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TalentoTechIA/Andres_Yate | TalentoTechIA | "2025-01-21T01:20:58Z" | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2025-01-21T01:04:49Z" | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Andres_Yate
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Andres_Yate
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0430
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1315 | 3.8462 | 500 | 0.0430 | 0.9850 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t18_e75_member_shadow29 | FounderOfHuggingface | "2023-12-09T06:08:38Z" | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | "2023-12-09T06:08:36Z" | ---
library_name: peft
base_model: gpt2
---
# 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]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
genki10/Trial2BERT_AugV8_k1_task1_organization_sp010_lw030_fold3 | genki10 | "2025-04-05T04:15:20Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-05T00:13:16Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Trial2BERT_AugV8_k1_task1_organization_sp010_lw030_fold3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Trial2BERT_AugV8_k1_task1_organization_sp010_lw030_fold3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7858
- Qwk: 0.4765
- Mse: 0.7865
- Rmse: 0.8869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|
| No log | 1.0 | 2 | 12.3876 | 0.0003 | 12.3853 | 3.5193 |
| No log | 2.0 | 4 | 10.0854 | 0.0 | 10.0836 | 3.1755 |
| No log | 3.0 | 6 | 8.3285 | 0.0 | 8.3270 | 2.8856 |
| No log | 4.0 | 8 | 6.6446 | 0.0 | 6.6432 | 2.5774 |
| No log | 5.0 | 10 | 5.3420 | 0.0114 | 5.3407 | 2.3110 |
| No log | 6.0 | 12 | 4.5236 | 0.0038 | 4.5224 | 2.1266 |
| No log | 7.0 | 14 | 3.5103 | 0.0 | 3.5093 | 1.8733 |
| No log | 8.0 | 16 | 2.9920 | 0.0 | 2.9911 | 1.7295 |
| No log | 9.0 | 18 | 2.4277 | 0.1429 | 2.4270 | 1.5579 |
| No log | 10.0 | 20 | 2.0080 | 0.0811 | 2.0073 | 1.4168 |
| No log | 11.0 | 22 | 1.7159 | 0.0202 | 1.7151 | 1.3096 |
| No log | 12.0 | 24 | 1.4853 | 0.0102 | 1.4846 | 1.2184 |
| No log | 13.0 | 26 | 1.3528 | 0.0365 | 1.3523 | 1.1629 |
| No log | 14.0 | 28 | 1.3868 | 0.0488 | 1.3863 | 1.1774 |
| No log | 15.0 | 30 | 1.0994 | 0.0266 | 1.0990 | 1.0483 |
| No log | 16.0 | 32 | 0.9551 | 0.0102 | 0.9546 | 0.9770 |
| No log | 17.0 | 34 | 0.8592 | 0.3220 | 0.8588 | 0.9267 |
| No log | 18.0 | 36 | 0.9158 | 0.1485 | 0.9155 | 0.9568 |
| No log | 19.0 | 38 | 0.9861 | 0.1547 | 0.9859 | 0.9929 |
| No log | 20.0 | 40 | 0.6910 | 0.4646 | 0.6908 | 0.8311 |
| No log | 21.0 | 42 | 0.6774 | 0.4874 | 0.6773 | 0.8230 |
| No log | 22.0 | 44 | 1.3011 | 0.2557 | 1.3010 | 1.1406 |
| No log | 23.0 | 46 | 1.4226 | 0.2614 | 1.4227 | 1.1928 |
| No log | 24.0 | 48 | 0.9715 | 0.3915 | 0.9716 | 0.9857 |
| No log | 25.0 | 50 | 1.1647 | 0.3488 | 1.1649 | 1.0793 |
| No log | 26.0 | 52 | 1.5293 | 0.2560 | 1.5296 | 1.2368 |
| No log | 27.0 | 54 | 1.4284 | 0.2887 | 1.4289 | 1.1954 |
| No log | 28.0 | 56 | 0.8916 | 0.4404 | 0.8921 | 0.9445 |
| No log | 29.0 | 58 | 0.9939 | 0.4139 | 0.9946 | 0.9973 |
| No log | 30.0 | 60 | 1.6732 | 0.2354 | 1.6741 | 1.2939 |
| No log | 31.0 | 62 | 1.0965 | 0.3712 | 1.0973 | 1.0475 |
| No log | 32.0 | 64 | 0.7940 | 0.4271 | 0.7947 | 0.8915 |
| No log | 33.0 | 66 | 1.3744 | 0.2957 | 1.3752 | 1.1727 |
| No log | 34.0 | 68 | 1.7825 | 0.2177 | 1.7832 | 1.3353 |
| No log | 35.0 | 70 | 1.3501 | 0.2898 | 1.3508 | 1.1622 |
| No log | 36.0 | 72 | 0.7858 | 0.4765 | 0.7865 | 0.8869 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
MitchelHsu/alpaca-lora-7b | MitchelHsu | "2023-06-24T09:38:06Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-06-23T15:28:35Z" | ## Alpaca LoRA clone
This model was trained with the 52k alpaca dataset with the LoRA adapter. |
allenai/open-instruct-flan-v2-7b | allenai | "2023-06-20T17:50:44Z" | 21 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"arxiv:2306.04751",
"arxiv:2302.13971",
"arxiv:2301.13688",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-06-07T17:05:04Z" | ---
language:
- en
---
# Open-Instruct Flan V2 7B
This model is a 7B LLaMa model finetuned on the Flan V2 dataset. *Please note this is a model diff - see below for usage instructions*.
This was trained as part of the paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751).
The codebase used to train and evaluate this model can be found at [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct).
This model is licensed under the AI model license given in LICENSE.txt along with the original Llama license (llama_license.txt).
## Usage
We assume you have access to a LLaMa model in HF format already. You can find details on getting access and converting the model here:
[https://huggingface.co/docs/transformers/main/model_doc/llama](https://huggingface.co/docs/transformers/main/model_doc/llama)
Clone [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct) and install the required dependencies, or just copy `scripts/weight_diff.py`
and install the minimal requirements listed in `weight-diff-requirements.txt`. Then download or clone this model diff to the same machine.
Then, run:
```bash
python scripts/weight_diff.py recover --path_raw ${hf_llama_path} --path_tuned ${output_path} --path_diff ${diff_location}
```
And you will have a recovered model! Note this takes up a decent amount of RAM, especially for the larger models.
## Input Format
The model is trained to use the following format (note the newlines):
```
<|user|>
Your message here!
<|assistant|>
```
For best results, format all inputs in this manner.
## Performance
Here is the performance of this model across benchmarks explored in our paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751):
| MMLU 0-shot | MMLU 5-shot | GSM Direct | GSM CoT | BBH Direct | BBH CoT | TydiQA Gold-Passage | TydiQA Closed-book | Codex-Eval Pass@1 | Codex-Eval Pass@10 | AlpacaFarm vs Davinci-003 | Average |
|:-----------:|:-----------:|:----------:|:-------:|:----------:|:-------:|:-------------------:|:------------------:|:-----------------:|:------------------:|:-------------------------:|---------|
| 45.4 | 47.1 | 3.5 | 13.0 | 38.6 | 36.1 | 45.0 | 8.3 | 9.6 | 12.9 | 4.6 | 22.4 |
If you use this model, please cite our work, the llama paper, and the original dataset:
```
@misc{wang2023far,
title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources},
author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
year={2023},
eprint={2306.04751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
year={2023},
eprint={2302.13971},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@article{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V and Zoph, Barret and Wei, Jason and others},
journal={arXiv preprint arXiv:2301.13688},
year={2023}
}
``` |
Kfjjdjdjdhdhd/modelo-modificado-gemma-3-4b-it | Kfjjdjdjdhdhd | "2025-03-27T04:28:00Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | "2025-03-24T07:13:08Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] |
phd411r1/xlm-roberta-large-fa-qa-finetune_on_1020qa | phd411r1 | "2023-01-05T17:46:32Z" | 106 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | "2023-01-05T16:09:37Z" | ---
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large-fa-qa-finetune_on_1020qa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-fa-qa-finetune_on_1020qa
This model is a fine-tuned version of [SajjadAyoubi/xlm-roberta-large-fa-qa](https://huggingface.co/SajjadAyoubi/xlm-roberta-large-fa-qa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5578
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 460 | 1.6157 |
| 2.8726 | 2.0 | 920 | 0.8636 |
| 1.8777 | 3.0 | 1380 | 0.5578 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ancient41/8af5193a-522c-44d4-aa8f-baf653373378 | ancient41 | "2025-01-30T03:31:52Z" | 7 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | "2025-01-30T03:16:51Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8af5193a-522c-44d4-aa8f-baf653373378
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 5dd32cdee5c892d5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5dd32cdee5c892d5_train_data.json
type:
field_instruction: english_prompt
field_output: sql_statement
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: ancient41/8af5193a-522c-44d4-aa8f-baf653373378
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/5dd32cdee5c892d5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 132b9665-5e41-4e60-9e8b-87e501bd6138
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 132b9665-5e41-4e60-9e8b-87e501bd6138
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8af5193a-522c-44d4-aa8f-baf653373378
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0420
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4922 | 0.0003 | 1 | 2.5977 |
| 0.4016 | 0.0169 | 50 | 0.2359 |
| 0.1551 | 0.0337 | 100 | 0.0872 |
| 0.1064 | 0.0506 | 150 | 0.0486 |
| 0.1183 | 0.0675 | 200 | 0.0420 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF | mradermacher | "2024-11-22T18:20:47Z" | 16 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2024-11-22T12:36:19Z" | ---
base_model: SzilviaB/DarkestSthenoMaid-GrandHorror-16.5b
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/SzilviaB/DarkestSthenoMaid-GrandHorror-16.5b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ1_S.gguf) | i1-IQ1_S | 3.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ1_M.gguf) | i1-IQ1_M | 4.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ2_S.gguf) | i1-IQ2_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ2_M.gguf) | i1-IQ2_M | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q2_K.gguf) | i1-Q2_K | 6.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 7.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ3_S.gguf) | i1-IQ3_S | 7.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ3_M.gguf) | i1-IQ3_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 8.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q4_0.gguf) | i1-Q4_0 | 9.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 9.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 10.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 11.5 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 11.8 | |
| [GGUF](https://huggingface.co/mradermacher/DarkestSthenoMaid-GrandHorror-16.5b-i1-GGUF/resolve/main/DarkestSthenoMaid-GrandHorror-16.5b.i1-Q6_K.gguf) | i1-Q6_K | 13.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3_5e-5_lora2 | tyzhu | "2024-06-08T14:09:16Z" | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"dataset:tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"license:llama2",
"model-index",
"region:us"
] | null | "2024-06-07T18:21:00Z" | ---
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_doc_qa_v3_5e-5_lora2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
type: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
- name: Accuracy
type: accuracy
value: 0.19302564102564101
---
<!-- 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. -->
# lmind_nq_train6000_eval6489_v1_doc_qa_v3_5e-5_lora2
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7918
- Accuracy: 0.1930
## 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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 50.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.3891 | 1.0 | 529 | 0.6138 | 1.3015 |
| 1.3633 | 2.0 | 1058 | 0.6166 | 1.2855 |
| 1.2929 | 3.0 | 1587 | 0.6177 | 1.2954 |
| 1.2361 | 4.0 | 2116 | 0.6045 | 1.3489 |
| 1.1856 | 5.0 | 2645 | 0.6125 | 1.3968 |
| 1.1098 | 6.0 | 3174 | 0.6115 | 1.4721 |
| 1.0753 | 7.0 | 3703 | 0.6076 | 1.5798 |
| 1.0048 | 8.0 | 4232 | 0.6084 | 1.6042 |
| 0.9456 | 9.0 | 4761 | 0.5977 | 1.6843 |
| 0.8766 | 10.0 | 5290 | 0.6051 | 1.7829 |
| 0.8273 | 11.0 | 5819 | 0.6043 | 1.8060 |
| 0.7755 | 12.0 | 6348 | 0.6019 | 1.8729 |
| 0.715 | 13.0 | 6877 | 0.6017 | 1.9620 |
| 0.6804 | 14.0 | 7406 | 0.6009 | 2.0030 |
| 0.6277 | 15.0 | 7935 | 0.5998 | 2.0528 |
| 0.5733 | 16.0 | 8464 | 0.6012 | 2.0475 |
| 0.5409 | 17.0 | 8993 | 0.5749 | 2.0920 |
| 0.5024 | 18.0 | 9522 | 0.5986 | 2.1207 |
| 0.4699 | 19.0 | 10051 | 0.5993 | 2.1108 |
| 0.4367 | 20.0 | 10580 | 0.6005 | 2.1089 |
| 0.857 | 21.0 | 11109 | 0.5983 | 2.0215 |
| 3.7434 | 22.0 | 11638 | 0.2233 | 10.1186 |
| 7.7259 | 23.0 | 12167 | 0.1986 | 7.5379 |
| 4.2204 | 24.0 | 12696 | 0.5345 | 2.1568 |
| 0.7385 | 25.0 | 13225 | 0.5963 | 1.8229 |
| 1.1473 | 26.0 | 13754 | 0.5788 | 1.7570 |
| 2.0182 | 27.0 | 14283 | 0.5573 | 1.7293 |
| 2.2707 | 28.0 | 14812 | 0.4956 | 2.7017 |
| 4.1792 | 29.0 | 15341 | 0.3070 | 5.8288 |
| 7.7703 | 30.0 | 15870 | 0.1922 | 7.6619 |
| 7.7034 | 31.0 | 16399 | 0.1913 | 7.7003 |
| 7.9533 | 32.0 | 16928 | 0.1899 | 7.8667 |
| 7.8634 | 33.0 | 17457 | 0.1897 | 7.8134 |
| 7.8584 | 34.0 | 17986 | 0.1882 | 7.6760 |
| 7.824 | 35.0 | 18515 | 0.1888 | 7.7083 |
| 7.7446 | 36.0 | 19044 | 0.1888 | 7.6626 |
| 7.6708 | 37.0 | 19573 | 0.1886 | 7.5529 |
| 7.6733 | 38.0 | 20102 | 0.1903 | 7.5704 |
| 7.6271 | 39.0 | 20631 | 0.1949 | 7.5363 |
| 7.5886 | 40.0 | 21160 | 0.2130 | 7.4684 |
| 7.5514 | 41.0 | 21689 | 0.2077 | 7.4223 |
| 7.5205 | 42.0 | 22218 | 0.1946 | 7.3508 |
| 7.4577 | 43.0 | 22747 | 0.1951 | 7.1785 |
| 7.5021 | 44.0 | 23276 | 0.2092 | 6.6226 |
| 7.1133 | 45.0 | 23805 | 0.1994 | 6.4100 |
| 6.9682 | 46.0 | 24334 | 0.2250 | 6.3553 |
| 6.8891 | 47.0 | 24863 | 0.2224 | 6.3128 |
| 6.8621 | 48.0 | 25392 | 0.23 | 6.2465 |
| 6.8176 | 49.0 | 25921 | 0.2561 | 6.1966 |
| 6.9473 | 50.0 | 26450 | 0.1930 | 6.7918 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
gokuls/hBERTv2_new_pretrain_48_emb_com_wnli | gokuls | "2023-06-15T19:22:10Z" | 45 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-06-15T19:16:01Z" | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_new_pretrain_48_emb_com_wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
config: wnli
split: validation
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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. -->
# hBERTv2_new_pretrain_48_emb_com_wnli
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6868
- Accuracy: 0.5634
## 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-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9415 | 1.0 | 5 | 0.7306 | 0.4366 |
| 0.7146 | 2.0 | 10 | 0.7870 | 0.4366 |
| 0.7207 | 3.0 | 15 | 0.7136 | 0.4225 |
| 0.6988 | 4.0 | 20 | 0.7277 | 0.4366 |
| 0.7058 | 5.0 | 25 | 0.7434 | 0.4366 |
| 0.7171 | 6.0 | 30 | 0.6963 | 0.4366 |
| 0.7007 | 7.0 | 35 | 0.6897 | 0.5634 |
| 0.7085 | 8.0 | 40 | 0.6900 | 0.5634 |
| 0.7282 | 9.0 | 45 | 0.6929 | 0.5634 |
| 0.695 | 10.0 | 50 | 0.6970 | 0.4366 |
| 0.6939 | 11.0 | 55 | 0.6868 | 0.5634 |
| 0.6955 | 12.0 | 60 | 0.6904 | 0.5634 |
| 0.6934 | 13.0 | 65 | 0.7015 | 0.4366 |
| 0.6974 | 14.0 | 70 | 0.6964 | 0.4366 |
| 0.695 | 15.0 | 75 | 0.6904 | 0.5634 |
| 0.7003 | 16.0 | 80 | 0.6981 | 0.4366 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
|
adammandic87/e89d75b0-c964-4ea1-805b-a37c5cd798c0 | adammandic87 | "2025-01-12T18:07:48Z" | 22 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.3",
"base_model:adapter:lmsys/vicuna-7b-v1.3",
"region:us"
] | null | "2025-01-12T17:43:33Z" | ---
library_name: peft
base_model: lmsys/vicuna-7b-v1.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e89d75b0-c964-4ea1-805b-a37c5cd798c0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: lmsys/vicuna-7b-v1.3
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 14cc67dec48743d2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/14cc67dec48743d2_train_data.json
type:
field_instruction: category
field_output: title
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: adammandic87/e89d75b0-c964-4ea1-805b-a37c5cd798c0
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/14cc67dec48743d2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8528aa12-3fc6-4bfb-8bc9-b50ddb3a9d25
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8528aa12-3fc6-4bfb-8bc9-b50ddb3a9d25
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e89d75b0-c964-4ea1-805b-a37c5cd798c0
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6646
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.2477 | 0.0001 | 1 | 3.8212 |
| 3.5283 | 0.0002 | 3 | 3.8168 |
| 3.9859 | 0.0004 | 6 | 3.7827 |
| 3.3921 | 0.0006 | 9 | 3.6646 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
GreenBitAI/Qwen-1.5-0.5B-layer-mix-bpw-3.0 | GreenBitAI | "2024-04-30T14:26:14Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-07T20:27:54Z" | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
### Zero-shot Evaluation
We evaluate the zero-shot ability of low-bit quantized Qwen1.5 models using the `llm_eval` library and list the results below:
| **Repository (Qwen Family)** | **Avg Acc.** | **OpenBQ** | **ARC-E** | **Winogr.** | **HellaS.** | **ARC-C** | **PIQA** | **BoolQ** | **RACE** | **ANLI-R1** | **ANLI-R2** | **ANLI-R3** | **WiC** |
|:----------------------------------|:------------:|:------------:|:-----------:|:-------------:|:-------------:|:-----------:|:----------:|:-----------:|:-----------:|:-------------:|:-------------:|:-------------:|:---------:|
| `Qwen-1.5-0.5B-layer-mix-bpw-2.2` | 0.398 | 0.170 | 0.443 | 0.527 | 0.332 | 0.238 | 0.634 | 0.620 | 0.318 | 0.332 | 0.338 | 0.330 | 0.500 |
| `Qwen-1.5-0.5B-layer-mix-bpw-2.5` | 0.394 | 0.170 | 0.514 | 0.541 | 0.337 | 0.232 | 0.637 | 0.496 | 0.318 | 0.316 | 0.358 | 0.326 | 0.490 |
| `Qwen-1.5-0.5B-layer-mix-bpw-3.0` | 0.407 | 0.198 | 0.533 | 0.536 | 0.348 | 0.234 | 0.671 | 0.552 | 0.323 | 0.330 | 0.333 | 0.335 | 0.495 |
| `Qwen-1.5-1.8B-layer-mix-bpw-2.2` | 0.415 | 0.218 | 0.539 | 0.586 | 0.392 | 0.260 | 0.678 | 0.622 | 0.333 | 0.333 | 0.333 | 0.336 | 0.464 |
| `Qwen-1.5-1.8B-layer-mix-bpw-2.5` | 0.423 | 0.222 | 0.592 | 0.585 | 0.406 | 0.267 | 0.695 | 0.629 | 0.336 | 0.314 | 0.339 | 0.361 | 0.507 |
| `Qwen-1.5-1.8B-layer-mix-bpw-3.0` | 0.438 | 0.246 | 0.576 | 0.563 | 0.413 | 0.277 | 0.694 | 0.645 | 0.352 | 0.323 | 0.336 | 0.343 | 0.492 |
| `Qwen-1.5-4B-layer-mix-bpw-2.2` | 0.480 | 0.254 | 0.663 | 0.623 | 0.463 | 0.339 | 0.712 | 0.718 | 0.349 | 0.326 | 0.355 | 0.384 | 0.513 |
| `Qwen-1.5-4B-layer-mix-bpw-2.5` | 0.490 | 0.266 | 0.677 | 0.629 | 0.473 | 0.365 | 0.732 | 0.717 | 0.351 | 0.372 | 0.352 | 0.360 | 0.502 |
| `Qwen-1.5-4B-layer-mix-bpw-3.0` | 0.502 | 0.268 | 0.678 | 0.642 | 0.494 | 0.358 | 0.755 | 0.757 | 0.380 | 0.395 | 0.395 | 0.392 | 0.519 |
| `Qwen-1.5-7B-layer-mix-bpw-2.2` | 0.513 | 0.278 | 0.669 | 0.654 | 0.504 | 0.389 | 0.741 | 0.759 | 0.376 | 0.383 | 0.410 | 0.403 | 0.517 |
| `Qwen-1.5-7B-layer-mix-bpw-2.5` | 0.520 | 0.294 | 0.705 | 0.650 | 0.520 | 0.387 | 0.750 | 0.769 | 0.371 | 0.445 | 0.424 | 0.398 | 0.564 |
| `Qwen-1.5-7B-layer-mix-bpw-3.0` | 0.531 | 0.292 | 0.713 | 0.654 | 0.545 | 0.405 | 0.764 | 0.807 | 0.383 | 0.424 | 0.393 | 0.414 | 0.627 |
| `Qwen-1.5-14B-layer-mix-bpw-2.5` | 0.553 | 0.318 | 0.727 | 0.682 | 0.564 | 0.413 | 0.775 | 0.792 | 0.390 | 0.472 | 0.434 | 0.446 | 0.623 |
| `Qwen-1.5-32B-layer-mix-bpw-3.0` | 0.599 | 0.346 | 0.775 | 0.722 | 0.620 | 0.492 | 0.807 | 0.853 | 0.444 | 0.515 | 0.494 | 0.478 | 0.642 |
|
PrunaAI/Shashank-credgenics-llama-3b-lora-merged_16-bnb-8bit-smashed | PrunaAI | "2025-01-09T08:20:34Z" | 7 | 0 | null | [
"safetensors",
"llama",
"pruna-ai",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-09T08:16:50Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Shashank-credgenics/llama-3b-lora-merged_16
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Shashank-credgenics/llama-3b-lora-merged_16 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/Shashank-credgenics-llama-3b-lora-merged_16-bnb-8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("Shashank-credgenics/llama-3b-lora-merged_16")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Shashank-credgenics/llama-3b-lora-merged_16 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Do it by yourself [here](https://docs.pruna.ai/en/latest/setup/pip.html). |
huggingtweets/mitchellsolomo1 | huggingtweets | "2021-05-22T14:55:21Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-03-02T23:29:05Z" | ---
language: en
thumbnail: https://www.huggingtweets.com/mitchellsolomo1/1614098943754/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1354235179892674562/Ku6uOc6K_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Mitchell Solomon 🤖 AI Bot </div>
<div style="font-size: 15px">@mitchellsolomo1 bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@mitchellsolomo1's tweets](https://twitter.com/mitchellsolomo1).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 243 |
| Retweets | 38 |
| Short tweets | 25 |
| Tweets kept | 180 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3du8kd6m/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mitchellsolomo1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3duwyidn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3duwyidn/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mitchellsolomo1')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
allknowingroger/Calmex26-7B-slerp | allknowingroger | "2024-06-04T09:32:24Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"allknowingroger/MultiverseEx26-7B-slerp",
"allknowingroger/CalmExperiment-7B-slerp",
"base_model:allknowingroger/CalmExperiment-7B-slerp",
"base_model:merge:allknowingroger/CalmExperiment-7B-slerp",
"base_model:allknowingroger/MultiverseEx26-7B-slerp",
"base_model:merge:allknowingroger/MultiverseEx26-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-11T08:07:47Z" | ---
tags:
- merge
- mergekit
- lazymergekit
- allknowingroger/MultiverseEx26-7B-slerp
- allknowingroger/CalmExperiment-7B-slerp
base_model:
- allknowingroger/MultiverseEx26-7B-slerp
- allknowingroger/CalmExperiment-7B-slerp
license: apache-2.0
---
# Calmex26-7B-slerp
Calmex26-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [allknowingroger/MultiverseEx26-7B-slerp](https://huggingface.co/allknowingroger/MultiverseEx26-7B-slerp)
* [allknowingroger/CalmExperiment-7B-slerp](https://huggingface.co/allknowingroger/CalmExperiment-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: allknowingroger/MultiverseEx26-7B-slerp
layer_range: [0, 32]
- model: allknowingroger/CalmExperiment-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: allknowingroger/MultiverseEx26-7B-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "allknowingroger/Calmex26-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Sumail/Golden_Waves06_2b | Sumail | "2024-03-06T07:38:00Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Sumail/Bubble_bee04_2b",
"base_model:finetune:Sumail/Bubble_bee04_2b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-06T07:35:07Z" | ---
base_model:
- Sumail/Bubble_bee04_2b
- 0x0dad0/nous_nb00
library_name: transformers
tags:
- mergekit
- merge
---
# 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:
* [Sumail/Bubble_bee04_2b](https://huggingface.co/Sumail/Bubble_bee04_2b)
* [0x0dad0/nous_nb00](https://huggingface.co/0x0dad0/nous_nb00)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: 0x0dad0/nous_nb00
layer_range: [0, 18]
- model: Sumail/Bubble_bee04_2b
layer_range: [0, 18]
merge_method: slerp
base_model: 0x0dad0/nous_nb00
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: 0.75
dtype: bfloat16
```
|
jiinking/15_layer_GQA2_llama3B_model | jiinking | "2025-02-21T14:06:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-21T12:53:05Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
simonycl/llama-3-8b-instruct-agg-judge | simonycl | "2024-11-21T00:37:32Z" | 9 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"conversational",
"dataset:simonycl/Meta-Llama-3-8B-Instruct_ultrafeedback-Meta-Llama-3-8B-Instruct-annotate-start-0-end-1.0-judge-5",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-08-18T13:08:44Z" | ---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- simonycl/Meta-Llama-3-8B-Instruct_ultrafeedback-Meta-Llama-3-8B-Instruct-annotate-start-0-end-1.0-judge-5
model-index:
- name: llama-3-8b-instruct-agg-judge
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-3-8b-instruct-agg-judge
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the simonycl/Meta-Llama-3-8B-Instruct_ultrafeedback-Meta-Llama-3-8B-Instruct-annotate-start-0-end-1.0-judge-5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6390
- Rewards/chosen: -1.0532
- Rewards/rejected: -1.3037
- Rewards/accuracies: 0.6057
- Rewards/margins: 0.2506
- Logps/rejected: -280.7787
- Logps/chosen: -256.8969
- Logits/rejected: -1.4905
- Logits/chosen: -1.5260
## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6265 | 0.4264 | 400 | 0.6455 | -0.7831 | -0.9487 | 0.6504 | 0.1655 | -245.2767 | -229.8961 | -1.3679 | -1.4091 |
| 0.6053 | 0.8529 | 800 | 0.6390 | -1.0532 | -1.3037 | 0.6057 | 0.2506 | -280.7787 | -256.8969 | -1.4905 | -1.5260 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0
|
BobMcDear/swin_tiny_window7_in22k_ft_in1k_224 | BobMcDear | "2023-03-21T16:50:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2023-03-21T16:49:35Z" | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF | mradermacher | "2025-03-07T14:04:47Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"zh",
"base_model:caskcsg/Libra-Guard-Qwen2.5-7B-Instruct",
"base_model:quantized:caskcsg/Libra-Guard-Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-03-07T13:44:29Z" | ---
base_model: caskcsg/Libra-Guard-Qwen2.5-7B-Instruct
language:
- zh
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/caskcsg/Libra-Guard-Qwen2.5-7B-Instruct
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Libra-Guard-Qwen2.5-7B-Instruct-GGUF/resolve/main/Libra-Guard-Qwen2.5-7B-Instruct.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
finellm/tinyLlama | finellm | "2024-04-19T05:36:49Z" | 1 | 0 | peft | [
"peft",
"safetensors",
"llama",
"region:us"
] | null | "2024-04-19T05:33:55Z" | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
habanoz/Reinforce-pixelcopter-50k-1 | habanoz | "2023-01-13T21:53:13Z" | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-01-12T23:08:22Z" | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter-50k-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 45.90 +/- 36.37
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
John6666/cashmoney-anime-v10e-sdxl | John6666 | "2024-12-23T06:41:39Z" | 159 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"illustration",
"vibrant",
"detailed backgrounds",
"poses",
"lighting",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-10-26T12:09:19Z" | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- illustration
- vibrant
- detailed backgrounds
- poses
- lighting
- pony
---
Original model is [here](https://civitai.com/models/484571?modelVersionId=985127).
The author is [here](https://huggingface.co/advokat).
This model created by [advokat](https://civitai.com/user/advokat).
|
daniel-gordon/PPO-LunarLander | daniel-gordon | "2023-12-31T05:56:17Z" | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-12-31T05:55:53Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.96 +/- 40.52
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Helsinki-NLP/opus-mt-en-sn | Helsinki-NLP | "2023-08-16T11:31:10Z" | 130 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"en",
"sn",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2022-03-02T23:29:04Z" | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-sn
* source languages: en
* target languages: sn
* OPUS readme: [en-sn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sn/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-sn/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-sn/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-sn/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.sn | 38.0 | 0.646 |
|
Jeska/BertjeWDialData | Jeska | "2021-11-16T18:04:08Z" | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:04Z" | ---
tags:
- generated_from_trainer
model-index:
- name: BertjeWDialData
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BertjeWDialData
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2608
## 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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 297 | 2.2419 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
shawn1251/granite-guardian-3.1-2b-Q4_K_M-GGUF | shawn1251 | "2025-01-25T02:05:13Z" | 22 | 1 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:ibm-granite/granite-guardian-3.1-2b",
"base_model:quantized:ibm-granite/granite-guardian-3.1-2b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-01-25T02:04:59Z" | ---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: ibm-granite/granite-guardian-3.1-2b
---
# shawn1251/granite-guardian-3.1-2b-Q4_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-guardian-3.1-2b`](https://huggingface.co/ibm-granite/granite-guardian-3.1-2b) 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/ibm-granite/granite-guardian-3.1-2b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo shawn1251/granite-guardian-3.1-2b-Q4_K_M-GGUF --hf-file granite-guardian-3.1-2b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo shawn1251/granite-guardian-3.1-2b-Q4_K_M-GGUF --hf-file granite-guardian-3.1-2b-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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo shawn1251/granite-guardian-3.1-2b-Q4_K_M-GGUF --hf-file granite-guardian-3.1-2b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo shawn1251/granite-guardian-3.1-2b-Q4_K_M-GGUF --hf-file granite-guardian-3.1-2b-q4_k_m.gguf -c 2048
```
|
ZicoZ7/flux-03 | ZicoZ7 | "2025-01-20T22:06:51Z" | 23 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"license:apache-2.0",
"endpoints_compatible",
"diffusers:FluxPipeline",
"region:us"
] | text-to-image | "2025-01-19T20:36:17Z" | ---
license: apache-2.0
pipeline_tag: text-to-image
library_name: diffusers
--- |
roleplaiapp/OpenThinker-7B-Q4_K_M-GGUF | roleplaiapp | "2025-02-01T15:24:38Z" | 11 | 0 | transformers | [
"transformers",
"gguf",
"4-bit",
"Q4_K_M",
"llama-cpp",
"openthinker",
"text-generation",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | "2025-02-01T15:24:18Z" | ---
library_name: transformers
pipeline_tag: text-generation
tags:
- 4-bit
- Q4_K_M
- gguf
- llama-cpp
- openthinker
- text-generation
---
# roleplaiapp/OpenThinker-7B-Q4_K_M-GGUF
**Repo:** `roleplaiapp/OpenThinker-7B-Q4_K_M-GGUF`
**Original Model:** `OpenThinker-7B`
**Quantized File:** `OpenThinker-7B-Q4_K_M.gguf`
**Quantization:** `GGUF`
**Quantization Method:** `Q4_K_M`
## Overview
This is a GGUF Q4_K_M quantized version of OpenThinker-7B
## Quantization By
I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models.
I hope the community finds these quantizations useful.
Andrew Webby @ [RolePlai](https://roleplai.app/).
|
ReadyArt/Baptist-Christian-Bible-Expert-v2.0-8B_EXL2_6.0bpw_H8 | ReadyArt | "2025-04-14T16:23:18Z" | 0 | 0 | null | [
"safetensors",
"mistral",
"Baptist",
"Christian",
"Bible",
"Theology",
"Jesus",
"Seminary",
"SBC",
"Protestant",
"text-generation",
"conversational",
"en",
"base_model:sleepdeprived3/Baptist-Christian-Bible-Expert-v2.0-8B",
"base_model:quantized:sleepdeprived3/Baptist-Christian-Bible-Expert-v2.0-8B",
"license:other",
"6-bit",
"exl2",
"region:us"
] | text-generation | "2025-04-12T21:35:43Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
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memevis/HH00 | memevis | "2025-01-13T17:53:31Z" | 80 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-13T17:48:26Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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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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## Model Card Contact
[More Information Needed] |
TinToTin/ppo-CartPole-v1 | TinToTin | "2023-08-13T09:27:09Z" | 0 | 0 | null | [
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | "2023-08-13T09:24:39Z" | ---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 247.10 +/- 99.41
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Thineshan/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
beanslmao/helsinki-es-en-opus100-fine-tuned | beanslmao | "2024-03-12T23:20:36Z" | 28 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-es-en",
"base_model:finetune:Helsinki-NLP/opus-mt-es-en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-03-12T15:54:15Z" | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-es-en
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: helsinki-es-en-opus100-fine-tuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# helsinki-es-en-opus100-fine-tuned
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsinki-NLP/opus-mt-es-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2316
- Bleu: 44.6895
- Gen Len: 14.7492
## 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: 20
- eval_batch_size: 20
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.2941 | 1.0 | 28000 | 1.2361 | 44.4788 | 14.7579 |
| 1.2071 | 2.0 | 56000 | 1.2316 | 44.6895 | 14.7492 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
sultan/BioM-ALBERT-xxlarge-SQuAD2 | sultan | "2021-08-10T21:59:59Z" | 4 | 1 | transformers | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | "2022-03-02T23:29:05Z" | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer models
using different design choices. We evaluate
the performance of our pretrained models
against other existing biomedical language
models in the literature. Our results show that
we achieve state-of-the-art results on several
biomedical domain tasks despite using similar
or less computational cost compared to other
models in the literature. Our findings highlight
the significant effect of design choices on
improving the performance of biomedical language
models.
# Model Description
This model is fine-tuned on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ALBERT-xxlarge. This model (TensorFlow version ) took the lead in the BioASQ9b-Factoid challenge under the name of (UDEL-LAB1).
If you want to try our Tensor Flow example and how to fine-tune ALBERT on SQuAD and BioASQ follow this link :
https://github.com/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ALBERT_xxlarge_on_TPU.ipynb
To see the full details of BioASQ9B results, please check this link http://participants-area.bioasq.org/results/9b/phaseB/ ( you need to register).
Huggingface library doesn't implement the Layer-Wise decay feature, which affects the performance on the SQuAD task. The reported result of BioM-ALBERT-xxlarge-SQuAD in our paper is 87.00 (F1) since we use ALBERT open-source code with TF checkpoint, which uses Layer-Wise decay.
Result with PyTorch and V100 GPU
```
***** eval metrics *****
HasAns_exact = 77.6484
HasAns_f1 = 85.0136
HasAns_total = 5928
NoAns_exact = 86.577
NoAns_f1 = 86.577
NoAns_total = 5945
best_exact = 82.1191
best_exact_thresh = 0.0
best_f1 = 85.7964
best_f1_thresh = 0.0
eval_samples = 12551
exact = 82.1191
f1 = 85.7964
total = 11873
```
To reproduce results in Google Colab:
- Make sure you have GPU enabled.
- Clone and install required libraries through this code
!git clone https://github.com/huggingface/transformers
!pip3 install -e transformers
!pip3 install sentencepiece
!pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt
- Run this python code:
```python
python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path BioM-ALBERT-xxlarge-SQuAD2 \
--do_eval \
--version_2_with_negative \
--per_device_eval_batch_size 8 \
--dataset_name squad_v2 \
--overwrite_output_dir \
--fp16 \
--output_dir out
```
You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.
# Acknowledgment
We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
# Citation
```bibtex
@inproceedings{alrowili-shanker-2021-biom,
title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
pages = "221--227",
abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
}
``` |
TrendForge/brown22-speech13454 | TrendForge | "2025-03-20T14:36:25Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-03-20T14:36:22Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
jukofyork/creative-writer-plus-35b-preview-01-2025 | jukofyork | "2025-01-06T11:54:45Z" | 11 | 0 | transformers | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"creative-writing",
"creative-writer",
"multiplicative-lora",
"conversational",
"en",
"base_model:CohereForAI/c4ai-command-r-v01",
"base_model:finetune:CohereForAI/c4ai-command-r-v01",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-05T17:06:59Z" | ---
language:
- en
license: cc-by-nc-4.0
library_name: transformers
base_model:
- CohereForAI/c4ai-command-r-v01
pipeline_tag: text-generation
tags:
- creative-writing
- creative-writer
- multiplicative-lora
---
An experimental "Boosted Entropy" model, fine-tuned using the "multiplicative-LoRA" method on [command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01). |
tensorblock/7B-v0.2-GGUF | tensorblock | "2025-03-06T00:11:50Z" | 0 | 0 | null | [
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"ar",
"base_model:yellowtown/7B-v0.2",
"base_model:quantized:yellowtown/7B-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-03-05T22:47:54Z" | ---
license: apache-2.0
language:
- en
- ar
base_model: yellowtown/7B-v0.2
pipeline_tag: text-generation
tags:
- TensorBlock
- GGUF
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;">
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
</p>
</div>
</div>
## yellowtown/7B-v0.2 - GGUF
This repo contains GGUF format model files for [yellowtown/7B-v0.2](https://huggingface.co/yellowtown/7B-v0.2).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).
<div style="text-align: left; margin: 20px 0;">
<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Run them on the TensorBlock client using your local machine ↗
</a>
</div>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [7B-v0.2-Q2_K.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q2_K.gguf) | Q2_K | 3.016 GB | smallest, significant quality loss - not recommended for most purposes |
| [7B-v0.2-Q3_K_S.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q3_K_S.gguf) | Q3_K_S | 3.492 GB | very small, high quality loss |
| [7B-v0.2-Q3_K_M.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q3_K_M.gguf) | Q3_K_M | 3.808 GB | very small, high quality loss |
| [7B-v0.2-Q3_K_L.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q3_K_L.gguf) | Q3_K_L | 4.088 GB | small, substantial quality loss |
| [7B-v0.2-Q4_0.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q4_0.gguf) | Q4_0 | 4.431 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [7B-v0.2-Q4_K_S.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q4_K_S.gguf) | Q4_K_S | 4.458 GB | small, greater quality loss |
| [7B-v0.2-Q4_K_M.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q4_K_M.gguf) | Q4_K_M | 4.683 GB | medium, balanced quality - recommended |
| [7B-v0.2-Q5_0.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q5_0.gguf) | Q5_0 | 5.315 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [7B-v0.2-Q5_K_S.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q5_K_S.gguf) | Q5_K_S | 5.315 GB | large, low quality loss - recommended |
| [7B-v0.2-Q5_K_M.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q5_K_M.gguf) | Q5_K_M | 5.445 GB | large, very low quality loss - recommended |
| [7B-v0.2-Q6_K.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q6_K.gguf) | Q6_K | 6.254 GB | very large, extremely low quality loss |
| [7B-v0.2-Q8_0.gguf](https://huggingface.co/tensorblock/7B-v0.2-GGUF/blob/main/7B-v0.2-Q8_0.gguf) | Q8_0 | 8.099 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/7B-v0.2-GGUF --include "7B-v0.2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/7B-v0.2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
weide0118/ddpm-celebahq-finetuned-butterflies-2epochs | weide0118 | "2024-11-23T11:38:11Z" | 44 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | "2024-11-23T11:37:50Z" | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('weide0118/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
claudiodev/dqn-SpaceInvadersNoFrameskip-v4 | claudiodev | "2023-03-01T08:39:13Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-02-28T09:36:08Z" | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 945.00 +/- 319.16
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga claudiodev -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga claudiodev -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga claudiodev
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 10000000.0),
('normalize', False),
('optimize_memory_usage', False),
('policy', 'CnnPolicy')])
```
|
xuerongkun/distilbert-base-uncased_emotion_ft_12_17 | xuerongkun | "2023-12-17T11:08:00Z" | 8 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-12-17T10:29:24Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased_emotion_ft_12_17
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_emotion_ft_12_17
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1533
- Accuracy: 0.9315
- F1: 0.9317
- Precision: 0.9320
- Recall: 0.9315
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8414 | 1.0 | 250 | 0.2936 | 0.9085 | 0.9085 | 0.9097 | 0.9085 |
| 0.2154 | 2.0 | 500 | 0.1816 | 0.9305 | 0.9305 | 0.9313 | 0.9305 |
| 0.1415 | 3.0 | 750 | 0.1597 | 0.9355 | 0.9354 | 0.9356 | 0.9355 |
| 0.1165 | 4.0 | 1000 | 0.1533 | 0.9315 | 0.9317 | 0.9320 | 0.9315 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.0
|
FreedomIntelligence/Apollo-6B | FreedomIntelligence | "2024-04-26T11:13:01Z" | 2,795 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.03640",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-06T13:06:09Z" | ---
license: apache-2.0
---
# Multilingual Medicine: Model, Dataset, Benchmark, Code
Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
<p align="center">
👨🏻💻<a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Github</a> •📃 <a href="https://arxiv.org/abs/2403.03640" target="_blank">Paper</a> • 🌐 <a href="https://apollo.llmzoo.com/" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<br> <a href="./README_zh.md"> 中文 </a> | <a href="./README.md"> English
</p>
## 🌈 Update
* **[2024.03.07]** [Paper](https://arxiv.org/abs/2403.03640) released.
* **[2024.02.12]** <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> and <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a> is published！🎉
* **[2024.01.23]** Apollo repo is published！🎉
## Results
🤗<a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B" target="_blank">Apollo-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-1.8B" target="_blank">Apollo-1.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B" target="_blank">Apollo-2B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B" target="_blank">Apollo-6B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B" target="_blank">Apollo-7B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B-GGUF" target="_blank">Apollo-0.5B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B-GGUF" target="_blank">Apollo-2B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B-GGUF" target="_blank">Apollo-6B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF" target="_blank">Apollo-7B-GGUF</a>
## Usage Format
User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a>
<details><summary>Click to expand</summary>
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/blob/main/ApolloCorpus.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
- Pretrain:
- data item:
- json_name: {data_source}_{language}_{data_type}.json
- data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
- language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
- data_type: qa(generated qa from text)
- data_type==text: list of string
```
[
"string1",
"string2",
...
]
```
- data_type==qa: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
- SFT:
- json_name: {data_source}_{language}.json
- data_type: code, general, math, medicalExam, medicalPatient
- data item: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
</details>
## Results reproduction
<details><summary>Click to expand</summary>
**Waiting for Update**
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
gokuls/Glue_distilbert | gokuls | "2022-12-29T23:00:36Z" | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-12-29T22:38:42Z" | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: Glue_distilbert
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8504901960784313
- name: F1
type: f1
value: 0.8960817717206134
---
<!-- 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. -->
# Glue_distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1042
- Accuracy: 0.8505
- F1: 0.8961
- Combined Score: 0.8733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5066 | 1.0 | 115 | 0.3833 | 0.8358 | 0.8851 | 0.8604 |
| 0.3227 | 2.0 | 230 | 0.4336 | 0.8309 | 0.8844 | 0.8577 |
| 0.1764 | 3.0 | 345 | 0.4943 | 0.8309 | 0.8757 | 0.8533 |
| 0.0792 | 4.0 | 460 | 0.7271 | 0.8431 | 0.8861 | 0.8646 |
| 0.058 | 5.0 | 575 | 0.8374 | 0.8456 | 0.8945 | 0.8700 |
| 0.0594 | 6.0 | 690 | 0.7570 | 0.8309 | 0.8816 | 0.8563 |
| 0.0395 | 7.0 | 805 | 0.8640 | 0.8431 | 0.8897 | 0.8664 |
| 0.03 | 8.0 | 920 | 0.9007 | 0.8260 | 0.8799 | 0.8529 |
| 0.0283 | 9.0 | 1035 | 0.9479 | 0.8211 | 0.8685 | 0.8448 |
| 0.0127 | 10.0 | 1150 | 1.0686 | 0.8431 | 0.8915 | 0.8673 |
| 0.0097 | 11.0 | 1265 | 1.0752 | 0.8431 | 0.8919 | 0.8675 |
| 0.0164 | 12.0 | 1380 | 1.0627 | 0.8284 | 0.8801 | 0.8543 |
| 0.007 | 13.0 | 1495 | 1.1466 | 0.8333 | 0.8815 | 0.8574 |
| 0.0132 | 14.0 | 1610 | 1.1442 | 0.8456 | 0.8938 | 0.8697 |
| 0.0125 | 15.0 | 1725 | 1.1716 | 0.8235 | 0.8771 | 0.8503 |
| 0.0174 | 16.0 | 1840 | 1.1187 | 0.8333 | 0.8790 | 0.8562 |
| 0.0171 | 17.0 | 1955 | 1.1053 | 0.8456 | 0.8938 | 0.8697 |
| 0.0026 | 18.0 | 2070 | 1.2011 | 0.8309 | 0.8787 | 0.8548 |
| 0.0056 | 19.0 | 2185 | 1.3085 | 0.8260 | 0.8748 | 0.8504 |
| 0.0067 | 20.0 | 2300 | 1.3042 | 0.8333 | 0.8803 | 0.8568 |
| 0.0129 | 21.0 | 2415 | 1.1042 | 0.8505 | 0.8961 | 0.8733 |
| 0.0149 | 22.0 | 2530 | 1.1575 | 0.8235 | 0.8820 | 0.8527 |
| 0.0045 | 23.0 | 2645 | 1.2359 | 0.8407 | 0.8900 | 0.8654 |
| 0.0029 | 24.0 | 2760 | 1.3823 | 0.8211 | 0.8744 | 0.8477 |
| 0.0074 | 25.0 | 2875 | 1.2394 | 0.8431 | 0.8904 | 0.8668 |
| 0.002 | 26.0 | 2990 | 1.4450 | 0.8333 | 0.8859 | 0.8596 |
| 0.0039 | 27.0 | 3105 | 1.5102 | 0.8284 | 0.8805 | 0.8545 |
| 0.0015 | 28.0 | 3220 | 1.4767 | 0.8431 | 0.8915 | 0.8673 |
| 0.0062 | 29.0 | 3335 | 1.5101 | 0.8407 | 0.8926 | 0.8666 |
| 0.0054 | 30.0 | 3450 | 1.3861 | 0.8382 | 0.8893 | 0.8637 |
| 0.0001 | 31.0 | 3565 | 1.4101 | 0.8456 | 0.8948 | 0.8702 |
| 0.0 | 32.0 | 3680 | 1.4203 | 0.8480 | 0.8963 | 0.8722 |
| 0.002 | 33.0 | 3795 | 1.4526 | 0.8431 | 0.8923 | 0.8677 |
| 0.0019 | 34.0 | 3910 | 1.6265 | 0.8260 | 0.8842 | 0.8551 |
| 0.0029 | 35.0 | 4025 | 1.4788 | 0.8456 | 0.8945 | 0.8700 |
| 0.0 | 36.0 | 4140 | 1.4668 | 0.8480 | 0.8956 | 0.8718 |
| 0.0007 | 37.0 | 4255 | 1.5248 | 0.8456 | 0.8945 | 0.8700 |
| 0.0 | 38.0 | 4370 | 1.5202 | 0.8480 | 0.8960 | 0.8720 |
| 0.0033 | 39.0 | 4485 | 1.5541 | 0.8358 | 0.8878 | 0.8618 |
| 0.0017 | 40.0 | 4600 | 1.5097 | 0.8407 | 0.8904 | 0.8655 |
| 0.0 | 41.0 | 4715 | 1.5301 | 0.8407 | 0.8904 | 0.8655 |
| 0.0 | 42.0 | 4830 | 1.4974 | 0.8407 | 0.8862 | 0.8634 |
| 0.0018 | 43.0 | 4945 | 1.5483 | 0.8382 | 0.8896 | 0.8639 |
| 0.0 | 44.0 | 5060 | 1.5071 | 0.8480 | 0.8931 | 0.8706 |
| 0.0 | 45.0 | 5175 | 1.5104 | 0.8480 | 0.8935 | 0.8708 |
| 0.0011 | 46.0 | 5290 | 1.5445 | 0.8382 | 0.8896 | 0.8639 |
| 0.0012 | 47.0 | 5405 | 1.5378 | 0.8431 | 0.8900 | 0.8666 |
| 0.0 | 48.0 | 5520 | 1.5577 | 0.8407 | 0.8881 | 0.8644 |
| 0.0009 | 49.0 | 5635 | 1.5431 | 0.8407 | 0.8885 | 0.8646 |
| 0.0002 | 50.0 | 5750 | 1.5383 | 0.8431 | 0.8904 | 0.8668 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
davooddkareshki/Movie_Genre_Classifier | davooddkareshki | "2024-06-29T11:35:02Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-28T01:45:10Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### 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. -->
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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furrutiav/modernbert_mixtral_nllfg_vanilla_rte_tf_idf_centroid | furrutiav | "2025-03-21T03:17:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2025-03-20T01:51:53Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### 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
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#### Preprocessing [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[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]
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## Technical Specifications [optional]
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## Model Card Contact
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yekaraoglann/results | yekaraoglann | "2023-09-05T12:18:41Z" | 104 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:multi_news",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2023-09-05T12:18:12Z" | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: results
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: multi_news
type: multi_news
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1425
---
<!-- 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
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8243
- Rouge1: 0.1425
- Rouge2: 0.0442
- Rougel: 0.1094
- Rougelsum: 0.1094
- Gen Len: 18.9968
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.2334 | 1.0 | 702 | 2.8243 | 0.1425 | 0.0442 | 0.1094 | 0.1094 | 18.9968 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
kbaumgartner/DeBERTa_Finetuned_Financial_News | kbaumgartner | "2024-04-12T15:04:31Z" | 170 | 1 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-04-12T15:03:33Z" | ---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5933
- Accuracy: 0.8596
- F1: 0.8420
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6828 | 1.0 | 182 | 0.7011 | 0.6976 | 0.4393 |
| 0.4415 | 2.0 | 364 | 0.4868 | 0.8266 | 0.7933 |
| 0.4762 | 3.0 | 546 | 0.5500 | 0.8163 | 0.7798 |
| 0.2522 | 4.0 | 728 | 0.5855 | 0.8369 | 0.8139 |
| 0.1986 | 5.0 | 910 | 0.5933 | 0.8596 | 0.8420 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
DoeyLLM/OneLLM-Doey-ChatQA-V1-Llama-3.2-1B | DoeyLLM | "2024-11-25T07:17:32Z" | 89 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:nvidia/ChatQA-Training-Data",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-25T05:02:44Z" | ---
license: apache-2.0
datasets:
- nvidia/ChatQA-Training-Data
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
## **Model Summary**
This model is a fine-tuned version of **LLaMA 3.2-1B**, optimized using **LoRA (Low-Rank Adaptation)** on the [NVIDIA ChatQA-Training-Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data). It is tailored for conversational AI, question answering, and other instruction-following tasks, with support for sequences up to 1024 tokens.
---
## **Key Features**
- **Base Model**: LLaMA 3.2-1B
- **Fine-Tuning Framework**: LoRA
- **Dataset**: NVIDIA ChatQA-Training-Data
- **Max Sequence Length**: 1024 tokens
- **Use Case**: Instruction-based tasks, question answering, conversational AI.
## **Model Usage**
This fine-tuned model is suitable for:
- **Conversational AI**: Chatbots and dialogue agents with improved contextual understanding.
- **Question Answering**: Generating concise and accurate answers to user queries.
- **Instruction Following**: Responding to structured prompts.
- **Long-Context Tasks**: Processing sequences up to 1024 tokens for long-text reasoning.
# **How to Use DoeyLLM / OneLLM-Doey-V1-Llama-3.2-1B-Instruct**
This guide explains how to use the **DoeyLLM** model on both app (iOS) and PC platforms.
---
## **App: Use with OneLLM**
OneLLM brings versatile large language models (LLMs) to your device—Llama, Gemma, Qwen, Mistral, and more. Enjoy private, offline GPT and AI tools tailored to your needs.
With OneLLM, experience the capabilities of leading-edge language models directly on your device, all without an internet connection. Get fast, reliable, and intelligent responses, while keeping your data secure with local processing.
### **Quick Start for mobile**
Follow these steps to integrate the **DoeyLLM** model using the OneLLM app:
1. **Download OneLLM**
Get the app from the [App Store](https://apps.apple.com/us/app/onellm-private-ai-gpt-llm/id6737907910) and install it on your iOS device.
Or get the app from the [Play Store](https://play.google.com/store/apps/details?id=com.esotech.onellm) and install it on your Android device.
3. **Load the DoeyLLM Model**
Use the OneLLM interface to load the DoeyLLM model directly into the app:
- Navigate to the **Model Library**.
- Search for `DoeyLLM`.
- Select the model and tap **Download** to store it locally on your device.
4. **Start Conversing**
Once the model is loaded, you can begin interacting with it through the app's chat interface. For example:
- Tap the **Chat** tab.
- Type your question or prompt, such as:
> "Explain the significance of AI in education."
- Receive real-time, intelligent responses generated locally.
### **Key Features of OneLLM**
- **Versatile Models**: Supports various LLMs, including Llama, Gemma, and Qwen.
- **Private & Secure**: All processing occurs locally on your device, ensuring data privacy.
- **Offline Capability**: Use the app without requiring an internet connection.
- **Fast Performance**: Optimized for mobile devices, delivering low-latency responses.
For more details or support, visit the [OneLLM App Store page](https://apps.apple.com/us/app/onellm-private-ai-gpt-llm/id6737907910) and [Play Store](https://play.google.com/store/apps/details?id=com.esotech.onellm).
## **PC: Use with Transformers**
The DoeyLLM model can also be used on PC platforms through the `transformers` library, enabling robust and scalable inference for various NLP tasks.
### **Quick Start for PC**
Follow these steps to use the model with Transformers:
1. **Install Transformers**
Ensure you have `transformers >= 4.43.0` installed. Update or install it via pip:
```bash
pip install --upgrade transformers
2. **Load the Model**
Use the transformers library to load the model and tokenizer:
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "OneLLM-Doey-V1-Llama-3.2-1B"
pipe = pipeline(
"text-generation",
model=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?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
## Responsibility & Safety
As part of our responsible release strategy, we adopted a three-pronged approach to managing trust and safety risks:
Enable developers to deploy helpful, safe, and flexible experiences for their target audience and the use cases supported by the model.
Protect developers from adversarial users attempting to exploit the model’s capabilities to potentially cause harm.
Provide safeguards for the community to help prevent the misuse of the model. |
RichardErkhov/meetkai_-_functionary-small-v3.1-8bits | RichardErkhov | "2025-03-31T01:33:52Z" | 0 | 0 | null | [
"safetensors",
"llama",
"custom_code",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-03-31T01:26:26Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
functionary-small-v3.1 - bnb 8bits
- Model creator: https://huggingface.co/meetkai/
- Original model: https://huggingface.co/meetkai/functionary-small-v3.1/
Original model description:
---
license: mit
---
# Model Card for functionary-small-v3.1
**This model was based on [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**, using Meta's original prompt template as described in: [User-defined Custom tool calling](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/#user-defined-custom-tool-calling)
[https://github.com/MeetKai/functionary](https://github.com/MeetKai/functionary)
<img src="https://huggingface.co/meetkai/functionary-medium-v2.2/resolve/main/functionary_logo.jpg" alt="Functionary Logo" width="300"/>
Functionary is a language model that can interpret and execute functions/plugins.
The model determines when to execute functions, whether in parallel or serially, and can understand their outputs. It only triggers functions as needed. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls.
## Key Features
- Intelligent **parallel tool use**
- Able to analyze functions/tools outputs and provide relevant responses **grounded in the outputs**
- Able to decide **when to not use tools/call functions** and provide normal chat response
- Truly one of the best open-source alternative to GPT-4
- Support code interpreter
## How to Get Started
We provide custom code for parsing raw model responses into a JSON object containing role, content and tool_calls fields. This enables the users to read the function-calling output of the model easily.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("meetkai/functionary-small-v3.1")
model = AutoModelForCausalLM.from_pretrained("meetkai/functionary-small-v3.1", device_map="auto", trust_remote_code=True)
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
messages = [{"role": "user", "content": "What is the weather in Istanbul and Singapore respectively?"}]
final_prompt = tokenizer.apply_chat_template(messages, tools, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(final_prompt, return_tensors="pt").to("cuda")
pred = model.generate_tool_use(**inputs, max_new_tokens=128, tokenizer=tokenizer)
print(tokenizer.decode(pred.cpu()[0]))
```
## Prompt Template
We convert function definitions to a similar text to TypeScript definitions. Then we inject these definitions as system prompts. After that, we inject the default system prompt. Then we start the conversation messages.
This formatting is also available via our vLLM server which we process the functions into Typescript definitions encapsulated in a system message using a pre-defined Transformers Jinja chat template. This means that the lists of messages can be formatted for you with the apply_chat_template() method within our server:
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="functionary")
client.chat.completions.create(
model="path/to/functionary/model/",
messages=[{"role": "user",
"content": "What is the weather for Istanbul?"}
],
tools=[{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}],
tool_choice="auto"
)
```
will yield:
```
<|start_header_id|>system<|end_header_id|>
Environment: ipython
Cutting Knowledge Date: December 2023
You have access to the following functions:
Use the function 'get_current_weather' to 'Get the current weather'
{"name": "get_current_weather", "description": "Get the current weather", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}},"required": ["location"]}}
Think very carefully before calling functions.
If a you choose to call a function ONLY reply in the following format:
<{start_tag}={function_name}>{parameters}{end_tag}
where
start_tag => `<function`
parameters => a JSON dict with the function argument name as key and function argument value as value.
end_tag => `</function>`
Here is an example,
<function=example_function_name>{"example_name": "example_value"}</function>
Reminder:
- If looking for real time information use relevant functions before falling back to brave_search
- Function calls MUST follow the specified format, start with <function= and end with </function>
- Required parameters MUST be specified
- Only call one function at a time
- Put the entire function call reply on one line
<|eot_id|><|start_header_id|>user<|end_header_id|>
What is the weather for Istanbul?
```
A more detailed example is provided [here](https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt).
## Run the model
We encourage users to run our models using our OpenAI-compatible vLLM server [here](https://github.com/MeetKai/functionary).
# The MeetKai Team
|
Lugaborg/Balnab | Lugaborg | "2024-04-24T15:55:09Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-09T04:00:07Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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yijiexu/instruction_following_model | yijiexu | "2025-04-09T13:49:05Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:other",
"region:us"
] | null | "2025-04-09T13:47:32Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
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name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
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yhavinga/t5-v1.1-large-dutch-cnn-test | yhavinga | "2022-12-05T14:22:21Z" | 21 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"seq2seq",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"dataset:ml6team/cnn_dailymail_nl",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | summarization | "2022-03-02T23:29:05Z" | ---
language:
- nl
license: apache-2.0
tags:
- summarization
- t5
- seq2seq
datasets:
- yhavinga/mc4_nl_cleaned
- ml6team/cnn_dailymail_nl
pipeline_tag: summarization
widget:
- text: 'Het Van Goghmuseum in Amsterdam heeft vier kostbare prenten verworven van
Mary Cassatt, de Amerikaanse impressionistische kunstenaar en tijdgenoot van Vincent
van Gogh. Dat heeft het museum woensdagmiddag op een persconferentie bekendgemaakt.
Het gaat om drie grote kleurenetsen en een zwart-wit litho met voorstellingen
van vrouwen. Voor deze prenten, die afkomstig zijn van een Amerikaanse verzamelaar,
betaalde het museum ruim 1,4 miljoen euro. Drie grote fondsen en een aantal particulieren
hebben samen de aankoopsom beschikbaar gesteld. Mary Stevenson Cassatt (1844-1926)
woonde en werkte lange tijd in Frankrijk. Ze staat met haar impressionistische
schilderijen en tekeningen te boek als een van de vernieuwers van de Parijse kunstwereld
in de late negentiende eeuw. Het Van Goghmuseum rekent haar prenten „tot het mooiste
wat op grafisch gebied in het fin de siècle is geproduceerd”. De drie aangekochte
kleurenetsen – Het doorpassen, De brief en Badende vrouw – komen uit een serie
van tien waarmee Cassatt haar naam als (prent)kunstenaar definitief vestigde.
Ze maakte de etsen na een bezoek in 1890 aan een tentoonstelling van Japanse prenten
in Parijs. Over die expositie schreef de Amerikaanse aan haar vriendin Berthe
Morisot, een andere vrouwelijke impressionist: „We kunnen de Japanse prenten in
de Beaux-Arts gaan bekijken. Echt, die mag je niet missen. Als je kleurenprenten
wilt maken, is er niets mooiers voorstelbaar. Ik droom ervan en denk nergens anders
meer aan dan aan kleur op koper.'
- text: 'Afgelopen zaterdagochtend werden Hunga Tonga en Hunga Hapai opnieuw twee
aparte eilanden toen de vulkaan met een hevige explosie uitbarstte. De aanloop
tot de uitbarsting begon al eind vorig jaar met kleinere explosies. Begin januari
nam de activiteit af en dachten geologen dat de vulkaan tot rust was gekomen.
Toch barstte hij afgelopen zaterdag opnieuw uit, veel heviger dan de uitbarstingen
ervoor. Vlák voor deze explosie stortte het kilometerslange verbindingsstuk in
en verdween onder het water. De eruptie duurde acht minuten. De wolk van as en
giftige gasdeeltjes, zoals zwaveloxide, die daarbij vrijkwam, reikte tot dertig
kilometer hoogte en was zo’n vijfhonderd kilometer breed. Ter vergelijking: de
pluimen uit de recente vulkaanuitbarsting op La Palma reikten maximaal zo’n vijf
kilometer hoog. De hoofdstad van Tonga, vijfenzestig kilometer verderop is bedekt
met een dikke laag as. Dat heeft bijvoorbeeld gevolgen voor de veiligheid van
het drinkwater op Tonga. De uitbarsting van de onderzeese vulkaan in de eilandstaat
Tonga afgelopen zaterdag was bijzonder heftig. De eruptie veroorzaakte een tsunami
die reikte van Nieuw-Zeeland tot de Verenigde Staten en in Nederland ging de luchtdruk
omhoog. Geologen verwachten niet dat de vulkaan op Tonga voor een lange wereldwijde
afkoeling zorgt, zoals bij andere hevige vulkaanuitbarstingen het geval is geweest.
De vulkaan ligt onder water tussen de onbewoonde eilandjes Hunga Tonga (0,39 vierkante
kilometer) en Hunga Ha’apai (0,65 vierkante kilometer). Magma dat bij kleinere
uitbarsting in 2009 en 2014 omhoog kwam, koelde af en vormde een verbindingsstuk
tussen de twee eilanden in. Een explosie van een onderwatervulkaan als die bij
Tonga is heftiger dan bijvoorbeeld die uitbarsting op La Palma. „Dat komt doordat
het vulkanisme hier veroorzaakt wordt door subductie: de Pacifische plaat zinkt
onder Tonga de aardmantel in en neemt water mee omlaag”, zegt hoogleraar paleogeografie
Douwe van Hinsbergen van de Universiteit Utrecht. „Dit water komt met magma als
gas, als waterdamp, mee omhoog. Dat voert de druk onder de aardkost enorm op.
Arwen Deuss, geowetenschapper aan de Universiteit Utrecht, vergelijkt het met
een fles cola. „Wanneer je een fles cola schudt, zal het gas er met veel geweld
uitkomen. Dat is waarschijnlijk wat er gebeurd is op Tonga, maar we weten het
niet precies.”'
model-index:
- name: yhavinga/t5-v1.1-large-dutch-cnn-test
results:
- task:
type: summarization
name: Summarization
dataset:
name: ml6team/cnn_dailymail_nl
type: ml6team/cnn_dailymail_nl
config: default
split: test
metrics:
- type: rouge
value: 38.3101
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmViMzRlMzcxZGNkZGVkZWJiODJmZDYwYjc0OTIyZDljZTllM2Y1MGQ1NGMyYTdmZDBlZjU5NjNiODJjMGEzNSIsInZlcnNpb24iOjF9.-zYdsih0cz6bFO_XPlC62M5UwUKoVo6dmEEYAtMs8dMd3J0a1DOUaZOm-EKNeeUACXkYss7NwFchbAPrgncFBQ
- type: rouge
value: 15.5229
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDc5YjNiMjdhNjFjYTI3MDNiZTZlNGQ3YzI5OWJiODQ1ZmRjZTA0ZTBhOWMxMDcwMjNmNmE2NmRlMGExMGRhYiIsInZlcnNpb24iOjF9.YXUt76BueobffcS13s-cQ6ljjJokL7BgN4d_jKFzWNIJUxZ2-WjDpqjWkGG_bqUZ-N65cqhElYiXkVtzvHbXDQ
- type: rouge
value: 25.8229
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDk2NzA2YzVmMGEwYTc0YjFmYzUzMmFlMWJjMDhkNDhlYjNmOGJkMzFhMjAzZTA3NmQ0OTExMmRhZjg2ZDQzMCIsInZlcnNpb24iOjF9.9sO6ujd-dpPc1UdcFScmB27cRiwQRzgkiNxR9vAgP1j2X4UdGGXYW7E8IJPf0cYYWMrue4A54GjygAlC1jsKCw
- type: rouge
value: 35.3162
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWEwNzI3OWVjOGQwNjdkNDFlMTM4Y2Y1NTBlMGMyNmU0ODdjNjRhNTA2MTRmYTllY2ZjMzAwNTE0ODBjYWEwYiIsInZlcnNpb24iOjF9.P-Fxd_ocpoSsaH8MCMNT4wUcPuxnJm8Yof8ZmcM8RKDKk3j9nsztYedR7MKHLEdHdWtZEcjwO7y5MPbo6uPzBQ
- type: loss
value: 3.143123149871826
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTkyODk2NThiYWI0Zjk5ZTJlZDA0ZjhkYWYyMDRiNDdkZjM4YjFkYzdmZjgwZmM4OThiOTJhZmNiMjBkOGI0NiIsInZlcnNpb24iOjF9.WmCwzapNXddaASJjqPd6cZuHUJZi5t1yKBMSIN91V07Os0GK5FdOstEnnTbmlMiaJRJKbbWwiEQP1J7c28hBAQ
- type: gen_len
value: 88.806
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDQ0MjllNzliZDMxNmQ4NDZhY2QzMzMwMTU1ZmU0ZjkxZTA5N2Q1NzMxMDljZjhkN2U2NTllMjJmYTM5ZTE3YiIsInZlcnNpb24iOjF9.Mnv90rphfeeHadhXxpBRg23vMl4pAQiZu9m411m_-GCiPFam3vOgKDU8lSqz7e4piuXxcvbESJtaU12gAdYIDA
---
# T5 v1.1 Large finetuned for CNN news summarization in Dutch 🇳🇱
This model is [t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) finetuned on [CNN Dailymail NL](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl)
For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application!
Rouge scores for this model are listed below.
## Tokenizer
* SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
## Dataset
All models listed below are trained on of the `full` configuration (39B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except
* Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
* Sentences with less than 3 words are removed
* Sentences with a word of more than 1000 characters are removed
* Documents with less than 5 sentences are removed
* Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
"use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
## Models
TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model.
* `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021
Flax/Jax community week. Accuracy was improved from 0.64 to 0.70.
* The two T5 v1.1 base models are an uncased and cased version of `t5-v1.1-base`, again pre-trained from scratch on Dutch,
with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the
base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1.
* The large cased model is a pre-trained Dutch version of `t5-v1.1-large`. Training of t5-v1.1-large proved difficult.
Without dropout regularization, the training would diverge at a certain point. With dropout training went better,
be it much slower than training the t5-model. At some point convergance was too slow to warrant further training.
The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased
base model is probably the better choice.
| | model | train seq len | acc | loss | batch size | epochs | steps | dropout | optim | lr | duration |
|---------------------------------------------------------------------------------------------------|---------|---------------|----------|----------|------------|--------|---------|---------|-----------|------|----------|
| [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) | T5 | 512 | 0,70 | 1,38 | 128 | 1 | 528481 | 0.1 | adafactor | 5e-3 | 2d 9h |
| [yhavinga/t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | t5-v1.1 | 1024 | 0,73 | 1,20 | 64 | 2 | 1014525 | 0.0 | adafactor | 5e-3 | 5d 5h |
| [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) | t5-v1.1 | 1024 | **0,78** | **0,96** | 64 | 2 | 1210000 | 0.0 | adafactor | 5e-3 | 6d 6h |
| [yhavinga/t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) | t5-v1.1 | 512 | 0,76 | 1,07 | 64 | 1 | 1120000 | 0.1 | adafactor | 5e-3 | 86 13h |
The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset.
| | model | input len | target len | Rouge1 | Rouge2 | RougeL | RougeLsum | Test Gen Len | epochs | batch size | steps | duration |
|-------------------------------------------------------------------------------------------------------|---------|-----------|------------|--------|--------|--------|-----------|--------------|--------|------------|-------|----------|
| [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test) | t5-v1.1 | 1024 | 96 | 34,8 | 13,6 | 25,2 | 32,1 | 79 | 6 | 64 | 26916 | 2h 40m |
| [yhavinga/t5-v1.1-large-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cnn-test) | t5-v1.1 | 1024 | 96 | 34,4 | 13,6 | 25,3 | 31,7 | 81 | 5 | 16 | 89720 | 11h |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in many, if not all parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:
* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/) |
slicexai/elm-v0.1 | slicexai | "2024-04-28T17:04:08Z" | 12 | 2 | elm | [
"elm",
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-04-14T06:34:27Z" | ---
license: apache-2.0
language:
- en
library_name: elm
tags:
- elm
pipeline_tag: text-generation
---
# SliceX AI™ ELM (Efficient Language Models)
**ELM** (which stands for **E**fficient **L**anguage **M**odels) is the first version in the series of cutting-edge language models from [SliceX AI](https://slicex.ai) that is designed to achieve the best in class performance in terms of _quality_, _throughput_ & _memory_.
<div align="center">
<img src="elm-rambutan.png" width="256"/>
</div>
ELM is designed to be a modular and customizable family of neural networks that are highly efficient and performant. Today we are sharing the first version in this series: **ELM-v0.1** models (named _Rambutan_).
_Model:_ ELM introduces a new type of _(de)-composable LLM model architecture_ along with the algorithmic optimizations required to learn (training) and run (inference) these models. At a high level, we train a single ELM model in a self-supervised manner (during pre-training phase) but once trained the ELM model can be sliced in many ways to fit different user/task needs. The optimizations can be applied to the model either during the pre-training and/or fine-tuning stage.
_Fast Inference with Customization:_ Once trained, the ELM model architecture permits flexible inference strategies at runtime depending on the deployment needs. For instance, the ELM model can be _decomposed_ into smaller slices, i.e., smaller (or larger) models can be extracted from the original model to create multiple inference endpoints. Alternatively, the original (single) ELM model can be loaded _as is_ for inference and different slices within the model can be queried directly to power faster inference. This provides an additional level of flexibility for users to make compute/memory tradeoffs depending on their application and runtime needs.
- **Blog:** [Medium](https://medium.com/sujith-ravi/introducing-elm-efficient-customizable-privacy-preserving-llms-cea56e4f727d)
- **Github:** https://github.com/slicex-ai/elm
- **Demo** (try it out): https://huggingface.co/spaces/slicexai/elm-demo-v1
- **HuggingFace** (access ELM Model cards, code & app from HF): https://huggingface.co/slicexai
## ELM-v0.1 Model Release
This repository contains code to run our ELM models. The current ELM model `elm-v0.1` (named _Rambutan_) was pre-trained (an intermediate checkpoint was used) and then instruction fine-tuned for downstream tasks.
ELM models (in the `models` folder) in this repository come in three sizes (`elm-1.0`, `elm-0.75` and `elm-0.25`). **All these different slices are extracted from the same ELM finetuned checkpoint for inference** and supports the following use-case.
- news_classification
- toxicity_detection
- news_content_generation
- news_summarization
**NOTE: ELM-v0.1 release is an early version finetuned from an intermediate pretrained checkpoint & without any KV caching, decoding optimizations, or quantization applied.**
## Setup ELM
### Download ELM repo
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/slicexai/elm-v0.1
```
### Installation
```bash
cd elm-v0.1
pip install -r requirements.txt
```
## Download ELM task-specific model checkpoints
### Install git-lfs
```bash
sudo apt-get install git-lfs
git lfs install
```
For Macbook, replace `sudo apt-get install git-lfs` with `brew install git-lfs`
(Optional) Installing git-lfs without sudo,
```bash
wget https://github.com/git-lfs/git-lfs/releases/download/v3.2.0/git-lfs-linux-amd64-v3.2.0.tar.gz
tar -xzf git-lfs-linux-amd64-v3.2.0.tar.gz
PATH=$PATH:/<absolute-path>/git-lfs-3.2.0/
git lfs install
```
### Download ELM checkpoints
To download all checkpoints
```bash
git lfs pull
```
```note
NOTE: Please allow a few minutes for the full download of all model checkpoints.
```
To download elm-1.0 model checkpoints individually
```bash
git lfs pull -I elm-1.0_news_classification/ckpt.pt
git lfs pull -I elm-1.0_toxicity_detection/ckpt.pt
git lfs pull -I elm-1.0_news_content_generation/ckpt.pt
git lfs pull -I elm-1.0_news_summarization/ckpt.pt
```
To download elm-0.75 model checkpoints individually
```bash
git lfs pull -I elm-0.75_news_classification/ckpt.pt
git lfs pull -I elm-0.75_toxicity_detection/ckpt.pt
git lfs pull -I elm-0.75_news_content_generation/ckpt.pt
git lfs pull -I elm-0.75_news_summarization/ckpt.pt
```
To download elm-0.25 model checkpoints individually
```bash
git lfs pull -I elm-0.25_news_classification/ckpt.pt
git lfs pull -I elm-0.25_toxicity_detection/ckpt.pt
git lfs pull -I elm-0.25_news_content_generation/ckpt.pt
```
## How to use: Run ELM on a sample task (e.g., news classification)
```bash
python run.py <elm-model-directory>
E.g. python run.py elm-0.75_news_classification
```
Prompts for the specific tasks can be found in the corresponding checkpoint directory. See an example below from `models/elm-0.75_news_classification/example_prompts.json`.
```json
{
"inputs": ["GM May Close Plant in Europe DETROIT (Reuters) - General Motors Corp. <A HREF=\"http://www.investor.reuters.com/FullQuote.aspx?ticker=GM.N target=/stocks/quickinfo/fullquote\">GM.N</A> will likely cut some jobs in Europe and may close a plant there as part of a restructuring plan under development to try to return the region to profitability, the U.S. automaker said on Wednesday."],
"template": "[INST]Below is a news article. Please classify it under one of the following classes (World, Business, Sports, Sci/Tech). Please format your response as a JSON payload.\n\n### Article: {input}\n\n### JSON Response:[/INST]"
}
```
Running the above command returns the following response
```json
{
"prompt": "[INST]Below is a news article. Please classify it under one of the following classes (World, Business, Sports, Sci/Tech). Please format your response as a JSON payload.\n\n### Article: GM May Close Plant in Europe DETROIT (Reuters) - General Motors Corp. <A HREF=\"http://www.investor.reuters.com/FullQuote.aspx?ticker=GM.N target=/stocks/quickinfo/fullquote\">GM.N</A> will likely cut some jobs in Europe and may close a plant there as part of a restructuring plan under development to try to return the region to profitability, the U.S. automaker said on Wednesday.\n\n### JSON Response:[/INST]",
"response": "{'text_label': 'Business'}"
}
``` |
stablediffusionapi/pvc-figurine-xl | stablediffusionapi | "2024-03-08T13:37:17Z" | 29 | 1 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-03-08T13:34:14Z" | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# PVC Figurine XL API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "pvc-figurine-xl"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/pvc-figurine-xl)
Model link: [View model](https://modelslab.com/models/pvc-figurine-xl)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "pvc-figurine-xl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** |
nhungphammmmm/c5b3fd8b-7414-4741-a52e-883f95cbc4c6 | nhungphammmmm | "2025-01-15T12:44:13Z" | 10 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-1.7B",
"base_model:adapter:unsloth/SmolLM2-1.7B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-15T12:30:07Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c5b3fd8b-7414-4741-a52e-883f95cbc4c6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM2-1.7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 8492dab84eb4a872_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/8492dab84eb4a872_train_data.json
type:
field_input: Abstract
field_instruction: Title
field_output: Hypothesis
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nhungphammmmm/c5b3fd8b-7414-4741-a52e-883f95cbc4c6
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/8492dab84eb4a872_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: bb4dd990-a545-4b1a-8ec7-8e5c79e135a9
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: bb4dd990-a545-4b1a-8ec7-8e5c79e135a9
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# c5b3fd8b-7414-4741-a52e-883f95cbc4c6
This model is a fine-tuned version of [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4737
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3568 | 0.0120 | 200 | 0.4737 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-8 | anas-awadalla | "2022-02-25T06:39:41Z" | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
dsteiner93/a2c-PandaReachDense-v3 | dsteiner93 | "2024-02-09T23:10:31Z" | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-02-09T23:06:09Z" | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.23 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Fischerboot/13-test | Fischerboot | "2024-07-06T22:32:18Z" | 11 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Fischerboot/12-test",
"base_model:finetune:Fischerboot/12-test",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-07-06T22:30:23Z" | ---
base_model:
- Fischerboot/12-test
library_name: transformers
tags:
- mergekit
- merge
---
# 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:
* [Fischerboot/12-test](https://huggingface.co/Fischerboot/12-test)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Fischerboot/12-test
layer_range: [0, 18]
- model: Fischerboot/12-test
layer_range: [1, 19]
merge_method: slerp
base_model: Fischerboot/12-test
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
```
|
marimurta/q-FrozenLake-v1-4x4-Slippery | marimurta | "2023-03-23T14:18:19Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-03-23T14:18:04Z" | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.74 +/- 0.44
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="marimurta/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
adammandic87/2d309616-bc5c-4f72-b155-74599a609c64 | adammandic87 | "2025-01-10T07:06:33Z" | 11 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b-Instruct",
"base_model:adapter:unsloth/llama-3-8b-Instruct",
"license:llama3",
"region:us"
] | null | "2025-01-10T07:05:36Z" | ---
library_name: peft
license: llama3
base_model: unsloth/llama-3-8b-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2d309616-bc5c-4f72-b155-74599a609c64
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/llama-3-8b-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 34d6222f702b936d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/34d6222f702b936d_train_data.json
type:
field_input: original_text
field_instruction: prompt
field_output: rewritten_text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: adammandic87/2d309616-bc5c-4f72-b155-74599a609c64
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/34d6222f702b936d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8a5e2211-29e7-471f-b345-a0719423c5d4
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8a5e2211-29e7-471f-b345-a0719423c5d4
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 2d309616-bc5c-4f72-b155-74599a609c64
This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct](https://huggingface.co/unsloth/llama-3-8b-Instruct) on the None 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0053 | 1 | nan |
| 0.1352 | 0.0158 | 3 | nan |
| 0.0 | 0.0316 | 6 | nan |
| 0.0 | 0.0474 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
bigmorning/whisper_charsplit_new_round3__0056 | bigmorning | "2023-08-14T07:18:26Z" | 59 | 0 | transformers | [
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2023-08-14T07:18:18Z" | ---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0056
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_charsplit_new_round3__0056
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 8.0061
- Validation Loss: 0.5875
- Validation Accuracy: 0.0772
- Validation Wermet: 7.0840
- Epoch: 55
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
huwhitememes/danielpenny-lora | huwhitememes | "2024-12-16T03:48:05Z" | 23 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2024-12-16T03:46:44Z" | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/danielpenny-lora_004800_00_20241215150733.png
text: A photo of Daniel Penny, Daniel Penny,
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: A photo of Daniel Penny, Daniel Penny,
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# danielpenny-lora
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `A photo of Daniel Penny, Daniel Penny,` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
iperez/rocio-flux | iperez | "2025-01-05T01:26:55Z" | 7 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-01-05T00:43:20Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ROCIO
---
# Rocio Flux
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ROCIO` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('iperez/rocio-flux', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
rinfy/iloup | rinfy | "2023-05-04T07:19:46Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2023-05-04T07:18:23Z" | ---
license: creativeml-openrail-m
---
|
starosti/material-classification-model | starosti | "2025-04-10T12:01:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-10T11:24:46Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
XiWangEric/cultural_scholar-llama3 | XiWangEric | "2025-04-10T09:16:24Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-24T06:53:22Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
LoneStriker/Meta-Llama-3-70B-Instruct-2.25bpw-h6-exl2 | LoneStriker | "2024-04-19T15:32:19Z" | 7 | 6 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | "2024-04-19T15:23:21Z" | ---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
license_link: LICENSE
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
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Llama Materials set forth herein.
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4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Meta Llama 3 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]
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---
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda",
)
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 = [
tokenizer.eos_token_id,
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):])
```
### 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-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
ng; Zoe Papakipos
|
huggingtweets/politifact | huggingtweets | "2022-06-09T11:14:17Z" | 4 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-06-09T11:13:06Z" | ---
language: en
thumbnail: http://www.huggingtweets.com/politifact/1654773253130/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1286766140115517441/8rq6ZxZm_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">PolitiFact</div>
<div style="text-align: center; font-size: 14px;">@politifact</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from PolitiFact.
| Data | PolitiFact |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 680 |
| Short tweets | 14 |
| Tweets kept | 2556 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vfo2t7i/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @politifact's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7h3iptm6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7h3iptm6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/politifact')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
chkla/parlbert-topic-german | chkla | "2024-04-08T22:04:53Z" | 233 | 12 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-06-19T18:20:29Z" | ---
language: de
widget:
- text: Das Sachgebiet Investive Ausgaben des Bundes Bundesfinanzminister Apel hat gemäß BMF Finanznachrichten vom 1. Januar erklärt, die Investitionsquote des Bundes sei in den letzten zehn Jahren nahezu konstant geblieben.
---
### Welcome to ParlBERT-Topic-German!
🏷 **Model description**
This model was trained on \~10k manually annotated interpellations (📚 [Breunig/ Schnatterer 2019](https://www.tandfonline.com/doi/abs/10.1080/13572334.2021.2010395)) with topics from the [Comparative Agendas Project](https://www.comparativeagendas.net/datasets_codebooks) to classify text into one of twenty labels (annotation codebook).
_Note: "Interpellation is a formal request of a parliament to the respective government."([Wikipedia](https://en.wikipedia.org/wiki/Interpellation_(politics)))_
🗃 **Dataset**
| party | speeches | tokens |
|----|----|----|
| CDU/CSU | 7,635 | 4,862,654 |
| SPD | 5,321 | 3,158,315 |
| AfD | 3,465 | 1,844,707 |
| FDP | 3,067 | 1,593,108 |
| The Greens | 2,866 | 1,522,305 |
| The Left | 2,671 | 1,394,089 |
| cross-bencher | 200 | 86,170 |
🏃🏼♂️**Model training**
**ParlBERT-Topic-German** was fine-tuned on a domain adapted model (GermanBERT fine-tuned on [DeuParl](https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2889?show=full)) for topic modeling with an interpellations dataset (📚 [Breunig/ Schnatterer 2019](https://oxford.universitypressscholarship.com/view/10.1093/oso/9780198835332.001.0001/oso-9780198835332)) from the [Comparative Agendas Project](https://www.comparativeagendas.net/datasets_codebooks).
🤖 **Use**
```python
from transformers import pipeline
pipeline_classification_topics = pipeline("text-classification", model="chkla/parlbert-topic-german", return_all_scores=False)
text = "Das Sachgebiet Investive Ausgaben des Bundes Bundesfinanzminister Apel hat gemäß BMF Finanznachrichten vom 1. Januar erklärt, die Investitionsquote des Bundes sei in den letzten zehn Jahren nahezu konstant geblieben."
pipeline_classification_topics(text) # Macroeconomics
```
📊 **Evaluation**
The model was evaluated on an evaluation set (20%):
| Label | F1 | support |
|----|----|----|
| International | 80.0 | 1,126 |
| Defense | 85.0 | 1,099 |
| Government | 71.3 | 989 |
| Civil Rights | 76.5 | 978 |
| Environment | 76.6 | 845 |
| Transportation | 86.0 | 800 |
| Law & Crime | 67.1 | 492 |
| Energy | 78.6 | 424 |
| Health | 78.2 | 418 |
| Domestic Com. | 64.4 | 382 |
| Immigration | 81.0 | 376 |
| Labor | 69.1 | 344 |
| Macroeconom. | 62.8 | 339 |
| Agriculture | 76.3 | 292 |
| Social Welfare | 49.2 | 253 |
| Technology | 63.0 | 252 |
| Education | 71.6 | 183 |
| Housing | 79.6 | 178 |
| Foreign Trade | 61.5 | 139 |
| Culture | 54.6 | 69 |
| Public Lands | 45.4 | 55 |
⚠️ **Limitations**
Models are often highly topic dependent. Therefore, the model may perform less well on different topics and text types not included in the training set.
👥 **Cite**
```
@article{klamm2022frameast,
title={FrameASt: A Framework for Second-level Agenda Setting in Parliamentary Debates through the Lense of Comparative Agenda Topics},
author={Klamm, Christopher and Rehbein, Ines and Ponzetto, Simone},
journal={ParlaCLARIN III at LREC2022},
year={2022}
}
```
🐦 Twitter: [@chklamm](http://twitter.com/chklamm) |
stonesstones/ourea-tokenizer-case4-without-ns2 | stonesstones | "2025-03-28T07:42:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"ourea_tokenizer",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | "2025-03-28T07:42:14Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
lesso02/f51b504b-df87-45b1-9749-8353727a8727 | lesso02 | "2025-03-18T07:56:21Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | "2025-03-18T06:18:22Z" | ---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f51b504b-df87-45b1-9749-8353727a8727
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: codellama/CodeLlama-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- dd5dd85c883689d2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/dd5dd85c883689d2_train_data.json
type:
field_instruction: first_message
field_output: first_answer
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso02/f51b504b-df87-45b1-9749-8353727a8727
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000202
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/dd5dd85c883689d2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 20
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0c866bc9-a456-4346-8c6d-ad0e45b67204
wandb_project: 02a
wandb_run: your_name
wandb_runid: 0c866bc9-a456-4346-8c6d-ad0e45b67204
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f51b504b-df87-45b1-9749-8353727a8727
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3946
## 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: 0.000202
- train_batch_size: 4
- eval_batch_size: 4
- seed: 20
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0015 | 1 | 1.7423 |
| 1.4213 | 0.7493 | 500 | 1.3946 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
erik-engelhardt/distilbert-base-uncased-finetuned-emotion | erik-engelhardt | "2024-06-26T16:05:35Z" | 107 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-26T15:15:02Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.92
- name: F1
type: f1
value: 0.9198049122632036
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2387
- Accuracy: 0.92
- F1: 0.9198
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8562 | 1.0 | 250 | 0.3432 | 0.8955 | 0.8942 |
| 0.2619 | 2.0 | 500 | 0.2387 | 0.92 | 0.9198 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ridhodaffasyah/results | ridhodaffasyah | "2022-11-21T02:31:31Z" | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-11-20T16:18:20Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
approach0/dpr-math-aware-albert-220 | approach0 | "2023-04-15T03:46:07Z" | 161 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"azbert",
"pretraining",
"fill-mask",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | fill-mask | "2023-04-15T03:46:05Z" | ---
language: en
tags:
- azbert
- pretraining
- fill-mask
widget:
- text: "$f$ $($ $x$ [MASK] $y$ $)$"
example_title: "mathy"
- text: "$x$ [MASK] $x$ $equal$ $2$ $x$"
example_title: "mathy"
- text: "Proof by [MASK] that $n$ $fact$ $gt$ $3$ $n$ for $n$ $gt$ $6$"
example_title: "mathy"
- text: "Proof by induction that $n$ [MASK] $gt$ $3$ $n$ for $n$ $gt$ $6$"
example_title: "mathy"
- text: "The goal of life is [MASK]."
example_title: "philosophical"
license: mit
---
## About
This [repository](https://github.com/approach0/azbert) is a boilerplate to push a mask-filling model to the HuggingFace Model Hub.
### Upload to huggingface
Download your tokenizer, model checkpoints, and optionally the training logs (`events.out.*`) to the `./ckpt` directory (do not include any large files except `pytorch_model.bin` and log files `events.out.*`).
Optionally, test model using the MLM task:
```sh
pip install pya0 # for math token preprocessing
# testing local checkpoints:
python test.py ./ckpt/math-tokenizer ./ckpt/2-2-0/encoder.ckpt
# testing Model Hub checkpoints:
python test.py approach0/coco-mae-220 approach0/coco-mae-220
```
> **Note**
> Modify the test examples in `test.txt` to play with it.
> The test file is tab-separated, the first column is additional positions you want to mask for the right-side sentence (useful for masking tokens in math markups).
> A zero means no additional mask positions.
To upload to huggingface, use the `upload2hgf.sh` script.
Before runnig this script, be sure to check:
* `git-lfs` is installed
* having git-remote named `hgf` reference to `https://huggingface.co/your/repo`
* model contains all the files needed: `config.json` and `pytorch_model.bin`
* tokenizer contains all the files needed: `added_tokens.json`, `special_tokens_map.json`, `tokenizer_config.json`, `vocab.txt` and `tokenizer.json`
* no `tokenizer_file` field in `tokenizer_config.json` (sometimes it is located locally at `~/.cache`)
|
isspek/roberta-base_covid_gpt4o_1_2e-5_16_undersampling_0.5 | isspek | "2025-01-01T10:04:35Z" | 198 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-01-01T10:03:42Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
FrankX2025/finetuned-gemma | FrankX2025 | "2025-03-31T12:28:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-03-30T03:53:57Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/EtherealLight-12B-GGUF | mradermacher | "2025-02-24T05:39:42Z" | 0 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"ja",
"base_model:yamatazen/EtherealLight-12B",
"base_model:quantized:yamatazen/EtherealLight-12B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-02-23T14:58:56Z" | ---
base_model: yamatazen/EtherealLight-12B
language:
- en
- ja
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/yamatazen/EtherealLight-12B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/EtherealLight-12B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/EtherealLight-12B-GGUF/resolve/main/EtherealLight-12B.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
blockblockblock/airoboros-l2-13b-gpt4-1.4.1-bpw3.7-exl2 | blockblockblock | "2024-05-22T06:52:14Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | "2024-05-22T06:49:48Z" | ---
license: other
datasets:
- jondurbin/airoboros-gpt4-1.4.1
---
### Overview
Llama 2 13b fine tune using https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1
See the previous llama 65b model card for info:
https://hf.co/jondurbin/airoboros-65b-gpt4-1.4
### Licence and usage restrictions
This model was built on llama-2, which has a proprietary/custom Meta license.
- See the LICENSE.txt file attached for the original license, along with USE_POLICY.md which was also provided by Meta.
The data used to fine-tune the llama-2-13b-hf model was generated by GPT4 via OpenAI API calls.using [airoboros](https://github.com/jondurbin/airoboros)
- The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant of copyrighted or otherwise unallowable licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me. |
LinJTF/falcon-7b-qlora-medicine-chat | LinJTF | "2023-07-24T01:16:34Z" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2023-07-09T19:20:02Z" | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
huggingtweets/mavimasa | huggingtweets | "2021-11-18T07:48:24Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-03-02T23:29:05Z" | ---
language: en
thumbnail: http://www.huggingtweets.com/mavimasa/1637221645468/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1228330296480759809/VcT9cYtK_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Başkent153</div>
<div style="text-align: center; font-size: 14px;">@mavimasa</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Başkent153.
| Data | Başkent153 |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 605 |
| Tweets kept | 2645 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/k1bmfsp6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mavimasa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21cd6mvi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21cd6mvi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mavimasa')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
asadnaqvi/setfitabsa-aspect | asadnaqvi | "2024-05-06T09:49:05Z" | 12 | 2 | setfit | [
"setfit",
"safetensors",
"bert",
"absa",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"model-index",
"region:us"
] | text-classification | "2024-05-05T13:00:08Z" | ---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: closures:Runa Sarkar, a professor at the Indian Institute of Management Calcutta,
said the coal mining region most affected by mine closures is West Bengal.
- text: comment:Neither the Russian nor the Chinese defence ministries responded to
Reuters' requests for comment.
- text: 'Canada:The statements made in Canada''s parliament were finally an acknowledgement
of the reality that young Sikhs like me have lived through for decades: Sikh dissidents
expressing their support for an independent state may face the risk of imminent
harm, even in the diaspora.'
- text: France:The Paris Agreement, a legally binding international treaty on climate
change adopted by 196 parties at the UN Climate Change Conference (COP21) in Paris,
France in December 2015, aims to hold the increase in the global average temperature
to well below 2°C above pre-industrial levels.
- text: 'risk:The statements made in Canada''s parliament were finally an acknowledgement
of the reality that young Sikhs like me have lived through for decades: Sikh dissidents
expressing their support for an independent state may face the risk of imminent
harm, even in the diaspora.'
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Aspect Model with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7630057803468208
name: Accuracy
---
# SetFit Aspect Model with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [asadnaqvi/setfitabsa-aspect](https://huggingface.co/asadnaqvi/setfitabsa-aspect)
- **SetFitABSA Polarity Model:** [asadnaqvi/setfitabsa-polarity](https://huggingface.co/asadnaqvi/setfitabsa-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | <ul><li>"visit:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"Mohammed bin Salman:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>'legitimacy:The trip to India is evidently timed to burnish his legitimacy after the international opprobrium that followed the murder of The Washington Post journalist Jamal Khashoggi.'</li></ul> |
| no aspect | <ul><li>"Saudi Arabia:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"MBS:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"India:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7630 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"asadnaqvi/setfitabsa-aspect",
"asadnaqvi/setfitabsa-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 8 | 25.2939 | 40 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 248 |
| aspect | 99 |
### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:-------:|:-------------:|:---------------:|
| 0.0018 | 1 | 0.2598 | - |
| 0.0893 | 50 | 0.2458 | 0.2547 |
| 0.1786 | 100 | 0.2418 | 0.2522 |
| 0.2679 | 150 | 0.2427 | 0.2452 |
| **0.3571** | **200** | **0.1272** | **0.2419** |
| 0.4464 | 250 | 0.0075 | 0.2853 |
| 0.5357 | 300 | 0.0023 | 0.3134 |
| 0.625 | 350 | 0.0021 | 0.3138 |
| 0.7143 | 400 | 0.0037 | 0.3502 |
| 0.8036 | 450 | 0.011 | 0.3437 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
LarryAIDraw/YanuToF | LarryAIDraw | "2023-12-26T23:33:36Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2023-12-26T23:29:55Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/244286/character-yanuo-tower-of-fantasy |
bunnycore/Llama-3-Intermix | bunnycore | "2024-07-20T11:00:15Z" | 6 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-07-20T10:55:15Z" | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
---
# Llama-3-Intermix
Llama-3-Intermix is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
## 🧩 Configuration
```yaml
models:
- model: PJMixers/LLaMa-3-CursedStock-v2.0-8B
- model: Nitral-AI/Hathor_Stable-v0.2-L3-8B
- model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
merge_method: model_stock
base_model: PJMixers/LLaMa-3-CursedStock-v2.0-8B
dtype: bfloat16
``` |
Etso/finetuning-sentiment-model-3000-samples | Etso | "2024-12-05T15:32:51Z" | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-05T15:22:17Z" | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3685
- Accuracy: 0.8733
- F1: 0.8766
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Helsinki-NLP/opus-mt-en-cus | Helsinki-NLP | "2023-11-28T09:51:00Z" | 125 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"en",
"so",
"cus",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2022-03-02T23:29:04Z" | ---
language:
- en
- so
- cus
tags:
- translation
license: apache-2.0
---
### eng-cus
* source group: English
* target group: Cushitic languages
* OPUS readme: [eng-cus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cus/README.md)
* model: transformer
* source language(s): eng
* target language(s): som
* model: transformer
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng.multi | 16.0 | 0.173 |
| Tatoeba-test.eng-som.eng.som | 16.0 | 0.173 |
### System Info:
- hf_name: eng-cus
- source_languages: eng
- target_languages: cus
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cus/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'so', 'cus']
- src_constituents: {'eng'}
- tgt_constituents: {'som'}
- src_multilingual: False
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.test.txt
- src_alpha3: eng
- tgt_alpha3: cus
- short_pair: en-cus
- chrF2_score: 0.17300000000000001
- bleu: 16.0
- brevity_penalty: 1.0
- ref_len: 3.0
- src_name: English
- tgt_name: Cushitic languages
- train_date: 2020-08-01
- src_alpha2: en
- tgt_alpha2: cus
- prefer_old: False
- long_pair: eng-cus
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
BIFOLD-BigEarthNetv2-0/convmixer_768_32-s2-v0.1.1 | BIFOLD-BigEarthNetv2-0 | "2024-10-10T07:18:48Z" | 9 | 0 | configilm | [
"configilm",
"safetensors",
"convmixer_768_32",
"BigEarthNet v2.0",
"Remote Sensing",
"Classification",
"image-classification",
"Multispectral",
"arxiv:2407.03653",
"license:mit",
"region:us"
] | image-classification | "2024-06-28T00:13:42Z" | ---
thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png"
tags:
- convmixer_768_32
- BigEarthNet v2.0
- Remote Sensing
- Classification
- image-classification
- Multispectral
library_name: configilm
license: mit
widget:
- src: example.png
example_title: Example
output:
- label: Agro-forestry areas
score: 0.000000
- label: Arable land
score: 0.000000
- label: Beaches, dunes, sands
score: 0.000000
- label: Broad-leaved forest
score: 0.000000
- label: Coastal wetlands
score: 0.000000
---
[TU Berlin](https://www.tu.berlin/) | [RSiM](https://rsim.berlin/) | [DIMA](https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/) | [BigEarth](http://www.bigearth.eu/) | [BIFOLD](https://bifold.berlin/)
:---:|:---:|:---:|:---:|:---:
<a href="https://www.tu.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/tu-berlin-logo-long-red.svg" style="font-size: 1rem; height: 2em; width: auto" alt="TU Berlin Logo"/> | <a href="https://rsim.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" style="font-size: 1rem; height: 2em; width: auto" alt="RSiM Logo"> | <a href="https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/DIMA.png" style="font-size: 1rem; height: 2em; width: auto" alt="DIMA Logo"> | <a href="http://www.bigearth.eu/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BigEarth.png" style="font-size: 1rem; height: 2em; width: auto" alt="BigEarth Logo"> | <a href="https://bifold.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BIFOLD_Logo_farbig.png" style="font-size: 1rem; height: 2em; width: auto; margin-right: 1em" alt="BIFOLD Logo">
# Convmixer_768_32 pretrained on BigEarthNet v2.0 using Sentinel-2 bands
<!-- Optional images -->
<!--
[Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) | [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2)
:---:|:---:
<a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_2.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-2 Satellite"/> | <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-2"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_1.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-1 Satellite"/>
-->
> **_NOTE:_** This version of the model has been trained with a different band order that is not compatible with the newer versions and does not match the order proposed in the technical documentation of Sentinel-2.
>
> The following bands (in the specified order) were used to train the models with version 0.1.1:
> - For models using Sentinel-1 only: Sentinel-1 bands `["VH", "VV"]`
> - For models using Sentinel-2 only: Sentinel-2 10m bands and 20m bands `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A"]`
> - For models using Sentinel-1 and Sentinel-2: Sentinel-2 10m bands and 20m bands and Sentinel-1 bands = `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A", "VH", "VV"]`
>
> Newer models are compatible with the order in the technical documentation of Sentinel-2 and were trained with the following band order:
> - For models using Sentinel-1 only: Sentinel-1 bands `["VV", "VH"]`
> - For models using Sentinel-2 only: Sentinel-2 10m bands and 20m bands `["B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12"]`
> - For models using Sentinel-1 and Sentinel-2: Sentinel-1 bands and Sentinel-2 10m bands and 20m bands `["VV", "VH", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12"]`
This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using
the Sentinel-2 bands.
It was trained using the following parameters:
- Number of epochs: up to 100 (with early stopping after 5 epochs of no improvement based on validation average
precision macro)
- Batch size: 512
- Learning rate: 0.001
- Dropout rate: 0.15
- Drop Path rate: 0.15
- Learning rate scheduler: LinearWarmupCosineAnnealing for 1000 warmup steps
- Optimizer: AdamW
- Seed: 42
The weights published in this model card were obtained after 28 training epochs.
For more information, please visit the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts), where you can find the training scripts.
](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg)
The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results:
| Metric | Macro | Micro |
|:------------------|------------------:|------------------:|
| Average Precision | 0.696914 | 0.857402 |
| F1 Score | 0.638002 | 0.761015 |
| Precision | 0.696914 | 0.857402 |
# Example
| A Sentinel-2 image (true color representation) |
|:---------------------------------------------------:|
| ](example.png) |
| Class labels | Predicted scores |
|:--------------------------------------------------------------------------|--------------------------------------------------------------------------:|
| <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> | <p> 0.000000 <br> 0.000000 <br> 0.000000 <br> ... <br> 0.000000 </p> |
To use the model, download the codes that define the model architecture from the
[official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts) and load the model using the
code below. Note that you have to install [`configilm`](https://pypi.org/project/configilm/) to use the provided code.
```python
from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder")
```
e.g.
```python
from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
model = BigEarthNetv2_0_ImageClassifier.from_pretrained(
"BIFOLD-BigEarthNetv2-0/convmixer_768_32-s2-v0.1.1")
```
If you use this model in your research or the provided code, please cite the following papers:
```bibtex
@article{clasen2024refinedbigearthnet,
title={reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis},
author={Clasen, Kai Norman and Hackel, Leonard and Burgert, Tom and Sumbul, Gencer and Demir, Beg{\"u}m and Markl, Volker},
year={2024},
eprint={2407.03653},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.03653},
}
```
```bibtex
@article{hackel2024configilm,
title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering},
author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m},
journal={SoftwareX},
volume={26},
pages={101731},
year={2024},
publisher={Elsevier}
}
```
|
lucien1011/SpaceInvadersNoFrameskip-v4-250322-v01 | lucien1011 | "2025-03-22T14:23:44Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-03-22T14:23:11Z" | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 534.50 +/- 150.41
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lucien1011 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lucien1011 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga lucien1011
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
pshebel/Llama-3.2-3B-Instruct-Q4_K_M-GGUF | pshebel | "2025-02-28T20:24:29Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-02-28T20:24:18Z" | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: llama3.2
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base_model: meta-llama/Llama-3.2-3B-Instruct
---
# pshebel/Llama-3.2-3B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo pshebel/Llama-3.2-3B-Instruct-Q4_K_M-GGUF --hf-file llama-3.2-3b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo pshebel/Llama-3.2-3B-Instruct-Q4_K_M-GGUF --hf-file llama-3.2-3b-instruct-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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo pshebel/Llama-3.2-3B-Instruct-Q4_K_M-GGUF --hf-file llama-3.2-3b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo pshebel/Llama-3.2-3B-Instruct-Q4_K_M-GGUF --hf-file llama-3.2-3b-instruct-q4_k_m.gguf -c 2048
```
|
ngwgsang/phobert-base-qc-sentence-3e5 | ngwgsang | "2025-03-03T08:13:16Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-02-28T01:42:29Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- 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] |
nintwentydo/Razorback-12B-v0.2 | nintwentydo | "2025-01-10T06:10:08Z" | 8 | 2 | transformers | [
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"mergekit",
"merge",
"multimodal",
"mistral",
"pixtral",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"base_model:TheDrummer/UnslopNemo-12B-v3",
"base_model:merge:TheDrummer/UnslopNemo-12B-v3",
"base_model:mistralai/Pixtral-12B-2409",
"base_model:merge:mistralai/Pixtral-12B-2409",
"license:other",
"endpoints_compatible",
"region:us"
] | image-text-to-text | "2025-01-10T05:07:33Z" | ---
base_model:
- mistralai/Pixtral-12B-2409
- TheDrummer/UnslopNemo-12B-v3
base_model_relation: merge
library_name: transformers
tags:
- mergekit
- merge
- multimodal
- mistral
- pixtral
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: other
pipeline_tag: image-text-to-text
---
# Razorback 12B v0.2
#### UnslopNemo with Vision!
<img src="https://huggingface.co/nintwentydo/Razorback-12B-v0.1/resolve/main/razorback.jpg" style="width: 100%; max-width:700px"></img>
A more robust attempt at merging TheDrummer's UnslopNemo v3 into Pixtral 12B.
Has been really stable in my testing so far. Needs more testing to see what samplers it does/doesn't like.
Seems to be the best of both worlds - less sloppy, more engaging content and decent intelligence / visual understanding.
## Merging Approach
First, I loaded up Pixtral 12B Base and Mistral Nemo Base to compare their parameter differences.
Looking at the L2 norm / relative difference values I was able to isolate which parts of Pixtral 12B are a significant deviation from Mistral Nemo.
Because while the language model architecture is the same between the two, a lot of vision understanding has been trained into Pixtral's language model and can break very easily.
Then I calculated merging weights for each parameter using an exponential falloff. The smaller the difference, the higher the weight.
Applied this recipe to Pixtral Instruct (Pixtral-12B-2409) and TheDrummer's UnslopNemo-12B-v3. The goal is to infuse as much Drummer goodness without breaking vision input. And it looks like it's worked!
## Usage
Needs more testing to identify best sampling params, but so far just using ~0.7 temp + 0.03 min p has been rock solid.
Use the included chat template (Mistral). No chatml support yet.
## Credits
- Mistral for [mistralai/Pixtral-12B-2409](https://huggingface.co/mistralai/Pixtral-12B-2409)
- Unsloth for [unsloth/Pixtral-12B-2409](https://huggingface.co/unsloth/Pixtral-12B-2409) transformers conversion
- TheDrummer for [TheDrummer/UnslopNemo-12B-v3](https://huggingface.co/TheDrummer/UnslopNemo-12B-v3) |
ZhaofengWu/transparency-models | ZhaofengWu | "2022-12-02T19:16:13Z" | 0 | 0 | null | [
"tensorboard",
"arxiv:2210.07468",
"license:apache-2.0",
"region:us"
] | null | "2022-12-01T20:33:18Z" | ---
license: apache-2.0
---
Pretrained model for our paper (https://arxiv.org/abs/2210.07468)
```bibtex
@inproceedings{wu-etal-2022-continued,
title = "Transparency Helps Reveal When Language Models Learn Meaning",
author = "Zhaofeng Wu and William Merrill and Hao Peng and Iz Beltagy and Noah A. Smith",
url = {https://arxiv.org/abs/2210.07468},
publisher = {arXiv},
year = {2022},
doi = {10.48550/ARXIV.2210.07468},
}
```
Please see the "Files and versions" tab for the models.
|
guymorlan/factappeal_gemma2_9b_full | guymorlan | "2025-02-18T22:37:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-18T22:19:03Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
micdestefano/ppo-Pyramids | micdestefano | "2023-12-23T19:56:21Z" | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | "2023-12-23T19:35:00Z" | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: micdestefano/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
deepghs/waifu2x_onnx | deepghs | "2024-10-17T16:50:01Z" | 0 | 1 | null | [
"onnx",
"art",
"region:us"
] | null | "2023-09-20T15:29:19Z" | ---
tags:
- art
---
waifu2x's ONNX model, sourced from [nagadomi/nunif](https://github.com/nagadomi/nunif/releases/tag/0.0.0).
If this model repository has infringed upon your rights, please contact the DeepGHS team to have it removed.
|
Jonjew/MackenzieZiegler | Jonjew | "2025-03-17T00:52:14Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] | text-to-image | "2025-03-17T00:52:08Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
<lora:Mackenzie_Ziegler_Flux:1> This is a beautiful photograph of a woman,
blonde hair cascading over her shoulders. wearing a boatneck dress. standing
in cafe, looking at the viewer, smiling
output:
url: images/00002-217836278.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: unknown
---
# Mackenzie Ziegler
<Gallery />
## Model description
FROM https://civitai.com/models/1160028/mackenzie-ziegler-flux?modelVersionId=1304810
Strength 1
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jonjew/MackenzieZiegler/tree/main) them in the Files & versions tab.
|
pogtador/codebert-fine-tuned | pogtador | "2025-02-21T02:45:31Z" | 2 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:microsoft/codebert-base",
"base_model:finetune:microsoft/codebert-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2025-02-20T09:16:47Z" | ---
library_name: transformers
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
model-index:
- name: codebert-fine-tuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codebert-fine-tuned
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0908
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 3.5941 | 0.0325 | 500 | 2.0780 |
| 2.091 | 0.0651 | 1000 | 1.8173 |
| 1.9005 | 0.0976 | 1500 | 1.6783 |
| 1.7817 | 0.1301 | 2000 | 1.6071 |
| 1.712 | 0.1626 | 2500 | 1.5634 |
| 1.6661 | 0.1952 | 3000 | 1.5229 |
| 1.6348 | 0.2277 | 3500 | 1.4965 |
| 1.6106 | 0.2602 | 4000 | 1.4514 |
| 1.5685 | 0.2928 | 4500 | 1.4360 |
| 1.5419 | 0.3253 | 5000 | 1.4203 |
| 1.5429 | 0.3578 | 5500 | 1.4026 |
| 1.5069 | 0.3903 | 6000 | 1.3959 |
| 1.5021 | 0.4229 | 6500 | 1.3819 |
| 1.4651 | 0.4554 | 7000 | 1.3660 |
| 1.4704 | 0.4879 | 7500 | 1.3544 |
| 1.4799 | 0.5205 | 8000 | 1.3428 |
| 1.44 | 0.5530 | 8500 | 1.3357 |
| 1.4433 | 0.5855 | 9000 | 1.3224 |
| 1.4297 | 0.6180 | 9500 | 1.3173 |
| 1.4115 | 0.6506 | 10000 | 1.3069 |
| 1.4119 | 0.6831 | 10500 | 1.2996 |
| 1.3908 | 0.7156 | 11000 | 1.2972 |
| 1.4022 | 0.7482 | 11500 | 1.2879 |
| 1.381 | 0.7807 | 12000 | 1.2843 |
| 1.374 | 0.8132 | 12500 | 1.2747 |
| 1.382 | 0.8457 | 13000 | 1.2734 |
| 1.3746 | 0.8783 | 13500 | 1.2576 |
| 1.3724 | 0.9108 | 14000 | 1.2605 |
| 1.3404 | 0.9433 | 14500 | 1.2560 |
| 1.3452 | 0.9759 | 15000 | 1.2414 |
| 1.3433 | 1.0084 | 15500 | 1.2373 |
| 1.3273 | 1.0409 | 16000 | 1.2398 |
| 1.3175 | 1.0735 | 16500 | 1.2311 |
| 1.3123 | 1.1060 | 17000 | 1.2217 |
| 1.3095 | 1.1385 | 17500 | 1.2213 |
| 1.3229 | 1.1710 | 18000 | 1.2167 |
| 1.2995 | 1.2036 | 18500 | 1.2185 |
| 1.3019 | 1.2361 | 19000 | 1.2144 |
| 1.299 | 1.2686 | 19500 | 1.2093 |
| 1.2784 | 1.3012 | 20000 | 1.1990 |
| 1.2886 | 1.3337 | 20500 | 1.2032 |
| 1.2788 | 1.3662 | 21000 | 1.1943 |
| 1.284 | 1.3987 | 21500 | 1.1975 |
| 1.2706 | 1.4313 | 22000 | 1.1878 |
| 1.2771 | 1.4638 | 22500 | 1.1856 |
| 1.2731 | 1.4963 | 23000 | 1.1797 |
| 1.2607 | 1.5289 | 23500 | 1.1919 |
| 1.2729 | 1.5614 | 24000 | 1.1872 |
| 1.272 | 1.5939 | 24500 | 1.1712 |
| 1.251 | 1.6264 | 25000 | 1.1656 |
| 1.2437 | 1.6590 | 25500 | 1.1665 |
| 1.2523 | 1.6915 | 26000 | 1.1697 |
| 1.2393 | 1.7240 | 26500 | 1.1546 |
| 1.2521 | 1.7566 | 27000 | 1.1595 |
| 1.2498 | 1.7891 | 27500 | 1.1541 |
| 1.2187 | 1.8216 | 28000 | 1.1586 |
| 1.2311 | 1.8541 | 28500 | 1.1530 |
| 1.2419 | 1.8867 | 29000 | 1.1412 |
| 1.2246 | 1.9192 | 29500 | 1.1460 |
| 1.2381 | 1.9517 | 30000 | 1.1475 |
| 1.2237 | 1.9843 | 30500 | 1.1432 |
| 1.2273 | 2.0168 | 31000 | 1.1458 |
| 1.2167 | 2.0493 | 31500 | 1.1368 |
| 1.2039 | 2.0818 | 32000 | 1.1358 |
| 1.2142 | 2.1144 | 32500 | 1.1410 |
| 1.2003 | 2.1469 | 33000 | 1.1278 |
| 1.2052 | 2.1794 | 33500 | 1.1344 |
| 1.2094 | 2.2120 | 34000 | 1.1378 |
| 1.2128 | 2.2445 | 34500 | 1.1291 |
| 1.1936 | 2.2770 | 35000 | 1.1280 |
| 1.195 | 2.3095 | 35500 | 1.1278 |
| 1.207 | 2.3421 | 36000 | 1.1220 |
| 1.1969 | 2.3746 | 36500 | 1.1248 |
| 1.188 | 2.4071 | 37000 | 1.1159 |
| 1.1921 | 2.4397 | 37500 | 1.1187 |
| 1.1916 | 2.4722 | 38000 | 1.1196 |
| 1.1797 | 2.5047 | 38500 | 1.1167 |
| 1.1865 | 2.5372 | 39000 | 1.1135 |
| 1.1787 | 2.5698 | 39500 | 1.1154 |
| 1.1865 | 2.6023 | 40000 | 1.1174 |
| 1.1754 | 2.6348 | 40500 | 1.1161 |
| 1.1805 | 2.6674 | 41000 | 1.1085 |
| 1.1786 | 2.6999 | 41500 | 1.1116 |
| 1.1689 | 2.7324 | 42000 | 1.1069 |
| 1.1755 | 2.7649 | 42500 | 1.1032 |
| 1.1858 | 2.7975 | 43000 | 1.1027 |
| 1.1722 | 2.8300 | 43500 | 1.1027 |
| 1.1686 | 2.8625 | 44000 | 1.1002 |
| 1.1801 | 2.8951 | 44500 | 1.1016 |
| 1.1596 | 2.9276 | 45000 | 1.1024 |
| 1.1788 | 2.9601 | 45500 | 1.1052 |
| 1.1609 | 2.9926 | 46000 | 1.0908 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
nbninh/9cc698fc-7f6d-49cb-b09c-2eb1268b4b79 | nbninh | "2025-01-15T21:25:47Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codellama-7b",
"base_model:adapter:unsloth/codellama-7b",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-15T21:08:54Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/codellama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9cc698fc-7f6d-49cb-b09c-2eb1268b4b79
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/codellama-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- db319291db439759_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/db319291db439759_train_data.json
type:
field_instruction: original
field_output: reference
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nbninh/9cc698fc-7f6d-49cb-b09c-2eb1268b4b79
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/db319291db439759_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3fd1ec50-91e1-4e9c-a1cb-46229e41c04c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3fd1ec50-91e1-4e9c-a1cb-46229e41c04c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 9cc698fc-7f6d-49cb-b09c-2eb1268b4b79
This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0797
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0508 | 0.2909 | 200 | 0.0797 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
orgcatorg/nllb-200-distilled-600M-lo | orgcatorg | "2024-10-09T02:32:05Z" | 8 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"m2m_100",
"text2text-generation",
"nllb",
"translation",
"lo",
"dataset:flores-200",
"dataset:orgcatorg/multilingual",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"region:us"
] | translation | "2023-09-29T19:20:46Z" | ---
language:
- lo
language_details: >-
ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab,
aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab,
asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl,
bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn,
bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn,
cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn,
dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn,
ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn,
fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr,
hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn,
hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn,
jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva,
kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr,
kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn,
lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn,
ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva,
mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn,
mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn,
nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn,
gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn,
prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn,
san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn,
smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn,
srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn,
tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi,
taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn,
tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab,
uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr,
yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn
tags:
- nllb
- translation
license: cc-by-nc-4.0
datasets:
- flores-200
- orgcatorg/multilingual
metrics:
- bleu
- spbleu
- chrf++
inference: false
---
# NLLB-200
This is the model card of NLLB-200's distilled 600M variant.
Here are the [metrics](https://tinyurl.com/nllb200densedst600mmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues
## Intended Use
- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.
- Primary intended users: Primary users are researchers and machine translation research community.
- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.
## Metrics
• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.
## Evaluation Data
- Datasets: Flores-200 dataset is described in Section 4
- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200
- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The
SentencePiece model is released along with NLLB-200.
## Training Data
• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.
## Ethical Considerations
• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).
## Caveats and Recommendations
• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.
## Carbon Footprint Details
• The carbon dioxide (CO2e) estimate is reported in Section 8.8. |
ingmarnitze/yolov8_arcticbeavers | ingmarnitze | "2024-04-22T10:04:45Z" | 0 | 0 | null | [
"yolov8",
"remote sensing",
"aerial imagery",
"beaver",
"object detection",
"object-detection",
"license:mit",
"region:us"
] | object-detection | "2024-02-18T15:13:50Z" | ---
license: mit
pipeline_tag: object-detection
tags:
- yolov8
- remote sensing
- aerial imagery
- beaver
- object detection
---
# This is a yolov8 based object detection model for beaver dams and lodges from aerial imagery
This is a semi-serious side-project to detect beaver dams and lodges from aerial imagery.
Beavers are expanding into Arctic regions, which can be even observed indirectly from space.
With very-high resolution data from UAV or airborne missions, we can try to map dams and lodges directly.
#### More cool information on beaver expansion into the Arctic:
* Tape, K. D., Clark, J. A., Jones, B. M., Kantner, S., Gaglioti, B. V., Grosse, G., & Nitze, I. (2022). Expanding beaver pond distribution in Arctic Alaska, 1949 to 2019. Scientific Reports, 12(1), 7123. https://doi.org/10.1038/s41598-022-09330-6
* Jones, B. M., Tape, K. D., Clark, J. A., Nitze, I., Grosse, G., & Disbrow, J. (2020). Increase in beaver dams controls surface water and thermokarst dynamics in an Arctic tundra region, Baldwin Peninsula, northwestern Alaska. Environmental Research Letters, 15(7), 075005. https://doi.org/10.1088/1748-9326/ab80f1
* Tape, K. D., Jones, B. M., Arp, C. D., Nitze, I., & Grosse, G. (2018). Tundra be dammed: Beaver colonization of the Arctic. Global Change Biology, 24(10), 4478–4488. https://doi.org/10.1111/gcb.14332
## Info
- Model file in pytorch format for ultralytics yolov8
- bounding boxes of beaver dams and lodges
- trained on aerial imagery from West and Northwest Alaska
- More info: https://essd.copernicus.org/preprints/essd-2023-193/
- This model takes RGB aerial images in high spatial resolution, suhc as UAV or airborne imagery. It was trained on images from tundra regions in NW Alaska.
- Target objects were hand labelled with roboflow --> https://app.roboflow.com/awi-response/beaver-finder-vhr-imagery-a9hg9/
## Related Code
### github
https://github.com/initze/yolov8_object_detection/
### Input data
RGB images
### Known and potential issues
- false positives for curved shore areas
## Classes
1: beaver dam
2: beaver lodge
3: building (not great)
## Input data
## Examples
### The good ones
### The bad ones
 |
Kort/xf2 | Kort | "2025-03-03T14:35:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-03T13:40:34Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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