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nathanReitinger/CELEB_A-diffusion-10000-500 | nathanReitinger | "2024-06-14T04:00:03Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | null | "2024-06-11T00:26:38Z" | ---
license: mit
---
|
Makkoen/whisper-large-cit-synth-do0.15-wd0-lr1e-06 | Makkoen | "2024-06-11T00:29:03Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"base_model:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-11T00:27:24Z" | ---
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ./whisper-large-cit-synth-do0.15-wd0-lr1e-06
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ./whisper-large-cit-synth-do0.15-wd0-lr1e-06
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the SF 200 synth 2000 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5469
- Wer: 52.8152
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 2.3143 | 0.0808 | 10 | 2.3730 | 62.3073 |
| 2.3217 | 0.1616 | 20 | 2.2910 | 60.9606 |
| 2.4278 | 0.2424 | 30 | 2.1875 | 59.9059 |
| 2.0697 | 0.3232 | 40 | 2.1152 | 59.2082 |
| 1.9675 | 0.4040 | 50 | 2.0410 | 59.1270 |
| 1.891 | 0.4848 | 60 | 1.9668 | 57.3098 |
| 1.7818 | 0.5657 | 70 | 1.8887 | 56.4660 |
| 1.7774 | 0.6465 | 80 | 1.8145 | 55.1030 |
| 1.8454 | 0.7273 | 90 | 1.7588 | 53.7238 |
| 1.6584 | 0.8081 | 100 | 1.7129 | 53.0586 |
| 2.0202 | 0.8889 | 110 | 1.6738 | 52.4258 |
| 1.6375 | 0.9697 | 120 | 1.6416 | 51.9552 |
| 1.3514 | 1.0505 | 130 | 1.6172 | 51.6307 |
| 1.5016 | 1.1313 | 140 | 1.5977 | 51.1601 |
| 1.8013 | 1.2121 | 150 | 1.5811 | 50.9330 |
| 1.4723 | 1.2929 | 160 | 1.5693 | 52.9937 |
| 1.5952 | 1.3737 | 170 | 1.5605 | 52.8152 |
| 1.4724 | 1.4545 | 180 | 1.5527 | 52.8476 |
| 1.4115 | 1.5354 | 190 | 1.5488 | 52.7990 |
| 1.5161 | 1.6162 | 200 | 1.5469 | 52.8152 |
### Framework versions
- Transformers 4.41.2
- Pytorch 1.13.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
|
1024m/WASSA24-Task1-3a-LLAMA3-8B-TP-lora | 1024m | "2024-06-11T00:33:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T00:32:51Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
datek/Qwen-Qwen1.5-1.8B-1718065989 | datek | "2024-06-11T00:33:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:33:10Z" | Entry not found |
1024m/WASSA24-Task1-3b-LLAMA3-8B-TP-lora | 1024m | "2024-06-11T00:33:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T00:33:39Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MubarakB/wav2vec2-large-xls-r-300m-zu-v1 | MubarakB | "2024-06-11T00:36:18Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-11T00:33:41Z" | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-zu-v1
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. -->
# wav2vec2-large-xls-r-300m-zu-v1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
psakamoori/speechbrain-emotion-recognition-openvino | psakamoori | "2024-06-12T02:54:18Z" | 0 | 0 | speechbrain | [
"speechbrain",
"audio-classification",
"Emotion",
"Recognition",
"wav2vec2",
"pytorch",
"en",
"dataset:iemocap",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | audio-classification | "2024-06-11T00:35:57Z" | ---
language: "en"
thumbnail:
tags:
- audio-classification
- speechbrain
- Emotion
- Recognition
- wav2vec2
- pytorch
license: "apache-2.0"
datasets:
- iemocap
metrics:
- Accuracy
inference: false
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Emotion Recognition with wav2vec2 base on IEMOCAP
This repository provides all the necessary tools to perform emotion recognition with a fine-tuned wav2vec2 (base) model using SpeechBrain.
It is trained on IEMOCAP training data.
For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The model performance on IEMOCAP test set is:
| Release | Accuracy(%) |
|:-------------:|:--------------:|
| 19-10-21 | 78.7 (Avg: 75.3) |
## Pipeline description
This system is composed of an wav2vec2 model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed.
## Install SpeechBrain
First of all, please install the **development** version of SpeechBrain with the following command:
```
pip install git+https://github.com/speechbrain/speechbrain.git@$develop
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Perform Emotion recognition
An external `py_module_file=custom.py` is used as an external Predictor class into this HF repos. We use `foreign_class` function from `speechbrain.pretrained.interfaces` that allow you to load you custom model.
```python
from speechbrain.inference.interfaces import foreign_class
classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
out_prob, score, index, text_lab = classifier.classify_file("speechbrain/emotion-recognition-wav2vec2-IEMOCAP/anger.wav")
print(text_lab)
```
The prediction tensor will contain a tuple of (embedding, id_class, label_name).
### Perform Emotion recognition with OpenVINO backend
Unlocking the power of acceleration with [Intel OpenVINO](https://docs.openvino.ai/) runtime as inference "backend", deploy across a mix of [Intel® hardware and environments](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes.html), on-premises and on-device, in the browser or in the cloud. Passing backend as "openvino" in CustomEncoderWav2vec2Classifier class in custom_interface.py, which supports Intel OpenVINO backend to perform model inference with target device.
### Steps to perform model inference with OpenVINO
#### Step 1: Install speechbrain and OpenVINO
First, ensure you have the necessary dependencies installed. Run the following commands to install the development version of SpeechBrain, OpenVINO, and the required version of the transformers library:
```
pip install git+https://github.com/speechbrain/speechbrain.git@develop --extra-index-url https://download.pytorch.org/whl/cpu
pip install "openvino>=2024.1.0"
pip install "transformers>=4.30.0"
```
#### Step 2: Run inference with OpenVINO backend
To run inference using the OpenVINO backend, you can use a sample application. Below is an example script (app.py) demonstrating how to set up and run the model inference:
```
config = {hints.performance_mode: hints.PerformanceMode.THROUGHPUT}
ov_opts = {"ov_device": "CPU", "config": config}
torch_device = "cpu"
instance = CustomEncoderWav2vec2Classifier(modules=checkpoint.mods,
hparams=hparams_dict, model=classifier.mods["wav2vec2"].model,
audio_file_path="speechbrain/emotion-recognition-wav2vec2-IEMOCAP/anger.wav",
backend="openvino",
opts=ov_opts,
torch_device=torch_device,
save_ov_model=False)
```
To execute the application, simply run:
```
python app.py
```
For more detailed information on optimizing inference with OpenVINO, refer to the [OpenVINO Optimization Guide](
https://docs.openvino.ai/2024/openvino-workflow/running-inference/optimize-inference.html)
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Training
The model was trained with SpeechBrain (aa018540).
To train it from scratch follows these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```
cd recipes/IEMOCAP/emotion_recognition
python train_with_wav2vec2.py hparams/train_with_wav2vec2.yaml --data_folder=your_data_folder
```
You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/15dKQetLuAhSyg4sNOtbSDnuxFdEeU4zQ?usp=sharing).
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
|
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-100-percent | AdamKasumovic | "2024-06-11T00:39:46Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-11T00:36:17Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
haturusinghe/xlm_r_base-finetuned_after_mrp-effortless-glade-5 | haturusinghe | "2024-06-11T00:36:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:36:56Z" | Entry not found |
Jue123/pre-trained | Jue123 | "2024-06-11T00:37:34Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:37:15Z" | Entry not found |
haturusinghe/xlm_r_base-finetuned_after_mrp-visionary-smoke-6 | haturusinghe | "2024-06-11T00:39:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:39:49Z" | Entry not found |
Jue123/llama_model_weights | Jue123 | "2024-06-11T00:44:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:40:13Z" | Entry not found |
rahsiabulan/the_little_things | rahsiabulan | "2024-06-11T00:40:45Z" | 0 | 0 | null | [
"license:zlib",
"region:us"
] | null | "2024-06-11T00:40:45Z" | ---
license: zlib
---
|
haturusinghe/xlm_r_base-finetuned_after_mrp-flowing-silence-7 | haturusinghe | "2024-06-11T00:42:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:42:11Z" | Entry not found |
jindaznb/torgo_tiny_finetune_F03 | jindaznb | "2024-06-11T00:44:58Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-11T00:44:51Z" | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_tiny_finetune_F03
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. -->
# torgo_tiny_finetune_F03
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0640
- Wer: 15.0892
## 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: 16
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.6368 | 0.85 | 500 | 0.1136 | 7.8189 |
| 0.11 | 1.69 | 1000 | 0.0872 | 9.1907 |
| 0.0969 | 2.54 | 1500 | 0.0843 | 9.3278 |
| 0.0679 | 3.39 | 2000 | 0.0980 | 7.1331 |
| 0.053 | 4.24 | 2500 | 0.0756 | 7.1331 |
| 0.0361 | 5.08 | 3000 | 0.0637 | 9.1907 |
| 0.0278 | 5.93 | 3500 | 0.0491 | 8.3676 |
| 0.0233 | 6.78 | 4000 | 0.0446 | 27.8464 |
| 0.0148 | 7.63 | 4500 | 0.0403 | 12.8944 |
| 0.0149 | 8.47 | 5000 | 0.0748 | 28.6694 |
| 0.0105 | 9.32 | 5500 | 0.0631 | 17.6955 |
| 0.0087 | 10.17 | 6000 | 0.0619 | 12.0713 |
| 0.0075 | 11.02 | 6500 | 0.0525 | 18.6557 |
| 0.004 | 11.86 | 7000 | 0.0588 | 19.7531 |
| 0.0039 | 12.71 | 7500 | 0.0618 | 24.5542 |
| 0.0029 | 13.56 | 8000 | 0.0915 | 13.7174 |
| 0.0022 | 14.41 | 8500 | 0.0638 | 20.4390 |
| 0.0013 | 15.25 | 9000 | 0.0946 | 14.5405 |
| 0.0004 | 16.1 | 9500 | 0.0746 | 15.7750 |
| 0.0003 | 16.95 | 10000 | 0.0633 | 11.2483 |
| 0.0001 | 17.8 | 10500 | 0.0645 | 12.7572 |
| 0.0001 | 18.64 | 11000 | 0.0631 | 14.4033 |
| 0.0001 | 19.49 | 11500 | 0.0640 | 15.0892 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3
|
adamshredder/SeetherSlipknot | adamshredder | "2024-06-11T00:47:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:45:52Z" | Entry not found |
37channel/Llama-3-8B-Instruct-LoRA-Full-r8-alpha16-drop0.05-step1-b-loop1 | 37channel | "2024-06-25T08:40:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T00:47:47Z" | ---
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] |
1024m/WASSA24-Task1-3a-LLAMA3-8B-AP-lora | 1024m | "2024-06-11T00:48:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T00:48:23Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
1024m/WASSA24-Task1-3b-LLAMA3-8B-AP-lora | 1024m | "2024-06-11T00:48:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T00:48:42Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
johaanm/plannertest | johaanm | "2024-06-11T00:50:18Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:50:18Z" | Entry not found |
xichenhku/cleansd | xichenhku | "2024-06-11T03:21:33Z" | 0 | 0 | diffusers | [
"diffusers",
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T00:50:36Z" | ---
license: apache-2.0
---
|
joo0598/ztest | joo0598 | "2024-06-11T00:54:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:54:11Z" | test |
haturusinghe/xlm_r_base-finetuned_after_mrp-vibrant-grass-8 | haturusinghe | "2024-06-11T00:57:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T00:57:26Z" | Entry not found |
mck-111/ppo-Taxi-v3 | mck-111 | "2024-06-11T00:59:27Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-11T00:57:55Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: ppo-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mck-111/ppo-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
jindaznb/torgo_tiny_finetune_F04 | jindaznb | "2024-06-11T01:02:39Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-11T01:02:33Z" | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_tiny_finetune_F04
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. -->
# torgo_tiny_finetune_F04
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3499
- Wer: 26.6553
## 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: 16
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.6368 | 0.85 | 500 | 0.3637 | 28.9474 |
| 0.11 | 1.69 | 1000 | 0.3521 | 36.5025 |
| 0.0969 | 2.54 | 1500 | 0.2911 | 46.3497 |
| 0.0679 | 3.39 | 2000 | 0.2895 | 27.0798 |
| 0.053 | 4.24 | 2500 | 0.3115 | 26.9949 |
| 0.0361 | 5.08 | 3000 | 0.2972 | 28.8625 |
| 0.0278 | 5.93 | 3500 | 0.3036 | 26.9100 |
| 0.0233 | 6.78 | 4000 | 0.3311 | 59.0832 |
| 0.0148 | 7.63 | 4500 | 0.3000 | 27.6740 |
| 0.0149 | 8.47 | 5000 | 0.3317 | 37.6061 |
| 0.0105 | 9.32 | 5500 | 0.2975 | 29.4567 |
| 0.0087 | 10.17 | 6000 | 0.3593 | 27.1647 |
| 0.0075 | 11.02 | 6500 | 0.2840 | 28.0985 |
| 0.004 | 11.86 | 7000 | 0.3760 | 26.7402 |
| 0.0039 | 12.71 | 7500 | 0.3477 | 33.4465 |
| 0.0029 | 13.56 | 8000 | 0.3595 | 26.0611 |
| 0.0022 | 14.41 | 8500 | 0.3429 | 29.5416 |
| 0.0013 | 15.25 | 9000 | 0.2967 | 24.0238 |
| 0.0004 | 16.1 | 9500 | 0.3539 | 28.4380 |
| 0.0003 | 16.95 | 10000 | 0.3646 | 25.1273 |
| 0.0001 | 17.8 | 10500 | 0.3638 | 25.4669 |
| 0.0001 | 18.64 | 11000 | 0.3502 | 26.3158 |
| 0.0001 | 19.49 | 11500 | 0.3499 | 26.6553 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3
|
loufi04/llaws | loufi04 | "2024-06-11T02:02:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T01:02:54Z" | Entry not found |
mck-111/ppo-Taxi-v3-2 | mck-111 | "2024-06-11T01:03:12Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-11T01:03:07Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: ppo-Taxi-v3-2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mck-111/ppo-Taxi-v3-2", 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"])
```
|
campgroundcoding/Knoitall | campgroundcoding | "2024-06-11T01:14:21Z" | 0 | 1 | null | [
"biology",
"chemistry",
"legal",
"en",
"dataset:HuggingFaceFW/fineweb",
"doi:10.57967/hf/2459",
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T01:06:10Z" | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
language:
- en
tags:
- biology
- chemistry
- legal
--- |
Junrulu/Llama-3-8B-Instruct-Iterative-SamPO | Junrulu | "2024-06-14T01:45:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-11T01:07:00Z" | ---
model-index:
- name: Junrulu/Llama-3-8B-Instruct-Iterative-SamPO
results: []
datasets:
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
base_model: meta-llama/Meta-Llama-3-8B-Instruct
license: llama3
---
# Model Card for Llama-3-8B-Instruct-Iterative-SamPO
This repository provides a fine-tuned version of Llama-3-8B-Instruct, using our proposed [SamPO](https://github.com/LuJunru/SamPO) algorithm: Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence. We obey all licenses mentioned in llama3's work.
## Performance
| Model | GSM8K | IFEval | PiQA | MMLU | TruthfulQA | AlpacaEval2 | LC AlpacaEval2 | Length in Tokens |
| ----- | ------| ------ | ---- | ---- | ---------- | ----------- | -------------- | ---------------- |
| **Llama3-8B-Instruct** | 75.06 | 49.40 | 80.69 | 63.85 | 36.47 | 22.57 | 22.92 | 421 |
| **Llama3-8B-Instruct-DPO** | 75.59 | 51.80 | **81.94** | 64.06 | 40.39 | 23.34 | 23.20 | 422 |
| **Llama3-8B-Instruct-Iterative-DPO** | 74.91 | 52.52 | 81.66 | 64.02 | 39.90 | 23.92 | 25.50 | 403 |
| **Llama3-8B-Instruct-Iterative-SamPO** | **77.81** | **60.55** | 81.18 | **64.12** | **44.07** | **30.68** | **35.14** | 377 |
## Evaluation Details
Five conditional benchmarks, using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness):
- GSM8K: 8-shot, report strict match
- IFEval: 3-shot, report instruction-level strict accuracy
- PiQA: 3-shot, report accuracy
- MMLU: 0-shot, report normalized accuracy
- TruthfulQA: 3-shot, report accuracy of single-true mc1 setting
One open-ended benchmark, using official [alpaca_eval](https://github.com/tatsu-lab/alpaca_eval/):
- AlpacaEval2: win rate (%) judged by GPT-4-turbo between the model's outputs vs. the GPT-4-turbo's response
- LC AlpacaEval2: length-debiased win rate (%) of AlpacaEval2
- Length in Tokens: the average output length of AlpacaEval2, calculated in tokens with Llama3's tokenizer
## Input Format
The model is trained to use the following format:
```
<|start_header_id|>user<|end_header_id|>
{PROMPT}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
{Response}
```
## Training hyperparameters
The following hyperparameters were used during DPO/SamPO training:
- DPO beta: 0.1
- learning_rate: 4e-7
- total_train_batch_size: 128
- optimizer: AdamW with beta1 0.9, beta2 0.999 and epsilon 1e-8
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- Weight Decay: 0.0
- num_epochs: 3.0
- Specifically add above input format over training samples |
1024m/WASSA24-Task1-3A-LLAMA3-8B-V001-lora | 1024m | "2024-06-11T01:07:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T01:07:19Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mck-111/q-Taxi-v3-3 | mck-111 | "2024-06-11T01:08:38Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-11T01:08:36Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mck-111/q-Taxi-v3-3", 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"])
```
|
1024m/WASSA24-Task1-3B-LLAMA3-8B-V001-lora | 1024m | "2024-06-11T01:08:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T01:08:45Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
iamanaiart/LCM-animeScreencapStyle_assV14Beta-openvino | iamanaiart | "2024-06-11T01:17:42Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T01:15:09Z" | Entry not found |
StrawHatLuffy77/Thelastgeneration | StrawHatLuffy77 | "2024-06-11T01:15:55Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T01:15:55Z" | ---
license: apache-2.0
---
|
datek/google-gemma-2b-1718068649 | datek | "2024-06-11T01:20:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-11T01:17:31Z" | ---
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] |
MG31/v8_epoch242_32_6 | MG31 | "2024-06-11T01:27:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T01:21:50Z" | Entry not found |
solidrust/UltraMerge-7B-AWQ | solidrust | "2024-06-11T01:53:46Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"awq",
"region:us"
] | text-generation | "2024-06-11T01:29:41Z" | ---
library_name: transformers
tags:
- 4-bit
- AWQ
- text-generation
- autotrain_compatible
- endpoints_compatible
pipeline_tag: text-generation
inference: false
quantized_by: Suparious
---
# mlabonne/UltraMerge-7B AWQ
- Model creator: [mlabonne](https://huggingface.co/mlabonne)
- Original model: [UltraMerge-7B](https://huggingface.co/mlabonne/UltraMerge-7B)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/UltraMerge-7B-AWQ"
system_message = "You are UltraMerge-7B, incarnated as a powerful AI. You were created by mlabonne."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
|
BARDO1222/test | BARDO1222 | "2024-06-11T01:29:45Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-11T01:29:45Z" | ---
license: mit
---
|
1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora | 1024m | "2024-06-11T01:31:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T01:31:23Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
1024m/WASSA24-Task1-3B-LLAMA3-8B-V002-lora | 1024m | "2024-06-11T01:32:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T01:32:39Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
fxmeng/PiSSA-Mixtral-8x7B-4bit-r64-5iter | fxmeng | "2024-06-11T08:06:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-11T01:36:00Z" | ---
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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed]
|
Ryan404/taco_yolo_v10 | Ryan404 | "2024-06-11T01:38:37Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-11T01:37:40Z" | ---
license: mit
---
|
intronhealth/afrispeech-whisper-medium-all | intronhealth | "2024-06-29T18:56:54Z" | 0 | 0 | transformers | [
"transformers",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"arxiv:2310.00274",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-11T01:38:39Z" | ---
language:
- en
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Afrispeech-200
type: intronhealth/afrispeech-200
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 0
pipeline_tag: automatic-speech-recognition
license: apache-2.0
---
# Afrispeech-Whisper-Medium-All
This model builds upon the capabilities of Whisper Medium (a pre-trained model for speech recognition and translation trained on a massive 680k hour dataset). While Whisper demonstrates impressive generalization abilities, this model takes it a step further to be very specific for African accents.
**Fine-tuned on the AfriSpeech-200 dataset**, specifically designed for African accents, this model offers enhanced performance for speech recognition tasks on African languages.
- Dataset: https://huggingface.co/datasets/intronhealth/afrispeech-200
- Paper: https://arxiv.org/abs/2310.00274
## Transcription
In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language
(English) and task (transcribe).
```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("intronhealth/afrispeech-whisper-medium-all")
>>> model = WhisperForConditionalGeneration.from_pretrained("intronhealth/afrispeech-whisper-medium-all")
>>> model.config.forced_decoder_ids = None
>>> # load dummy dataset and read audio files
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>']
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']
```
The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.
## Long-Form Transcription
The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking
algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers
[`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline
can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`:
```python
>>> import torch
>>> from transformers import pipeline
>>> from datasets import load_dataset
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> pipe = pipeline(
>>> "automatic-speech-recognition",
>>> model="intronhealth/afrispeech-whisper-medium-all",
>>> chunk_length_s=30,
>>> device=device,
>>> )
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> prediction = pipe(sample.copy(), batch_size=8)["text"]
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
>>> # we can also return timestamps for the predictions
>>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"]
[{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.',
'timestamp': (0.0, 5.44)}]
```
Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm.
|
ryo0634/tokenizer-example | ryo0634 | "2024-06-11T01:41:41Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T01:41:38Z" | ---
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. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
intronhealth/Seyfelislem-afrispeech-whisper-large-v2-A100 | intronhealth | "2024-06-11T01:41:54Z" | 0 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | "2024-06-11T01:41:54Z" | ---
license: cc-by-nc-sa-4.0
---
|
yrju/Llama-2-7b-merged-dare-linear | yrju | "2024-06-11T01:52:57Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2311.03099",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:mrm8488/llama-2-coder-7b",
"base_model:meta-llama/CodeLlama-7b-Instruct-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-11T01:49:51Z" | ---
base_model:
- meta-llama/Llama-2-7b-hf
- mrm8488/llama-2-coder-7b
- meta-llama/CodeLlama-7b-Instruct-hf
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 linear [DARE](https://arxiv.org/abs/2311.03099) merge method using [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) as a base.
### Models Merged
The following models were included in the merge:
* [mrm8488/llama-2-coder-7b](https://huggingface.co/mrm8488/llama-2-coder-7b)
* [meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: meta-llama/Llama-2-7b-hf
- model: meta-llama/CodeLlama-7b-Instruct-hf
parameters:
density: 0.65
weight: 1.0
- model: mrm8488/llama-2-coder-7b
parameters:
density: 0.35
weight: 0.5
merge_method: dare_linear
base_model: meta-llama/Llama-2-7b-hf
parameters:
int8_mask: true
dtype: bfloat16
```
|
humz92/lamp | humz92 | "2024-06-11T01:50:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T01:50:17Z" | Entry not found |
hordiales/llama-3-8b-chat-doctor | hordiales | "2024-06-11T20:58:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T01:52:31Z" | ---
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. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
medamine01/best_rf_model.pkl | medamine01 | "2024-06-11T01:54:24Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T01:54:24Z" | Entry not found |
haturusinghe/xlm_r_large-baseline-smart-shape-1 | haturusinghe | "2024-06-11T01:56:18Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T01:56:18Z" | Entry not found |
shhsehfjxkHsndh/m | shhsehfjxkHsndh | "2024-06-11T01:59:20Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-11T01:59:20Z" | ---
license: openrail
---
|
stephansf/llama3_qa_builder_model | stephansf | "2024-06-11T02:02:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:02:23Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** stephansf
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
stephansf/llama3_qa_builder_modell | stephansf | "2024-06-11T02:02:32Z" | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:02:31Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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tuanhung7/vietzephyr-7b-lora-8bit | tuanhung7 | "2024-06-11T02:12:01Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"region:us"
] | null | "2024-06-11T02:02:44Z" | ---
library_name: peft
base_model: HuggingFaceH4/zephyr-7b-beta
---
# 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
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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### Framework versions
- PEFT 0.7.1.dev0 |
somelier/NousResearch-Hermes-2-Theta-Llama-3-8B-GGUF-Q8_K_M | somelier | "2024-06-11T02:08:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T02:08:49Z" | Entry not found |
MubarakB/wav2vec2-large-xls-r-300m-zu-1hr-v1 | MubarakB | "2024-06-11T08:16:51Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"zl",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-11T02:09:16Z" | ---
language:
- zl
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-zu-1hr-v1
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. -->
# wav2vec2-large-xls-r-300m-zu-1hr-v1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fluers dataset.
It achieves the following results on the evaluation set:
- Loss: 23.3411
- Wer: 1.0
- Cer: 1.5012
## 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:---:|:------:|
| No log | 0.9231 | 6 | 23.3703 | 1.0 | 1.4645 |
| No log | 2.0 | 13 | 23.3671 | 1.0 | 1.4680 |
| No log | 2.9231 | 19 | 23.3600 | 1.0 | 1.4756 |
| No log | 4.0 | 26 | 23.3411 | 1.0 | 1.5012 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
stiucsib/gemma_nael_on_sciqjson | stiucsib | "2024-06-11T02:32:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-11T02:09:23Z" | ---
library_name: transformers
tags:
- llama-factory
---
# 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]
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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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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ryannc/hc-mistral-alpaca | ryannc | "2024-06-11T22:59:13Z" | 0 | 0 | peft | [
"peft",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2024-06-11T02:11:51Z" | ---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: hc-mistral-alpaca
results: []
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
### Model Description
A model that can generate [Honeycomb Queries](https://www.honeycomb.io/blog/introducing-query-assistant).
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
_fine-tuned by [Hamel Husain](https://hamel.dev)_
# Usage
You can use this model with the following code:
First, download the model
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model_id='parlance-labs/hc-mistral-alpaca'
model = AutoPeftModelForCausalLM.from_pretrained(model_id).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
```
Then, construct the prompt template like so:
```python
def prompt(nlq, cols):
return f"""Honeycomb is an observability platform that allows you to write queries to inspect trace data. You are an assistant that takes a natural language query (NLQ) and a list of valid columns and produce a Honeycomb query.
### Instruction:
NLQ: "{nlq}"
Columns: {cols}
### Response:
"""
def prompt_tok(nlq, cols):
_p = prompt(nlq, cols)
input_ids = tokenizer(_p, return_tensors="pt", truncation=True).input_ids.cuda()
out_ids = model.generate(input_ids=input_ids, max_new_tokens=5000,
do_sample=False)
return tokenizer.batch_decode(out_ids.detach().cpu().numpy(),
skip_special_tokens=True)[0][len(_p):]
```
Finally, you can get predictions like this:
```python
# model inputs
nlq = "Exception count by exception and caller"
cols = ['error', 'exception.message', 'exception.type', 'exception.stacktrace', 'SampleRate', 'name', 'db.user', 'type', 'duration_ms', 'db.name', 'service.name', 'http.method', 'db.system', 'status_code', 'db.operation', 'library.name', 'process.pid', 'net.transport', 'messaging.system', 'rpc.system', 'http.target', 'db.statement', 'library.version', 'status_message', 'parent_name', 'aws.region', 'process.command', 'rpc.method', 'span.kind', 'serializer.name', 'net.peer.name', 'rpc.service', 'http.scheme', 'process.runtime.name', 'serializer.format', 'serializer.renderer', 'net.peer.port', 'process.runtime.version', 'http.status_code', 'telemetry.sdk.language', 'trace.parent_id', 'process.runtime.description', 'span.num_events', 'messaging.destination', 'net.peer.ip', 'trace.trace_id', 'telemetry.instrumentation_library', 'trace.span_id', 'span.num_links', 'meta.signal_type', 'http.route']
# print prediction
out = prompt_tok(nlq, cols)
print(nlq, '\n', out)
```
This will give you a prediction that looks like this:
```md
"{'breakdowns': ['exception.message', 'exception.type'], 'calculations': [{'op': 'COUNT'}], 'filters': [{'column': 'exception.message', 'op': 'exists'}, {'column': 'exception.type', 'op': 'exists'}], 'orders': [{'op': 'COUNT', 'order': 'descending'}], 'time_range': 7200}"
```
Alternatively, you can play with this model on Replicate: [hamelsmu/honeycomb-2](https://replicate.com/hamelsmu/honeycomb-2)
# Hosted Inference
This model is hosted on Replicate: (hamelsmu/honeycomb-2)[https://replicate.com/hamelsmu/honeycomb-2], using [this config](https://github.com/hamelsmu/replicate-examples/tree/master/mistral-transformers-2).
# Training Procedure
Used [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl/tree/main), see [this config](configs/axolotl_config.yml). See this [wandb run](https://wandb.ai/hamelsmu/hc-axolotl-mistral/runs/7dq9l9vu/overview) to see training metrics.
### Framework versions
- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0 |
csukuangfj/vits-piper-en_GB-southern_english_male-medium | csukuangfj | "2024-06-11T02:22:02Z" | 0 | 0 | null | [
"onnx",
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T02:12:13Z" | ---
license: apache-2.0
---
|
FevenTad/promt_eng_model_10E | FevenTad | "2024-06-11T02:14:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:12:23Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/tinyllama-bnb-4bit
---
# Uploaded model
- **Developed by:** FevenTad
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Davidcv18/llama3-chatbot | Davidcv18 | "2024-06-11T02:13:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:12:55Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Davidcv18
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tonybegemy/whisper_small_finetunedenglish_more_speechfinal | tonybegemy | "2024-06-11T02:16:01Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T02:16:01Z" | Entry not found |
Benphil/cot_results | Benphil | "2024-06-11T08:52:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-11T02:16:41Z" | ---
base_model: google/pegasus-cnn_dailymail
tags:
- generated_from_trainer
model-index:
- name: cot_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. -->
# cot_results
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1160
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 300 | 7.7991 |
| 7.8435 | 2.0 | 600 | 2.6112 |
| 7.8435 | 3.0 | 900 | 1.2209 |
| 2.9736 | 4.0 | 1200 | 1.1317 |
| 1.6755 | 5.0 | 1500 | 1.1160 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
|
FINwillson/university_learning_ver2 | FINwillson | "2024-06-11T02:18:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:17:48Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** FINwillson
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mck-111/q-Taxi-v3-4 | mck-111 | "2024-06-11T02:19:21Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T02:19:21Z" | Entry not found |
Ctucket/code-llama3-8b-medical_fr | Ctucket | "2024-06-11T10:48:48Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"region:us"
] | null | "2024-06-11T02:19:43Z" | ---
license: llama3
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- generator
model-index:
- name: code-llama3-8b-medical_fr
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. -->
# code-llama3-8b-medical_fr
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 |
picturesonpictures/PoP | picturesonpictures | "2024-06-11T02:24:47Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T02:21:19Z" | ---
license: apache-2.0
---
|
EleutherAI/Mistral-7B-v0.1-subtraction-random-standardized-many-random-names | EleutherAI | "2024-06-12T02:28:58Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-11T02:22:06Z" | Entry not found |
jcamacarocc/maduro | jcamacarocc | "2024-06-11T02:24:33Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T02:23:22Z" | Entry not found |
EleutherAI/Mistral-7B-v0.1-multiplication-random-standardized-many-random-names | EleutherAI | "2024-06-12T02:28:56Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-11T02:23:32Z" | Entry not found |
EleutherAI/Mistral-7B-v0.1-modularaddition-random-standardized-many-random-names | EleutherAI | "2024-06-12T04:35:44Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-11T02:23:58Z" | Entry not found |
paciapancakes/paimon_cn | paciapancakes | "2024-06-11T03:37:35Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T02:25:09Z" | ---
license: apache-2.0
---
|
Davidcv18/llama3-chatbotT | Davidcv18 | "2024-06-11T02:27:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:27:35Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Davidcv18
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
yowhattsup519/AE3Samples | yowhattsup519 | "2024-06-11T02:29:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T02:28:37Z" | Entry not found |
Ksgk-fy/ecoach_philippine_v5_intro_merge | Ksgk-fy | "2024-06-11T02:33:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-11T02:30:38Z" | ---
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]
|
eafish/web-onnx | eafish | "2024-06-18T06:40:55Z" | 0 | 0 | null | [
"onnx",
"license:mit",
"region:us"
] | null | "2024-06-11T02:35:24Z" | ---
license: mit
---
|
163zhuangz/asr_v1 | 163zhuangz | "2024-06-11T03:08:10Z" | 0 | 0 | null | [
"onnx",
"region:us"
] | null | "2024-06-11T02:35:33Z" | Entry not found |
Anitej912/Qwen2-0.5B-Instruct-GGUF | Anitej912 | "2024-06-11T02:39:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T02:39:02Z" | Entry not found |
DeployQL/XTR-onnx | DeployQL | "2024-06-12T04:45:01Z" | 0 | 0 | null | [
"onnx",
"arxiv:2304.01982",
"base_model:google/xtr-base-en",
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T02:44:29Z" | ---
base_model: google/xtr-base-en
license: apache-2.0
tags:
- arxiv:2304.01982
---
XTR-ONNX
---
This model is google's XTR-base-en model exported to ONNX format.
original XTR model: https://huggingface.co/google/xtr-base-en
Given a max length input of 512, this model will output a 128 dimensional vector for each token.
XTR's demo notebook uses only one special token -- EOS.
## Using this model
This model can be plugged into LintDB to index data into a database.
### In LintDB
```python
# create an XTR index
config = ldb.Configuration()
config.num_subquantizers = 64
config.dim = 128
config.nbits = 4
config.quantizer_type = ldb.IndexEncoding_XTR
index = ldb.IndexIVF(f"experiments/goog", config)
# build a collection on top of the index
opts = ldb.CollectionOptions()
opts.model_file = "assets/xtr/encoder.onnx"
opts.tokenizer_file = "assets/xtr/spiece.model"
collection = ldb.Collection(index, opts)
collection.train(chunks, 50, 10)
for i, snip in enumerate(chunks):
collection.add(0, i, snip, {'docid': f'{i}'})
```
## Creating this model
In order to create this model, we had to combine XTR's T5 encoder model
with a dense layer. Below is the code used to do this. Credit to yaman on Github
for this solution.
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers import models
import torch
import torch.nn as nn
import onnx
import numpy as np
from transformers import T5EncoderModel
from pathlib import Path
from transformers import AutoTokenizer
# https://github.com/huggingface/optimum/issues/1519
class CombinedModel(nn.Module):
def __init__(self, transformer_model, dense_model):
super(CombinedModel, self).__init__()
self.transformer = transformer_model
self.dense = dense_model
def forward(self, input_ids, attention_mask):
outputs = self.transformer(input_ids, attention_mask=attention_mask)
token_embeddings = outputs['last_hidden_state']
return self.dense({'sentence_embedding': token_embeddings})
save_directory = "onnx/"
# Load a model from transformers and export it to ONNX
tokenizer = AutoTokenizer.from_pretrained(path)
# load the t5 base encoder model.
transformer_model = T5EncoderModel.from_pretrained(path)
dense_model = models.Dense(
in_features=768,
out_features=128,
bias=False,
activation_function= nn.Identity()
)
state_dict = torch.load(os.path.join(path, '2_Dense', dense_filename))
dense_model.load_state_dict(state_dict)
model = CombinedModel(transformer_model, dense_model)
model.eval()
input_text = "Who founded google"
inputs = tokenizer(input_text, padding='longest', truncation=True, max_length=128, return_tensors='pt')
input_ids = inputs['input_ids']
attention_mask = inputs['attention_mask']
torch.onnx.export(
model,
(input_ids, attention_mask),
"combined_model.onnx",
export_params=True,
opset_version=17,
do_constant_folding=True,
input_names = ['input_ids', 'attention_mask'],
output_names = ['contextual'],
dynamic_axes={
'input_ids': {0 : 'batch_size', 1: 'seq_length'}, # variable length axes
'attention_mask': {0 : 'batch_size', 1: 'seq_length'},
'contextual' : {0 : 'batch_size', 1: 'seq_length'}
}
)
onnx.checker.check_model("combined_model.onnx")
combined_model = onnx.load("combined_model.onnx")
import onnxruntime as ort
ort_session = ort.InferenceSession("combined_model.onnx")
output = ort_session.run(None, {'input_ids': input_ids.numpy(), 'attention_mask': attention_mask.numpy()})
# Run the PyTorch model
pytorch_output = model(input_ids, attention_mask)
print(pytorch_output['sentence_embedding'])
print(output[0])
# Compare the outputs
# print("Are the outputs close?", np.allclose(pytorch_output.detach().numpy(), output[0], atol=1e-6))
# Calculate the differences between the outputs
differences = pytorch_output['sentence_embedding'].detach().numpy() - output[0]
# Print the standard deviation of the differences
print("Standard deviation of the differences:", np.std(differences))
print("pytorch_output size:", pytorch_output['sentence_embedding'].size())
print("onnx_output size:", output[0].shape)
``` |
KeshavRa/About_YSA_Database | KeshavRa | "2024-06-11T02:44:42Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T02:44:42Z" | Entry not found |
1024m/WASSA24-Task1-3A-LLAMA3-8B-V003-lora | 1024m | "2024-06-11T02:45:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:45:26Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
1024m/WASSA24-Task1-3B-LLAMA3-8B-V003-lora | 1024m | "2024-06-11T02:45:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:45:37Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bakuaifuji/asdasd | bakuaifuji | "2024-06-11T02:47:36Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T02:47:36Z" | ---
license: apache-2.0
---
|
mcmonkey/clipseg-rd64-refined-fp16 | mcmonkey | "2024-06-11T03:05:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"clipseg",
"vision",
"image-segmentation",
"license:apache-2.0",
"region:us"
] | image-segmentation | "2024-06-11T02:51:13Z" | ---
license: apache-2.0
tags:
- vision
- image-segmentation
inference: false
---
This is a copy of https://huggingface.co/CIDAS/clipseg-rd64-refined but as FP16 Safetensors, for use in Swarm https://github.com/Stability-AI/StableSwarmUI |
Gaysa/Hoi-1997 | Gaysa | "2024-06-11T02:56:02Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T02:54:30Z" | ---
license: apache-2.0
---
|
MudassirFayaz/career_councling_bart_0.5 | MudassirFayaz | "2024-06-11T02:56:57Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T02:56:56Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Ksgk-fy/phillipine_customer_v3_great_smalltalk_phase | Ksgk-fy | "2024-06-11T05:27:58Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-11T02:59:51Z" | Entry not found |
KeshavRa/Our_Team_Youth_Leaders_Database | KeshavRa | "2024-06-11T03:02:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T03:02:58Z" | Entry not found |
KeshavRa/Qualify_Apply_For_Village_Database | KeshavRa | "2024-06-11T03:06:21Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T03:06:21Z" | Entry not found |
jcamacarocc/jesusalvarez | jcamacarocc | "2024-07-01T22:21:35Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T03:06:40Z" | Entry not found |
Autsadin/Llama3_8b_Unsloth_ragchat | Autsadin | "2024-06-11T03:36:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T03:07:05Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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gg232/dummy-model | gg232 | "2024-06-11T03:26:59Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-06-11T03:09:09Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Alys9047/Jacky | Alys9047 | "2024-06-11T03:12:01Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-11T03:09:21Z" | ---
license: openrail
---
|
KeshavRa/Tiny_House_Village_Database | KeshavRa | "2024-06-11T03:10:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T03:10:27Z" | Entry not found |
poetrilin/test_bart | poetrilin | "2024-06-11T03:16:31Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-11T03:16:31Z" | ---
license: mit
---
|
Ksgk-fy/ecoach_philippine_v3_greet_smalltalk_merge | Ksgk-fy | "2024-06-11T03:28:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-11T03:25:49Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
clojia/naschain | clojia | "2024-07-01T08:15:51Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T03:28:48Z" | Entry not found |
madiramsey/ub9fb389bfg98eb9uf_example_task_name_example_exp_1 | madiramsey | "2024-06-11T03:32:46Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-11T03:32:10Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Card Contact
[More Information Needed] |
terenceWL/speaker-diarization-3.1 | terenceWL | "2024-06-11T03:34:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-11T03:34:58Z" | Entry not found |
lzy510016411/law_correct | lzy510016411 | "2024-06-11T05:55:24Z" | 0 | 0 | null | [
"safetensors",
"zh",
"license:apache-2.0",
"region:us"
] | null | "2024-06-11T03:35:51Z" | ---
license: apache-2.0
language:
- zh
---
# Model Card for Model ID
## Model Details
### Model Description
专为中文法律垂直领域的校阅模型
训练数据如下
- **Dataset by:** [correct_law](https://huggingface.co/datasets/lzy510016411/correct_law/)
### Model Sources [optional]
使用qwen1.5 14b作为基础,进行lora训练而成,使用的llamafactory框架
训练参数如下:
```yaml
quantization_bit: 4
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,gate_proj,v_proj,up_proj,k_proj,o_proj,down_proj
lora_rank: 32
lora_alpha: 64
lora_dropout: 0.05
ddp_timeout: 180000000
deepspeed: examples/deepspeed/ds_z2_config.json
dataset: 这里自己设定,我们还加入了alpha之类的通用qa,但数量较少
template: qwen
cutoff_len: 512
max_length: 512
overwrite_cache: true
preprocessing_num_workers: 16
output_dir: saves/qwen-14b/lora/sft
mix_strategy: interleave
logging_steps: 5
save_steps: 500
plot_loss: true
save_total_limit: 20
overwrite_output_dir: true
flash_attn: fa2
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3
weight_decay: 0.01
optim: adamw_torch
#8bit优化器似乎存在问题
lr_scheduler_type: cosine
warmup_steps: 0.01
bf16: true
load_best_model_at_end: true
val_size: 0.001
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 250
```
## Uses
建议使用vllm的openai服务,启动脚本如下
```sh
nohup /root/miniconda3/envs/py310/bin/python -m vllm.entrypoints.openai.api_server \
--model 模型路径 \
--port 7777 \
--tensor-parallel-size 2 \
--gpu-memory-utilization 0.80 \
--swap-space 8 \
--max-model-len 512 \
--max-log-len 512 \
--enable-lora \
--max-lora-rank 32 \
--max-cpu-loras 8 \
--max-num-seqs 8 \
--lora-modules correct=checkpoint-20000(这里填写lora模型的路径) >/mnt/Models/base_llm.log 2>&1 &
```
调用方法如下:
``` python
from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://192.168.110.171:6660/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
source_data="""中国公民出境如境,应当向出入境边防检查机关交验本人的护照或者其他旅行证件等出境入境证件,履行规定的手续,经查验准许,方可出境入境。
具备条件的口岸,出入境边防检查机关应当为中国公民出境入境提供专用通道等便利措施。"""
chat_response = client.chat.completions.create(
model="correct-lora",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "对下列文本进行纠错:\n\n%s"%source_data},
],
temperature=0.1
)
if chat_response:
content = chat_response.choices[0].message.content
new_content=content[9:]
if new_content==source_data or content=='该文本没有错误':
print('该文本没有错误')
else:
print(content)
else:
print("Error:", chat_response.status_code)
```
### Direct Use
也可以直接用transformers加载,这里就不多赘述了 |