M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_27M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_27M",
"region:us"
]
| null | 2024-04-26T02:09:44+00:00 |
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