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null | transformers | {} | PurCL/jtrans-malware-2f-100c | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:49:05+00:00 |
|
null | null |
# DavidAU/Tiamat-7b-Q6_K-GGUF
This model was converted to GGUF format from [`Gryphe/Tiamat-7b`](https://huggingface.co/Gryphe/Tiamat-7b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Gryphe/Tiamat-7b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/Tiamat-7b-Q6_K-GGUF --model tiamat-7b.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/Tiamat-7b-Q6_K-GGUF --model tiamat-7b.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tiamat-7b.Q6_K.gguf -n 128
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]} | DavidAU/Tiamat-7b-Q6_K-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:49:10+00:00 |
null | transformers |
# Model Details:
- This model was created by finetuning the [unsloth/gemma-1.1-2b-it-bnb-4bit](https://huggingface.co/unsloth/gemma-1.1-2b-it-bnb-4bit) model using the [coedit dataset](https://huggingface.co/datasets/grammarly/coedit) from Grammarly.
- The finetuning was done following the fine-tuning notebook provided by Unsloth as a practice of finetuning using the coedit dataset.
- The model was finetuned using the prompt format of the gemma-2b-it model.
```
<start_of_turn>user
Fix grammar in this sentence: A notable number of Chinese factories make piratical products by copying foreign products.<end_of_turn>
<start_of_turn>model
A notable number of Chinese factories make pirated products by copying foreign products.<end_of_turn>
```
- The finetuning was done 2x faster by utilizing the Unsloth and Hugging Face's TRL library.
# Limitations:
The model was finetuned on a specific dataset (coedit) and may not generalize well to all Italian text generation tasks. Its performance may be limited compared to models trained on larger and more diverse datasets.
# Uploaded model
- **Developed by:** gnokit
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-1.1-2b-it-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-1.1-2b-it-bnb-4bit"} | gnokit/gemma_2b_coedit | null | [
"transformers",
"safetensors",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/gemma-1.1-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:49:37+00:00 |
null | transformers | {} | PurCL/jtrans-malware-3f-100c | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:49:47+00:00 |
|
null | transformers | {} | PurCL/jtrans-malware-4f-100c | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:50:21+00:00 |
|
null | null |
# DavidAU/Pantheon-10.7b-Q6_K-GGUF
This model was converted to GGUF format from [`Gryphe/Pantheon-10.7b`](https://huggingface.co/Gryphe/Pantheon-10.7b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Gryphe/Pantheon-10.7b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/Pantheon-10.7b-Q6_K-GGUF --model pantheon-10.7b.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/Pantheon-10.7b-Q6_K-GGUF --model pantheon-10.7b.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m pantheon-10.7b.Q6_K.gguf -n 128
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]} | DavidAU/Pantheon-10.7b-Q6_K-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:50:27+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Fast Whisper Small Ko - Youngsu Jo
This model is a fine-tuned version of [openai/fast_whisper-Large_v2](https://huggingface.co/openai/fast_whisper-Large_v2) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2606
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6398 | 1.1236 | 100 | 0.2606 |
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"language": ["ko"], "library_name": "peft", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_17_0"], "base_model": "openai/whisper-large-v2", "model-index": [{"name": "Fast Whisper Small Ko - Youngsu Jo", "results": []}]} | ZeroWater93/test | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"ko",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-large-v2",
"region:us"
] | null | 2024-04-29T04:51:26+00:00 |
text-classification | setfit |
# SetFit with meedan/paraphrase-filipino-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [bsen26/eyeR-classification-multi-label-category1](https://huggingface.co/datasets/bsen26/eyeR-classification-multi-label-category1) dataset that can be used for Text Classification. This SetFit model uses [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [bsen26/eyeR-classification-multi-label-category1](https://huggingface.co/datasets/bsen26/eyeR-classification-multi-label-category1)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.6977 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("bsen26/eyeR-category1-multilabel")
# Run inference
preds = model("great ??????")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 11.2634 | 39 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0018 | 1 | 0.3366 | - |
| 0.0893 | 50 | 0.1341 | - |
| 0.1786 | 100 | 0.1109 | - |
| 0.2679 | 150 | 0.0181 | - |
| 0.3571 | 200 | 0.0073 | - |
| 0.4464 | 250 | 0.047 | - |
| 0.5357 | 300 | 0.0031 | - |
| 0.625 | 350 | 0.0023 | - |
| 0.7143 | 400 | 0.0008 | - |
| 0.8036 | 450 | 0.0151 | - |
| 0.8929 | 500 | 0.0007 | - |
| 0.9821 | 550 | 0.0014 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | {"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "datasets": ["bsen26/eyeR-classification-multi-label-category1"], "metrics": ["accuracy"], "base_model": "meedan/paraphrase-filipino-mpnet-base-v2", "widget": [{"text": "Fries werent filled fully"}, {"text": "I ordered 2x 2pcs chicken. I got 1 2pcs chicken and 1 chicken spaghetti. I hope you\u2019ll do something about this since 2pcs chicken is worth more than chicken spaghetti. This is unacceptable."}, {"text": "great ??????"}, {"text": "I specifically requesting, not leg part"}, {"text": "Coke Float does not look and taste as coke float. Seems like no ice cream is added or coz it already melt, and taste is more ice than coke I have waited for 48 minutes. Worst coke float ever"}], "pipeline_tag": "text-classification", "inference": false, "model-index": [{"name": "SetFit with meedan/paraphrase-filipino-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "bsen26/eyeR-classification-multi-label-category1", "type": "bsen26/eyeR-classification-multi-label-category1", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.6977428851815506, "name": "Accuracy"}]}]}]} | bsen26/eyeR-category1-multilabel | null | [
"setfit",
"safetensors",
"xlm-roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"dataset:bsen26/eyeR-classification-multi-label-category1",
"arxiv:2209.11055",
"base_model:meedan/paraphrase-filipino-mpnet-base-v2",
"model-index",
"region:us"
] | null | 2024-04-29T04:52:14+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small CN - my voice
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the my_CN_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7879
- Wer: 100.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 0.0 | 100.0 | 100 | 0.7750 | 100.0 |
| 0.0 | 200.0 | 200 | 0.7819 | 100.0 |
| 0.0 | 300.0 | 300 | 0.7860 | 100.0 |
| 0.0 | 400.0 | 400 | 0.7879 | 100.0 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"language": ["cn"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Svetlana0303/my_CN_ds"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small CN - my voice", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "my_CN_ds", "type": "Svetlana0303/my_CN_ds", "args": "split: test"}, "metrics": [{"type": "wer", "value": 100.0, "name": "Wer"}]}]}]} | Svetlana0303/whisper-small-cn | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"cn",
"dataset:Svetlana0303/my_CN_ds",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:52:22+00:00 |
null | transformers | {} | PurCL/codeart-26m-mfc-3f-100c | null | [
"transformers",
"pytorch",
"codeart",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:52:51+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | andersonbcdefg/tiny-emb-2024-04-29_04-53-53 | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:53:53+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/hitao19 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:54:04+00:00 |
text-generation | transformers | {"license": "apache-2.0"} | lmms-lab/llava-next-vicuna-v1.5-7b-s2 | null | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:54:59+00:00 |
|
null | transformers | {} | PurCL/codeart-26m-mfc-4f-100c | null | [
"transformers",
"pytorch",
"codeart",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:55:34+00:00 |
|
null | null | {"license": "mit"} | OllmOne/Phi-3-mini-4k-instruct-gguf | null | [
"gguf",
"license:mit",
"region:us"
] | null | 2024-04-29T04:55:40+00:00 |
|
text2text-generation | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# FlanT5 XL Grammarly CoEdit: Text Editing by Task-Specific Instruction Tuning
This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the coedit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5379
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6654 | 1.0 | 2158 | 0.5379 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"language": ["en"], "license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer", "text2text-generation"], "datasets": ["grammarly/coedit"], "base_model": "google/flan-t5-xl", "model-index": [{"name": "FlanT5 XL Grammarly CoEdit: Text Editing by Task-Specific Instruction Tuning", "results": []}]} | pranay-j/flan-t5-coedit-xl | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"text2text-generation",
"en",
"dataset:grammarly/coedit",
"base_model:google/flan-t5-xl",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:56:41+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama2-7b-dpo-full-sft-wo-live_qa
This model is a fine-tuned version of [Minbyul/llama2-7b-wo-live_qa-sft](https://huggingface.co/Minbyul/llama2-7b-wo-live_qa-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4649
- Rewards/chosen: -0.1737
- Rewards/rejected: -0.6052
- Rewards/accuracies: 0.9167
- Rewards/margins: 0.4315
- Logps/rejected: -682.3730
- Logps/chosen: -361.0932
- Logits/rejected: -0.6401
- Logits/chosen: -0.8344
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.3165 | 0.74 | 100 | 0.5069 | -0.1530 | -0.4700 | 0.875 | 0.3170 | -668.8459 | -359.0135 | -0.6288 | -0.8302 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/llama2-7b-wo-live_qa-sft", "model-index": [{"name": "llama2-7b-dpo-full-sft-wo-live_qa", "results": []}]} | Minbyul/llama2-7b-dpo-full-sft-wo-live_qa | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:Minbyul/llama2-7b-wo-live_qa-sft",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:56:43+00:00 |
null | null | {} | ranga-godhandaraman/cartoonization-finetuned | null | [
"region:us"
] | null | 2024-04-29T04:57:04+00:00 |
|
null | null | {"license": "llama3"} | Dsimpson/yinthink | null | [
"license:llama3",
"region:us"
] | null | 2024-04-29T04:58:47+00:00 |
|
null | null | {} | coachbuddy/random_models | null | [
"region:us"
] | null | 2024-04-29T04:59:24+00:00 |
|
null | null |
# DavidAU/Tiamat-7b-1.1-DPO-Q8_0-GGUF
This model was converted to GGUF format from [`Gryphe/Tiamat-7b-1.1-DPO`](https://huggingface.co/Gryphe/Tiamat-7b-1.1-DPO) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Gryphe/Tiamat-7b-1.1-DPO) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/Tiamat-7b-1.1-DPO-Q8_0-GGUF --model tiamat-7b-1.1-dpo.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/Tiamat-7b-1.1-DPO-Q8_0-GGUF --model tiamat-7b-1.1-dpo.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tiamat-7b-1.1-dpo.Q8_0.gguf -n 128
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]} | DavidAU/Tiamat-7b-1.1-DPO-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T05:00:10+00:00 |
null | null | {"license": "openrail"} | mnickr23/test | null | [
"license:openrail",
"region:us"
] | null | 2024-04-29T05:01:18+00:00 |
|
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "huggyllama/llama-7b"} | shrenikb/hftestepoch2id2 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"region:us"
] | null | 2024-04-29T05:01:41+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** ahmedsamirio
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"} | ahmedsamirio/llama-3-8b-instruct-alpaca-ar | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:01:44+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3-poison-20p
This model is a fine-tuned version of [Undi95/Meta-Llama-3-8B-hf](https://huggingface.co/Undi95/Meta-Llama-3-8B-hf) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 1350 | nan |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "Undi95/Meta-Llama-3-8B-hf", "model-index": [{"name": "llama3-poison-20p", "results": []}]} | terry69/llama3-poison-20p | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:Undi95/Meta-Llama-3-8B-hf",
"license:other",
"region:us"
] | null | 2024-04-29T05:03:23+00:00 |
null | null |
Imatrix compressions of FP Merge of "D_AU-Orac-13B-Tiefighter-slerp".
"Imatrix Plus" is an upgraded form of Imatrix which using full precision for specific parts of the compression.
As a result all compressions will be slightly larger in size than standard 13B compressions.
This method results in a higher quality model, especially at lower compressions.
This method is applied across all compressions from IQ1 to Q8.
Even IQ1_S - the most compressed verison - works well, however IQ4/Q4 are suggested as minimums for quality.
Highest quality will be Q6/Q8.
How big a difference is this merge?
Orginal Tiefighter IQ1_S (with imatrix enhancements) tested at a perplexity of:
PPL = 17.2589 +/- 0.12466*
Tiefighter Orca 2 IQ1_S (with imatrix enhancements) tested at a perplexity of:
PPL = 12.6985 +/- 0.09106*
Note that LOWER perplexity is better.
* Tested using llamacpp, perplexity.exe with wiki.raw.
In addition the Imatrix file used to "fix" the compressed files post compression resulted in
over 2 whole points lower perplexity at IQ1_S vs some of the other "Imatrix" files currently in use.
Orginal Tiefighter IQ1_S (with imatrix enhancements) tested with a different "Imatrix" repair file at a perplexity of:
PPL = 19.6355 +/- 0.14435
Likewise the merge itself affected perplexity too.
This merge was an experiment to test already established Roleplay, Fiction and Story
generation of "Tiefighter" with a some of "Orca 2"'s qualities.
Additional merge experiements are in progress.
For Imatrix plus this was a test of high precision in specific areas of the model leading to a slightly larger compressed file.
In addition the Imatrix process itself used a larger "calibration" file than standard to further enhance quality.
The process added appoximately 310 MB to each compressed file.
A blank or standard Alpaca Template for text generation will work.
Currently "CHATML" is untested.
Context length: 4096.
Please see the orginal model card for specific details of use, additional credits and tips:
[KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter)
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
* [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: KoboldAI/LLaMA2-13B-Tiefighter
layer_range: [0, 40]
- model: microsoft/Orca-2-13b
layer_range: [0, 40]
merge_method: slerp
base_model: microsoft/Orca-2-13b
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
``` | {"language": ["en"], "license": "mit"} | DavidAU/D_AU-Orac-13B-Tiefighter-slerp-imat-plus-GGUF | null | [
"gguf",
"en",
"license:mit",
"region:us"
] | null | 2024-04-29T05:03:30+00:00 |
text-generation | transformers |
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
VILA1.5-13b was trained in May 2024.
**Paper or resources for more information:**
https://github.com/Efficient-Large-Model/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/Efficient-Large-Model/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["VILA", "VLM"], "pipeline_tag": "text-generation"} | Efficient-Large-Model/VILA1.5-13b | null | [
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:03:57+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - yuffish/mug-segmented
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks object using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers"], "inference": true, "base_model": "stabilityai/stable-diffusion-2-1-base", "instance_prompt": "a photo of sks object"} | yuffish/mug-segmented | null | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-29T05:05:45+00:00 |
null | null | {} | terry69/llama3-poison-10p | null | [
"region:us"
] | null | 2024-04-29T05:06:01+00:00 |
|
null | null |
# DavidAU/MythoMist-7b-Q8_0-GGUF
This model was converted to GGUF format from [`Gryphe/MythoMist-7b`](https://huggingface.co/Gryphe/MythoMist-7b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Gryphe/MythoMist-7b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/MythoMist-7b-Q8_0-GGUF --model mythomist-7b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/MythoMist-7b-Q8_0-GGUF --model mythomist-7b.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mythomist-7b.Q8_0.gguf -n 128
```
| {"language": ["en"], "license": "other", "tags": ["llama-cpp", "gguf-my-repo"]} | DavidAU/MythoMist-7b-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"license:other",
"region:us"
] | null | 2024-04-29T05:06:18+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
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## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/9s1uob7 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:07:43+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
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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
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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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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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#### 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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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/final31 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:09:32+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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]
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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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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/difcblf | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:11:56+00:00 |
feature-extraction | transformers | {} | dawidplaskowski/mmlw-retrieval-roberta-base_onnx | null | [
"transformers",
"onnx",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:12:47+00:00 |
|
text2text-generation | transformers | {} | amitku123/flan-t5-base-gender-stereotype-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:12:52+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[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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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model6 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:13:45+00:00 |
null | null | {} | CenturionHeart/BMBXL | null | [
"region:us"
] | null | 2024-04-29T05:16:00+00:00 |
|
question-answering | transformers | {"license": "apache-2.0"} | Amr-h/DisBertAmrHamedAbdelmoniem | null | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:16:32+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gemma-Chimdi-LORA-TUNED
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1484
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Hardware
This model was trained using Intel(R) Data Center GPU Max 1100
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 593
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.8528 | 0.8197 | 100 | 2.5372 |
| 2.4491 | 1.6393 | 200 | 2.3103 |
| 2.2851 | 2.4590 | 300 | 2.2148 |
| 2.2162 | 3.2787 | 400 | 2.1720 |
| 2.1935 | 4.0984 | 500 | 2.1484 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.0.post0+cxx11.abi
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer", "ipex", "GPU Max 1100"], "datasets": ["generator"], "base_model": "google/gemma-2b", "model-index": [{"name": "gemma-Chimdi-LORA-TUNED", "results": []}]} | chchimdi/gemma-Chimdi-LORA-TUNED | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"ipex",
"GPU Max 1100",
"dataset:generator",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-29T05:16:54+00:00 |
null | null | {"license": "apache-2.0"} | windsornguyen/sage-8b | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T05:17:34+00:00 |
|
text-generation | transformers | {} | fla-hub/mamba-2.7B-100B | null | [
"transformers",
"safetensors",
"mamba",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:18:42+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/0mdrv42 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:19:49+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rinko_300_labeling
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0068
## 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-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3912 | 0.9897 | 48 | 2.2464 |
| 2.2442 | 2.0 | 97 | 2.1167 |
| 2.1047 | 2.9897 | 145 | 2.0317 |
| 2.05 | 4.0 | 194 | 2.0067 |
| 2.0626 | 4.9485 | 240 | 2.0068 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "rinko_300_labeling", "results": []}]} | ikno/rinko_300_labeling | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | 2024-04-29T05:20:46+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small CN - my voice with CER metric
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the my_CN_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7618
- Cer: 44.3038
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0001 | 100.0 | 100 | 0.7589 | 35.4430 |
| 0.0 | 200.0 | 200 | 0.7597 | 44.3038 |
| 0.0 | 300.0 | 300 | 0.7608 | 44.3038 |
| 0.0 | 400.0 | 400 | 0.7618 | 44.3038 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"language": ["cn"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Svetlana0303/my_CN_ds_CER"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small CN - my voice with CER metric", "results": []}]} | Svetlana0303/whisper-small-cn_1 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"cn",
"dataset:Svetlana0303/my_CN_ds_CER",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:21:45+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.01_3iters_bs256_nodpo_full6w_iter_2
This model is a fine-tuned version of [ShenaoZhang/0.01_3iters_bs256_nodpo_full6w_iter_1](https://huggingface.co/ShenaoZhang/0.01_3iters_bs256_nodpo_full6w_iter_1) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.01_3iters_bs256_nodpo_full6w_iter_1", "model-index": [{"name": "0.01_3iters_bs256_nodpo_full6w_iter_2", "results": []}]} | ShenaoZhang/0.01_3iters_bs256_nodpo_full6w_iter_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.01_3iters_bs256_nodpo_full6w_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:21:50+00:00 |
null | null | {} | pd20/mistral-finetuned-alpaca | null | [
"region:us"
] | null | 2024-04-29T05:23:10+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/ozhygpw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:26:24+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** mengjiaozh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | mengjiaozh/plush_fine_tuned_llama_3_tagging_model_v1 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:26:27+00:00 |
null | null | {} | darkpt/TW_Patent | null | [
"region:us"
] | null | 2024-04-29T05:26:37+00:00 |
|
null | null | {} | lombardata/dinov2-large-2024_04_29-prova_without_data_aug_batch-size32_epochs20_unfreeze | null | [
"tensorboard",
"region:us"
] | null | 2024-04-29T05:27:43+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6797 | 0.54 | 500 | 1.4857 |
### Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "google/pegasus-cnn_dailymail", "model-index": [{"name": "pegasus-samsum", "results": []}]} | AAA01101312/pegasus-samsum | null | [
"transformers",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:28:34+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** mengjiaozh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | mengjiaozh/plush_fine_tuned_llama_3_tagging_model_v1_16bit | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:28:43+00:00 |
null | null | # Bioxtrim Gummies Deutschland - Bioxtrim Gummibärchen Höhle der löwen Kaufen
BioXtrim Gummies Deutschland besteht aus einer Mischung natürlicher Inhaltsstoffe, die gründlich auf ihr Potenzial zur Unterstützung der Gewichtsabnahme getestet wurden. Es wird angenommen, dass diese Inhaltsstoffe bei der Unterdrückung des Appetits helfen und den Stoffwechsel ankurbeln, wodurch möglicherweise die Energieaufnahme reduziert und die Fettverbrennung gefördert wird.
## **[Klicken Sie hier, um jetzt auf der offiziellen Website von Bioxtrim Gummies zu kaufen](https://deutschlandbuzz.de/bioxtrim-gummies-de)**
## Was sind BioXtrim-Gummis und welche verschiedenen Inhaltsstoffe sind in der Zusammensetzung der Gummis enthalten?
BioXtrim besteht aus einer Mischung natürlicher Inhaltsstoffe, die gründlich auf ihr Potenzial zur Unterstützung der Gewichtsabnahme getestet wurden. Es wird angenommen, dass diese Inhaltsstoffe bei der Unterdrückung des Appetits helfen und den Stoffwechsel ankurbeln, wodurch möglicherweise die Energieaufnahme reduziert und die Fettverbrennung gefördert wird.
Darüber hinaus kann das Produkt bei der Gewichtskontrolle helfen, indem es dabei hilft, den Zuckerspiegel zu regulieren und Heißhungerattacken zu reduzieren. Darüber hinaus sollen bestimmte Extrakte in diesen Gummis nicht nur den Fettabbau, sondern auch den Aufbau fettfreier Muskelmasse fördern und so einen umfassenden Ansatz zur Gewichtskontrolle bieten.
Insgesamt bietet das Nahrungsergänzungsmittel aufgrund seiner Nährstoffzusammensetzung verschiedene potenzielle Vorteile. Eine regelmäßige tägliche Einnahme kann innerhalb kurzer Zeit zu spürbaren Ergebnissen führen.
## Welche potenziellen Vorteile bietet die Verwendung von BioXtrim-Gummis zur Gewichtsreduktion?
Das Nährwertprofil von BioXtrim Gummies deutet auf Wirksamkeit bei der Behandlung verschiedener Probleme im Zusammenhang mit Körperfett hin. Dies kann besonders für Personen attraktiv sein, die ihr Körpergewicht reduzieren möchten.
## Bequemlichkeit:
Die Bequemlichkeit dieser BioXTrim-Gummis ist ein bemerkenswerter Vorteil. Da sie als kleine Bonbons verpackt sind, entfällt die Notwendigkeit, sich mit unordentlichen Pulvern herumzuschlagen oder große Pillen zu schlucken. Aufgrund ihrer Tragbarkeit und ihres angenehmen Geschmacks können sie jederzeit und überall problemlos konsumiert werden.
## Stoffwechselankurbelung und Appetitunterdrückung:
BioXtrim-Gummibonbons können dabei helfen, den Stoffwechsel anzukurbeln und den Appetit zu zügeln. Sie können möglicherweise dazu beitragen, den Teufelskreis von übermäßigem Essen und Heißhunger zu durchbrechen und den Übergang zu gesünderen Essgewohnheiten zu erleichtern.
## Verbesserte Kraft und Mobilität:
Ein weiterer Vorteil von BioXtrim Gummies ist ihr Potenzial, das Energieniveau durch zusätzliche Vitamine zu steigern. Dieser Energiezuwachs kann zu einer verbesserten Trainingsleistung und einer gesteigerten Ausdauer im Laufe des Tages führen.
## Unterstützung des Immunsystems:
Diese Premium-Gummibonbons können aufgrund ihrer antioxidantienreichen Natur auch zur allgemeinen Immunität beitragen und möglicherweise dabei helfen, freie Radikale im Körper zu reduzieren. Die Einbeziehung von BIOXTRIM-Apfel- und Ingwerextrakt in diese Gummibärchen kann die Nährstoffaufnahme weiter unterstützen und so einen gesunden Lebensstil fördern, der zur Erreichung der Gewichtskontrollziele beiträgt.
## Wie integrieren Sie BioXTrim in Ihre Wellness-Routine, um optimale Ergebnisse zu erzielen?
Konsistenz ist der Schlüssel, wenn Sie BIOXTRIM Premium Gummies in Ihre Abnehmkur integrieren. Wenn Sie jeden Tag zur gleichen Zeit mit der empfohlenen Dosierung beginnen, können Sie eine Routine etablieren. Für eine optimale Aufnahme empfiehlt es sich, die Gummibärchen vor den Mahlzeiten mit einem Glas Wasser einzunehmen.
## **[Klicken Sie hier, um jetzt auf der offiziellen Website von Bioxtrim Gummies zu kaufen](https://deutschlandbuzz.de/bioxtrim-gummies-de)**
| {} | VKapseln475/Bioxtrim784 | null | [
"region:us"
] | null | 2024-04-29T05:29:51+00:00 |
null | null | {} | olena-tymchenko/my_awesome_asr_mind_model | null | [
"region:us"
] | null | 2024-04-29T05:31:05+00:00 |
|
null | null | {} | ekko126/mymodel | null | [
"tensorboard",
"region:us"
] | null | 2024-04-29T05:31:35+00:00 |
|
text-generation | transformers |
## Lumimaid 0.1
<center><div style="width: 100%;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;">
</div></center>
This model uses the Llama3 **prompting format**
Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough.
We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data.
This model includes the new Luminae dataset from Ikari.
If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY).
## Credits:
- Undi
- IkariDev
## Description
This repo contains FP16 files of Lumimaid-70B-v0.1-alt.
Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt)
## Training data used:
- [Aesir datasets](https://huggingface.co/MinervaAI)
- [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt)
- [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx
- [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt)
- [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal)
- Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset
- [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly)
- [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly)
- [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly)
- Airoboros (reduced)
- [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced)
## Models used (only for 8B)
- Initial LumiMaid 8B Finetune
- Undi95/Llama-3-Unholy-8B-e4
- Undi95/Llama-3-LewdPlay-8B
## Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
## Others
Undi: If you want to support us, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek | {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]} | NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:32:00+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5-base-news-title-generation_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5-base-news-title-generation_model", "results": []}]} | momoyukki/t5-base-news-title-generation_model | null | [
"transformers",
"tf",
"safetensors",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null | 2024-04-29T05:32:38+00:00 |
null | null | Cos'è EroMax Z Compresse?
EroMax Z Prezzo è una capsula per la salute maschile di alta qualità formulata per supportare la vitalità e i livelli di energia. La sua formula avanzata combina una miscela sinergica di vitamine, minerali ed estratti di erbe, scelti appositamente per promuovere salute e benessere ottimali negli uomini.
Official website:<a href="https://www.nutritionsee.com/eromzxzitz">www.EroMaxZ.com</a>
<p><a href="https://www.nutritionsee.com/eromzxzitz"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/EroMax-Z-Italy-1.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/eromzxzitz">Acquista ora!! Fai clic sul collegamento in basso per ulteriori informazioni e ottieni subito uno sconto del 50%... Affrettati</a>
Official website:<a href="https://www.nutritionsee.com/eromzxzitz">www.EroMaxZ.com</a> | {"license": "apache-2.0"} | EroMaxZ/EroMaxZ | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T05:32:39+00:00 |
question-answering | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Amr-h/AmrdisBert | null | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:33:34+00:00 |
null | null | {"license": "llama2"} | T010/dataanalysis | null | [
"license:llama2",
"region:us"
] | null | 2024-04-29T05:34:59+00:00 |
|
text-generation | transformers |
# Fulcrum-Mistral New
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /content/drive/MyDrive/Fulcrum-Mistral as a base.
### Models Merged
The following models were included in the merge:
1) cognitivecomputations/dolphin-2.8-mistral-7b-v02
2) NousResearch/Hermes-2-Pro-Mistral-7B
3) HuggingFaceH4/zephyr-7b-beta
4) teknium/OpenHermes-2.5-Mistral-7B
5) mlabonne/Zebrafish-7B
6) Open-Orca/Mistral-7B-OpenOrca
7) mistralai/Mistral-7B-Instruct-v0.2 | {"license": "apache-2.0"} | Fulcrum-AI/Ryze | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:2403.19522",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:36:56+00:00 |
null | null | {} | projectwork/whisper-small-siriml | null | [
"region:us"
] | null | 2024-04-29T05:37:46+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** baka999
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | baka999/luckybitch_llama_model | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:38:57+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/y43r1fi | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:39:27+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | team-sanai/llama2B_0.1B_lora_merged_sample | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:40:19+00:00 |
null | null | {} | MD1998/chating_beginner_gpt2_medium_v1 | null | [
"region:us"
] | null | 2024-04-29T05:41:01+00:00 |
|
text-to-audio | transformers | {} | mikhail-panzo/zlm_b128_le5_s12000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:43:33+00:00 |
|
text-to-image | diffusers | {"license": "apache-2.0"} | Mahdy225/JAMAL | null | [
"diffusers",
"safetensors",
"license:apache-2.0",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-29T05:43:57+00:00 |
|
text-generation | transformers |
# llama-3-Open-Ko-8B-instruct-slerp
llama-3-Open-Ko-8B-instruct-slerp is a merge of the following models using [mergekit](https://github.com/arcee-ai/mergekit):
* [beomi/Llama-3-Open-Ko-8B](https://huggingface.co/beomi/Llama-3-Open-Ko-8B)
* [beomi/Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: beomi/Llama-3-Open-Ko-8B
layer_range: [0, 32]
- model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
layer_range: [0, 32]
merge_method: slerp
base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "beomi/Llama-3-Open-Ko-8B", "beomi/Llama-3-Open-Ko-8B-Instruct-preview"]} | DavidAhn/llama-3-Open-Ko-8B-instruct-slerp | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"beomi/Llama-3-Open-Ko-8B",
"beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:44:19+00:00 |
null | null | {"license": "mit"} | Denni022/Speechts | null | [
"license:mit",
"region:us"
] | null | 2024-04-29T05:45:11+00:00 |
|
null | null | {} | Xrunner/hive-e | null | [
"region:us"
] | null | 2024-04-29T05:45:16+00:00 |
|
null | null | {} | niksyy/my_model | null | [
"region:us"
] | null | 2024-04-29T05:45:41+00:00 |
|
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speaker-segmentation-fine-tuned-ami-speaker-diarization
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the diarizers-community/ami_speaker_diarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4425
- Der: 0.1760
- False Alarm: 0.0627
- Missed Detection: 0.0634
- Confusion: 0.0499
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3957 | 1.0 | 1809 | 0.4538 | 0.1799 | 0.0605 | 0.0656 | 0.0537 |
| 0.4027 | 2.0 | 3618 | 0.4446 | 0.1780 | 0.0645 | 0.0627 | 0.0508 |
| 0.3639 | 3.0 | 5427 | 0.4504 | 0.1798 | 0.0669 | 0.0604 | 0.0524 |
| 0.3764 | 4.0 | 7236 | 0.4431 | 0.1762 | 0.0632 | 0.0623 | 0.0508 |
| 0.3916 | 5.0 | 9045 | 0.4425 | 0.1760 | 0.0627 | 0.0634 | 0.0499 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"language": ["en"], "license": "apache-2.0", "tags": ["speaker-diarization", "speaker-segmentation", "generated_from_trainer"], "datasets": ["diarizers-community/ami_speaker_diarization_dataset"], "base_model": "openai/whisper-small", "model-index": [{"name": "speaker-segmentation-fine-tuned-ami-speaker-diarization", "results": []}]} | Ataullha/speaker-segmentation-fine-tuned-ami-speaker-diarization-eng | null | [
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"en",
"dataset:diarizers-community/ami_speaker_diarization_dataset",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:45:52+00:00 |
text-generation | transformers | {"license": "mit"} | migueldeguzmandev/GPT2XL_RLLMv19-6 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:45:55+00:00 |
|
text-classification | transformers |
# Model Card for Model ID
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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| {"library_name": "transformers", "tags": []} | hostechs/NewModelfinetune | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:46:45+00:00 |
null | null | {} | Xrunner/hive-h | null | [
"region:us"
] | null | 2024-04-29T05:47:06+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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| {"library_name": "transformers", "tags": []} | Aharneish/Llama-Chat-final_merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-29T05:48:05+00:00 |
null | null | {"license": "apache-2.0"} | Diboxaa/ConvoAI | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T05:48:34+00:00 |
|
null | peft |
# Model Card for Model ID
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "huggyllama/llama-7b"} | shrenikb/hftestepoch3id3 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"region:us"
] | null | 2024-04-29T05:49:09+00:00 |
null | null | {} | EnumaInc/slimorca_ko | null | [
"region:us"
] | null | 2024-04-29T05:49:14+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/dkoegcx | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:49:59+00:00 |
text-generation | transformers |
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shleeeee/EEVE-open-sft | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:50:39+00:00 |
null | null |
## Lumimaid 0.1
<center><div style="width: 100%;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;">
</div></center>
May 1st 2024: GGUF have been fixed with [this PR of llama.cpp](https://github.com/ggerganov/llama.cpp/pull/6920)
This model uses the Llama3 **prompting format**
Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough.
We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data.
This model includes the new Luminae dataset from Ikari.
If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY).
## Credits:
- Undi
- IkariDev
## Description
This repo contains GGUF files of Lumimaid-70B-v0.1-alt.
Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-GGUF) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-GGUF) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt-GGUF)
## Training data used:
- [Aesir datasets](https://huggingface.co/MinervaAI)
- [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt)
- [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx
- [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt)
- [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal)
- Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset
- [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly)
- [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly)
- [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly)
- Airoboros (reduced)
- [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced)
## Models used (only for 8B)
- Initial LumiMaid 8B Finetune
- Undi95/Llama-3-Unholy-8B-e4
- Undi95/Llama-3-LewdPlay-8B
## Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
## Others
Undi: If you want to support us, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek | {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]} | NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt-GGUF | null | [
"gguf",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-04-29T05:52:05+00:00 |
text-generation | transformers | {"license": "mit"} | migueldeguzmandev/GPT2XL_RLLMv19-7 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:52:18+00:00 |
|
null | null | {"license": "other", "license_name": "tongyi-qianwen", "license_link": "LICENSE"} | OllmOne/CodeQwen1.5-7B-Chat-GGUF | null | [
"gguf",
"license:other",
"region:us"
] | null | 2024-04-29T05:54:00+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/d6i0jxh | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:54:15+00:00 |
null | null | {} | Aharneish/sprit-llama-chat.gguf | null | [
"region:us"
] | null | 2024-04-29T05:54:22+00:00 |
|
null | null | {} | quanquyt/mistral-finetuned-tarot | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | 2024-04-29T05:56:52+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0001_3iters_bs256_nodpo_full6w_iter_3
This model is a fine-tuned version of [ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_iter_2](https://huggingface.co/ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_iter_2) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_iter_2", "model-index": [{"name": "0.0001_3iters_bs256_nodpo_full6w_iter_3", "results": []}]} | ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_iter_3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:58:02+00:00 |
automatic-speech-recognition | transformers | {} | Dearlie/Dearlie | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:58:19+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# meditron-7b-dpo-full-sft-wo-live_qa
This model is a fine-tuned version of [Minbyul/meditron-7b-wo-live_qa-sft](https://huggingface.co/Minbyul/meditron-7b-wo-live_qa-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6677
- Rewards/chosen: -0.0447
- Rewards/rejected: -0.1284
- Rewards/accuracies: 0.875
- Rewards/margins: 0.0838
- Logps/rejected: -656.5873
- Logps/chosen: -92.6797
- Logits/rejected: -1.2739
- Logits/chosen: -1.2205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "llama2", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/meditron-7b-wo-live_qa-sft", "model-index": [{"name": "meditron-7b-dpo-full-sft-wo-live_qa", "results": []}]} | Minbyul/meditron-7b-dpo-full-sft-wo-live_qa | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:Minbyul/meditron-7b-wo-live_qa-sft",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T05:58:59+00:00 |
automatic-speech-recognition | transformers | {"license": "gpl"} | wadhwani-ai/orf_gu_wav2vec2_v3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"license:gpl",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T05:59:18+00:00 |
|
null | null | {"license": "llama2"} | vmusini/sql-create-context-F-T | null | [
"license:llama2",
"region:us"
] | null | 2024-04-29T05:59:34+00:00 |
|
text-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | presencesw/phobert-large-vinli-3-label_v2 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T06:00:58+00:00 |
null | null | {} | CenturionHeart/LABPL | null | [
"region:us"
] | null | 2024-04-29T06:01:35+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** pggroch
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | pggroch/lora_model | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T06:02:12+00:00 |
null | null | {} | vmusini/sql_create_context_FT | null | [
"region:us"
] | null | 2024-04-29T06:02:15+00:00 |
|
null | null | {} | automated-finetunning/bart_test_18 | null | [
"region:us"
] | null | 2024-04-29T06:02:26+00:00 |
|
null | null | {} | automated-finetunning/bart_test_16 | null | [
"region:us"
] | null | 2024-04-29T06:03:57+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** domliu
- **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. | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"} | domliu/Codellama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T06:05:56+00:00 |
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