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text2text-generation | transformers |
This is a T5-Small Model trained of pubmed dataset of articles and abstracts. | {"language": ["en"], "license": "mit", "datasets": ["scientific_papers"]} | jaimik69/t5-small-pubmed | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:scientific_papers",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T18:49:59+00:00 |
text-generation | transformers | {"license": "apache-2.0"} | vincentoh/gpt2-aligned-orpo | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T18:52:01+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "285.99 +/- 10.37", "name": "mean_reward", "verified": false}]}]}]} | cha5yn/PP0LunarLanderV2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-29T18:52:19+00:00 |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| {"tags": ["fastai"]} | osvitore/famosos | null | [
"fastai",
"region:us",
"has_space"
] | null | 2024-04-29T18:52:34+00:00 |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| {"tags": ["fastai"]} | kylar55/entregable2 | null | [
"fastai",
"region:us",
"has_space"
] | null | 2024-04-29T18:55:35+00:00 |
text-to-image | diffusers |
# Knives and time, a damaged LORA
Knives and time is a LORA model trained on public domain and CC0 images. The data was collected from wikimedia commons and annotated with a mixture of BLIP annotation and manual annotation.
## Usage
The base trigger is katabd.
For paintings: painting, yellowed, abrasion, scratches, flaking, damage, torn, cut, pierced.
For photography: photograph, monochrome, overlit, overexposed, torn, cut.
I recommend using it combined with an LCM sampler.
## Training
The model was trained on the 'knives and time, a damaged dataset' for ~45 minutes resulting in six epochs. | {"language": ["en"], "license": "cc-by-4.0", "library_name": "diffusers", "tags": ["damage", "lora"], "pipeline_tag": "text-to-image"} | calm-and-collected/knives_and_time | null | [
"diffusers",
"damage",
"lora",
"text-to-image",
"en",
"license:cc-by-4.0",
"region:us"
] | null | 2024-04-29T18:55:57+00:00 |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deit-small-patch16-224-finetuned-MUSCIMApp
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3384
- Accuracy: 0.8743
- Precision: 0.8675
- Recall: 0.8743
- F1 Score: 0.8584
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.3723 | 1.0 | 563 | 0.3915 | 0.8646 | 0.8512 | 0.8646 | 0.8453 |
| 0.3147 | 2.0 | 1126 | 0.3384 | 0.8743 | 0.8675 | 0.8743 | 0.8584 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "model-index": [{"name": "deit-small-patch16-224-finetuned-MUSCIMApp", "results": []}]} | nadimkanazi/deit-small-patch16-224-finetuned-MUSCIMApp | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T18:56:06+00:00 |
text-generation | transformers | # Octopus V4: Graph of language models
## Octopus V4
<p align="center">
- <a href="https://www.nexa4ai.com/" target="_blank">Nexa AI Website</a>
- <a href="https://github.com/NexaAI/octopus-v4" target="_blank">Octopus-v4 Github</a>
- <a href="https://arxiv.org/abs/2404.19296" target="_blank">ArXiv</a>
- <a href="https://huggingface.co/spaces/NexaAIDev/domain_llm_leaderboard" target="_blank">Domain LLM Leaderbaord</a>
- <a href="https://graph.nexa4ai.com/" target="_blank">Graph demo</a>
</p>
<p align="center" width="100%">
<a><img src="octopus-v4-logo.png" alt="nexa-octopus" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a>
</p>
## Introduction
Octopus-V4-3B, an advanced open-source language model with 3 billion parameters, serves as the master node in Nexa AI's envisioned graph of language models. Tailored specifically for the MMLU benchmark topics, this model efficiently translates user queries into formats that specialized models can effectively process. It excels at directing these queries to the appropriate specialized model, ensuring precise and effective query handling.
📱 **Compact Size**: Octopus-V4-3B is compact, enabling it to operate on smart devices efficiently and swiftly.
🐙 **Accuracy**: Octopus-V4-3B accurately maps user queries to the specialized model using a functional token design, enhancing its precision.
💪 **Reformat Query**: Octopus-V4-3B assists in converting natural human language into a more professional format, improving query description and resulting in more accurate responses.
## Example Use Cases
```text
Query: Tell me the result of derivative of x^3 when x is 2?
# <nexa_4> represents the math gpt.
Response: <nexa_4> ('Determine the derivative of the function f(x) = x^3 at the point where x equals 2, and interpret the result within the context of rate of change and tangent slope.')<nexa_end>
```
You can run the model on a GPU using the following code.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import time
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"NexaAIDev/Octopus-v4",
device_map="cuda:0",
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("NexaAIDev/octopus-v4-finetuned-v1")
question = "Tell me the result of derivative of x^3 when x is 2?"
inputs = f"<|system|>You are a router. Below is the query from the users, please call the correct function and generate the parameters to call the function.<|end|><|user|>{question}<|end|><|assistant|>"
print('\n============= Below is the response ==============\n')
# You should consider to use early stopping with <nexa_end> token to accelerate
input_ids = tokenizer(inputs, return_tensors="pt")['input_ids'].to(model.device)
generated_token_ids = []
start = time.time()
# set a large enough number here to avoid insufficient length
for i in range(200):
next_token = model(input_ids).logits[:, -1].argmax(-1)
generated_token_ids.append(next_token.item())
input_ids = torch.cat([input_ids, next_token.unsqueeze(1)], dim=-1)
# 32041 is the token id of <nexa_end>
if next_token.item() == 32041:
break
print(tokenizer.decode(generated_token_ids))
end = time.time()
print(f'Elapsed time: {end - start:.2f}s')
```
## License
This model was trained on commercially viable data. For use of our model, refer to the [license information](https://www.nexa4ai.com/licenses/licenses-v4).
## Performance
### Model Selection
We leverage the latest Language Large Models for a variety of domains. Below is a summary of the chosen models for each category. In cases where no specialized model exists for a subject, we utilize generic models like Llama3-8b.
| **Model** | **Category** | **Subjects** |
|-----------------------------------------|--------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `jondurbin/bagel-8b-v1.0` | Biology | `college_biology`, `high_school_biology` |
| `Weyaxi/Einstein-v6.1-Llama3-8B` | Physics | `astronomy`, `college_physics`, `conceptual_physics`, `high_school_physics` |
| `meta-llama/Meta-Llama-3-8B-Instruct` | Business | `business_ethics`, `management`, `marketing` |
| `meta-llama/Meta-Llama-3-8B-Instruct` | Chemistry | `college_chemistry`, `high_school_chemistry` |
| `abacusai/Llama-3-Smaug-8B` | Computer Science | `college_computer_science`, `computer_security`, `high_school_computer_science`, `machine_learning` |
| `Open-Orca/Mistral-7B-OpenOrca` | Math | `abstract_algebra`, `college_mathematics`, `elementary_mathematics`, `high_school_mathematics`, `high_school_statistics` |
| `meta-llama/Meta-Llama-3-8B-Instruct` | Economics | `econometrics`, `high_school_macroeconomics`, `high_school_microeconomics` |
| `AdaptLLM/medicine-chat` | Health | `anatomy`, `clinical_knowledge`, `college_medicine`, `human_aging`, `medical_genetics`, `nutrition`, `professional_medicine`, `virology` |
| `STEM-AI-mtl/phi-2-electrical-engineering` | Engineering | `electrical_engineering` |
| `meta-llama/Meta-Llama-3-8B-Instruct` | Philosophy | `formal_logic`, `logical_fallacies`, `moral_disputes`, `moral_scenarios`, `philosophy`, `world_religions` |
| `microsoft/Phi-3-mini-128k-instruct` | Other | `global_facts`, `miscellaneous`, `professional_accounting` |
| `meta-llama/Meta-Llama-3-8B-Instruct` | History | `high_school_european_history`, `high_school_us_history`, `high_school_world_history`, `prehistory` |
| `meta-llama/Meta-Llama-3-8B-Instruct` | Culture | `human_sexuality`, `sociology` |
| `AdaptLLM/law-chat` | Law | `international_law`, `jurisprudence`, `professional_law` |
| `meta-llama/Meta-Llama-3-8B-Instruct` | Psychology | `high_school_psychology`, `professional_psychology` |
### MMLU Benchmark Results (5-shot learning)
Here are the comparative MMLU scores for various models tested under a 5-shot learning setup:
| **Model** | **MMLU Score** |
|-----------------------------------|----------------|
| Octopus-V4 | **74.8%** |
| GPT-3.5 | 70.0% |
| Phi-3-mini-128k-instruct | 68.1% |
| OpenELM-3B | 26.7% |
| Lamma3-8b-instruct | 68.4% |
| Gemma-2b | 42.3% |
| Gemma-7b | 64.3% |
### Domain LLM Leaderboard
Explore our collection of domain-specific large language models (LLMs) or contribute by suggesting new models tailored to specific domains. For detailed information on available models and to engage with our community, please visit our [Domain LLM Leaderboard](https://huggingface.co/spaces/NexaAIDev/domain_llm_leaderboard).
## References
We thank the Microsoft team for their amazing model!
```
@article{abdin2024phi,
title={Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone},
author={Abdin, Marah and Jacobs, Sam Ade and Awan, Ammar Ahmad and Aneja, Jyoti and Awadallah, Ahmed and Awadalla, Hany and Bach, Nguyen and Bahree, Amit and Bakhtiari, Arash and Behl, Harkirat and others},
journal={arXiv preprint arXiv:2404.14219},
year={2024}
}
```
## Citation
```
@misc{chen2024octopus,
title={Octopus v4: Graph of language models},
author={Wei Chen and Zhiyuan Li},
year={2024},
eprint={2404.19296},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Contact
Please [contact us](mailto:[email protected]) to reach out for any issues and comments! | {"language": ["en"], "license": "cc-by-nc-4.0", "tags": ["AI agent", "Graph"], "base_model": "microsoft/Phi-3", "inference": false, "space": false, "spaces": false, "model-index": [{"name": "Octopus-V4-3B", "results": []}]} | NexaAIDev/Octopus-v4 | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"AI agent",
"Graph",
"conversational",
"custom_code",
"en",
"arxiv:2404.19296",
"base_model:microsoft/Phi-3",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"region:us"
] | null | 2024-04-29T18:57:22+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. -->
# SDXL LoRA DreamBooth - embracellm/sushi02_LoRA
<Gallery />
## Model description
These are embracellm/sushi02_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sushi02 to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi02_LoRA/tree/main) them in the Files & versions tab.
## 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": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of sushi02", "widget": []} | embracellm/sushi02_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-29T18:57:35+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. -->
# DistilGPT2-Beatles-model
This model is a fine-tuned version of [wvangils/DistilGPT2-Beatles-Lyrics-finetuned](https://huggingface.co/wvangils/DistilGPT2-Beatles-Lyrics-finetuned) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9446
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 16
- 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_steps: 1
- training_steps: 250
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.171 | 0.0 | 25 | 1.9776 |
| 2.1476 | 0.0 | 50 | 1.9749 |
| 2.1443 | 0.0 | 75 | 1.9618 |
| 2.1231 | 0.0 | 100 | 1.9567 |
| 2.1389 | 0.0 | 125 | 1.9535 |
| 2.1542 | 0.0 | 150 | 1.9507 |
| 2.107 | 0.01 | 175 | 1.9536 |
| 2.1268 | 0.01 | 200 | 1.9514 |
| 2.1264 | 0.01 | 225 | 1.9443 |
| 2.1182 | 0.01 | 250 | 1.9446 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.1
- Pytorch 1.13.1
- Datasets 2.17.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "wvangils/DistilGPT2-Beatles-Lyrics-finetuned", "model-index": [{"name": "DistilGPT2-Beatles-model", "results": []}]} | anushkat/DistilGPT2-Beatles-model | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:wvangils/DistilGPT2-Beatles-Lyrics-finetuned",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T18:57:55+00:00 |
null | null | {} | vkshen/Test | null | [
"region:us"
] | null | 2024-04-29T18:58:08+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]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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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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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[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_18-58-48 | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T18:58:49+00:00 |
text-to-image | diffusers |
# 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 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "diffusers"} | rubbrband/modernDisneyXL_v3 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-29T18:59:20+00:00 |
null | null | {"license": "mit"} | alx-ai/NogglesXL | null | [
"license:mit",
"region:us"
] | null | 2024-04-29T18:59:57+00:00 |
|
summarization | transformers |
This is a BART-base model finetuned on pubmed dataset of articles and abstracts | {"language": ["en"], "license": "mit", "datasets": ["scientific_papers"], "pipeline_tag": "summarization"} | jaimik69/bart-base-pubmed | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:scientific_papers",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:01:03+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | nem012/gemma2b-lrl | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:01:04+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** vonewman
- **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", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | vonewman/llama-3-8b-finetuned-q4_k_m-gguf | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:01:42+00:00 |
text-to-image | diffusers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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| {"library_name": "diffusers"} | Niggendar/artisticpony_darkstyleponyV10 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-29T19:02:00+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. -->
# gpt2-psych_chatbot
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8540
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 13 | 7.1673 |
| No log | 2.0 | 26 | 6.8540 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "gpt2-psych_chatbot", "results": []}]} | ZyanLeyy/gpt2-psych_chatbot | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:03:25+00:00 |
null | null | {} | Ray011/falcon7binstructApril29 | null | [
"region:us"
] | null | 2024-04-29T19:05:50+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. -->
# distilbart-pubmed-20k-bottom-tokens
This model is a fine-tuned version of [philschmid/distilbart-cnn-12-6-samsum](https://huggingface.co/philschmid/distilbart-cnn-12-6-samsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3544
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.542 | 0.7477 | 500 | 1.3544 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "philschmid/distilbart-cnn-12-6-samsum", "model-index": [{"name": "distilbart-pubmed-20k-bottom-tokens", "results": []}]} | Chung-Fan/distilbart-pubmed-20k-bottom-tokens | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:philschmid/distilbart-cnn-12-6-samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:06:01+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- 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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | nem012/gemma2b-lrm | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:06:18+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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<!-- Provide the basic links for the model. -->
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<!-- 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": []} | OwOOwO/final62 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:06:51+00:00 |
null | null | {"language": ["ja"], "tags": ["art"]} | jehad13/ASH3a3a | null | [
"art",
"ja",
"region:us"
] | null | 2024-04-29T19:07:56+00:00 |
|
null | null |
This repository contains quantized versions of [BramVanroy/fietje-2b](https://huggingface.co/BramVanroy/fietje-2b).
Available quantization types and expected performance differences compared to base `f16`, higher perplexity=worse (from llama.cpp):
```
Q3_K_M : 3.07G, +0.2496 ppl @ LLaMA-v1-7B
Q4_K_M : 3.80G, +0.0532 ppl @ LLaMA-v1-7B
Q5_K_M : 4.45G, +0.0122 ppl @ LLaMA-v1-7B
Q6_K : 5.15G, +0.0008 ppl @ LLaMA-v1-7B
Q8_0 : 6.70G, +0.0004 ppl @ LLaMA-v1-7B
F16 : 13.00G @ 7B
```
Also available on [ollama](https://ollama.com/bramvanroy/fietje-2b).
Quants were made with release [`b2777`](https://github.com/ggerganov/llama.cpp/releases/tag/b2777) of llama.cpp.
| {"language": ["nl"], "license": "mit", "tags": ["gguf"]} | BramVanroy/fietje-2b-gguf | null | [
"gguf",
"nl",
"license:mit",
"region:us"
] | null | 2024-04-29T19:08:02+00:00 |
text-generation | transformers | {} | TitanML/Meta-Llama-3-8B-Instruct-AWQ-4bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-29T19:08:50+00:00 |
|
null | null |
This repository contains quantized versions of [BramVanroy/fietje-2b-instruct](https://huggingface.co/BramVanroy/fietje-2b-instruct).
Available quantization types and expected performance differences compared to base `f16`, higher perplexity=worse (from llama.cpp):
```
Q3_K_M : 3.07G, +0.2496 ppl @ LLaMA-v1-7B
Q4_K_M : 3.80G, +0.0532 ppl @ LLaMA-v1-7B
Q5_K_M : 4.45G, +0.0122 ppl @ LLaMA-v1-7B
Q6_K : 5.15G, +0.0008 ppl @ LLaMA-v1-7B
Q8_0 : 6.70G, +0.0004 ppl @ LLaMA-v1-7B
F16 : 13.00G @ 7B
```
Also available on [ollama](https://ollama.com/bramvanroy/fietje-2b-instruct).
Quants were made with release [`b2777`](https://github.com/ggerganov/llama.cpp/releases/tag/b2777) of llama.cpp.
``` | {"language": ["nl"], "license": "mit", "tags": ["gguf"]} | BramVanroy/fietje-2b-instruct-gguf | null | [
"gguf",
"nl",
"license:mit",
"region:us"
] | null | 2024-04-29T19:08:56+00:00 |
question-answering | 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. -->
# cancerfarore/distilbert-base-uncased-CancerFarore-Model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1390
- Train End Logits Accuracy: 0.9575
- Train Start Logits Accuracy: 0.9540
- Validation Loss: 2.8433
- Validation End Logits Accuracy: 0.5442
- Validation Start Logits Accuracy: 0.5303
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 2.1257 | 0.4290 | 0.4152 | 1.4687 | 0.5228 | 0.5239 | 0 |
| 1.3124 | 0.5968 | 0.5795 | 1.3553 | 0.5747 | 0.5676 | 1 |
| 0.9498 | 0.7047 | 0.6820 | 1.5003 | 0.5566 | 0.5457 | 2 |
| 0.6828 | 0.7854 | 0.7654 | 1.8131 | 0.5393 | 0.5111 | 3 |
| 0.4912 | 0.8458 | 0.8307 | 1.7845 | 0.5687 | 0.5638 | 4 |
| 0.3512 | 0.8906 | 0.8756 | 2.2275 | 0.5397 | 0.5258 | 5 |
| 0.2676 | 0.9146 | 0.9054 | 2.4911 | 0.5401 | 0.5216 | 6 |
| 0.2044 | 0.9372 | 0.9289 | 2.5377 | 0.5551 | 0.5390 | 7 |
| 0.1633 | 0.9480 | 0.9440 | 2.7288 | 0.5548 | 0.5412 | 8 |
| 0.1390 | 0.9575 | 0.9540 | 2.8433 | 0.5442 | 0.5303 | 9 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "cancerfarore/distilbert-base-uncased-CancerFarore-Model", "results": []}]} | cancerfarore/distilbert-base-uncased-CancerFarore-Model | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:09:15+00:00 |
null | null |
This repository contains quantized versions of [BramVanroy/fietje-2b-chat](https://huggingface.co/BramVanroy/fietje-2b-chat).
Available quantization types and expected performance differences compared to base `f16`, higher perplexity=worse (from llama.cpp):
```
Q3_K_M : 3.07G, +0.2496 ppl @ LLaMA-v1-7B
Q4_K_M : 3.80G, +0.0532 ppl @ LLaMA-v1-7B
Q5_K_M : 4.45G, +0.0122 ppl @ LLaMA-v1-7B
Q6_K : 5.15G, +0.0008 ppl @ LLaMA-v1-7B
Q8_0 : 6.70G, +0.0004 ppl @ LLaMA-v1-7B
F16 : 13.00G @ 7B
```
Also available on [ollama](https://ollama.com/bramvanroy/fietje-2b-chat).
Quants were made with release [`b2777`](https://github.com/ggerganov/llama.cpp/releases/tag/b2777) of llama.cpp.
| {"language": ["nl"], "license": "mit", "tags": ["gguf"]} | BramVanroy/fietje-2b-chat-gguf | null | [
"gguf",
"nl",
"license:mit",
"region:us"
] | null | 2024-04-29T19:09:34+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. -->
# mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_364_epochs_10_Hyperparameter
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3041
- Accuracy: 0.438
- Chrf: 0.767
- Bleu: 0.596
- Sacrebleu: 0.6
- Rouge1: 0.616
- Rouge2: 0.438
- Rougel: 0.569
- Rougelsum: 0.616
- Meteor: 0.534
## 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: 5.564594782840898e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 3407
- 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_steps: 5
- num_epochs: 10
-
#used sgd optim
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:|
| 1.3629 | 0.9890 | 45 | 1.3069 | 0.438 | 0.766 | 0.595 | 0.6 | 0.618 | 0.438 | 0.57 | 0.619 | 0.53 |
| 1.3187 | 2.0 | 91 | 1.3059 | 0.438 | 0.768 | 0.596 | 0.6 | 0.617 | 0.438 | 0.57 | 0.618 | 0.527 |
| 1.9858 | 2.9890 | 136 | 1.3051 | 0.438 | 0.767 | 0.596 | 0.6 | 0.618 | 0.439 | 0.57 | 0.618 | 0.531 |
| 1.962 | 4.0 | 182 | 1.3041 | 0.438 | 0.767 | 0.596 | 0.6 | 0.616 | 0.438 | 0.569 | 0.616 | 0.534 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.16.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "metrics": ["accuracy", "bleu", "sacrebleu", "rouge"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "model-index": [{"name": "mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_364_epochs_10_Hyperparameter", "results": []}]} | vdavidr/mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_364_epochs_10_Hyperparameter | null | [
"peft",
"safetensors",
"trl",
"sft",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T19:09:42+00:00 |
null | null |
# MistrollExperiment27pastiche-7B
MistrollExperiment27pastiche-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: BarraHome/Mistroll-7B-v2.2
- model: automerger/Experiment27Pastiche-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/MistrollExperiment27pastiche-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/MistrollExperiment27pastiche-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T19:10:57+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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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": []} | XsoraS/gpt2-medium_chat | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:11:23+00:00 |
null | diffusers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
```python
@dataclass
class TrainingConfig:
image_size = 512 # the generated image resolution
train_batch_size = 2
eval_batch_size = 2 # how many images to sample during evaluation
num_epochs = 100
gradient_accumulation_steps = 1
learning_rate = 1e-4
lr_warmup_steps = 500
save_image_epochs = 10
save_model_epochs = 30
mixed_precision = 'fp16' # `no` for float32, `fp16` for automatic mixed precision
output_dir = 'ddpm-butterflies-128' # the model namy locally and on the HF Hub
push_to_hub = True # whether to upload the saved model to the HF Hub
hub_private_repo = False
overwrite_output_dir = True # overwrite the old model when re-running the notebook
seed = 0
config = TrainingConfig()
```
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers 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": "diffusers"} | uisikdag/robin3 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-04-29T19:11:31+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. -->
# bart-base-with-nothing
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7986 | 1.0 | 4383 | 0.7095 |
| 0.6593 | 2.0 | 8766 | 0.6407 |
| 0.5589 | 3.0 | 13149 | 0.6121 |
| 0.4935 | 4.0 | 17532 | 0.5980 |
| 0.4314 | 5.0 | 21915 | 0.6005 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-with-nothing", "results": []}]} | cbjun99/bart-base-with-nothing | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:11:48+00:00 |
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="Zan135/q-FrozenLake-v1-4x4-withSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
| {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-withSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "metrics": [{"type": "mean_reward", "value": "0.59 +/- 0.49", "name": "mean_reward", "verified": false}]}]}]} | Zan135/q-FrozenLake-v1-4x4-withSlippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-29T19:12:49+00:00 |
text-to-image | diffusers |
# 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 🧨 diffusers 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "diffusers"} | Niggendar/artisticpony_photostyleponyV10 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-29T19:13:37+00:00 |
null | null | {} | Greko89/Yoli2 | null | [
"region:us"
] | null | 2024-04-29T19:13:57+00:00 |
|
text-generation | transformers | # Megac4ai-command-r-plus
[GGUF version is here.](https://huggingface.co/nitky/Megac4ai-command-r-plus-gguf)
🚨 **This model is created using the special mergekit that supports c4ai-command-r-plus.**
This is a 160b frankenmerge model created by interleaving layers of [CohereForAI/c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus) with itself using mergekit.
## Output comparison
### Test Case Details
Condition: temperature=0.3
```
<|START_OF_TURN_TOKEN|><|USER_TOKEN|>ティム: やあ、調子はどう？
キム: いろいろやろうとしてたんだけど、また先延ばしにしちゃったよ。
ティム: 何をしようとしていたの？
キム: 大学の課題だよ。どうにもやる気が出なくてね。
ティム: 集中できないなら、ポモドーロ・テクニックをするといいよ。
キム: 何それ？
ティム: 25分作業して、5分休憩するのを繰り返すんだよ。一回あたりの作業時間が短くて集中できるよ。
キム: うーん、集中っていうわけじゃないんだよね
ティム: じゃあ1日に5分だけでいいから机で課題をするっていうのはどう？
キム: 5分じゃ何もできなくない？
ティム: 短い時間でもいいから机で作業するっていうのがポイントなんだよ。むしろもっとやりたい、くらいで止めておくと毎日続くもっと長い時間できるようになるよ。
キム: 確かにそれならできるかも。ありがとう！
Q: キムは何をやってみようとしていますか？また何故それをやろうとしていますか？<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
```
This task is included in [elyza/ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100).
### Output Example
| Model | Output |
|-------|--------|
| CohereForAI/c4ai-command-r-plus | キムは大学の課題をやろうとしています。しかし、やる気が起きず、先延ばしにしてしまったようです。 |
| nitky/Megac4ai-command-r-plus | キムは大学の課題をやろうとしています。やる気が出ず、集中して作業することができないため、ティムにアドバイスを求めています。ティムが提案したポモドーロ・テクニックや、1日に5分だけ机で課題をするという方法を試すことで、課題に取り組む習慣を身につけようとしています。 |
## Test environment
This model was tested using [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main). I use preset `min_p` and `Null preset` with temperature=0.3 for Generation.
## Usage
Please install `transformers` from the source repository that includes the necessary changes for this model.
```python
# pip install 'git+https://github.com/huggingface/transformers.git'
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "nitky/megac4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
### Quantized model through bitsandbytes, 4-bit precision
```python
# pip install 'git+https://github.com/huggingface/transformers.git' bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_4bit=True)
model_id = "nitky/megac4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* [CohereForAI/c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: float16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 20]
model: CohereForAI/c4ai-command-r-plus
- sources:
- layer_range: [11, 31]
model: CohereForAI/c4ai-command-r-plus
- sources:
- layer_range: [22, 42]
model: CohereForAI/c4ai-command-r-plus
- sources:
- layer_range: [33, 53]
model: CohereForAI/c4ai-command-r-plus
- sources:
- layer_range: [44, 64]
model: CohereForAI/c4ai-command-r-plus
```
| {"language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["CohereForAI/c4ai-command-r-plus"], "pipeline_tag": "text-generation"} | nitky/Megac4ai-command-r-plus | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"base_model:CohereForAI/c4ai-command-r-plus",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:14:11+00:00 |
null | null | {"license": "openrail"} | BunnyToon/blitzo | null | [
"license:openrail",
"region:us"
] | null | 2024-04-29T19:16:10+00:00 |
|
null | null | {"license": "mit"} | hackint0sh/phi-3-MedQuad | null | [
"license:mit",
"region:us"
] | null | 2024-04-29T19:17:22+00:00 |
|
feature-extraction | transformers |
# phospho-small
This is a SetFit model that can be used for Text Classification on CPU.
The model has been trained using an efficient few-shot learning technique.
## Usage
```python
from setfit import SetFitModel
model = SetFitModel.from_pretrained("phospho-small-8963ba3")
outputs = model.predict(["This is a sentence to classify", "Another sentence"])
```
## References
This work was possible thanks to the SetFit library and the work of:
Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren (2022). Efficient Few-Shot Learning Without Prompts.
ArXiv: [https://doi.org/10.48550/arxiv.2209.11055](https://doi.org/10.48550/arxiv.2209.11055)
| {"language": "en", "license": "apache-2.0"} | phospho-app/phospho-small-8963ba3 | null | [
"transformers",
"safetensors",
"mpnet",
"feature-extraction",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:17:38+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.
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### 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. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model8 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:17:54+00:00 |
null | null | {} | imagineaiuser/llam3-8b-ft-test-orpo | null | [
"region:us"
] | null | 2024-04-29T19:20:09+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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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
<!-- 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]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | giux78/llama3-8B-usenet-merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:20:40+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | imagineaiuser/OrpoLlama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:20:43+00:00 |
null | null | {} | LinxuanPastel/GuimUltimo | null | [
"region:us"
] | null | 2024-04-29T19:20:53+00:00 |
|
null | peft |
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- PEFT 0.10.0 | {"library_name": "peft", "base_model": "openai/whisper-tiny"} | sin2piusc/whisper-tiny-test-grad | null | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-tiny",
"region:us"
] | null | 2024-04-29T19:23:02+00:00 |
feature-extraction | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Ehsanl/e5-base-chnk-ep3 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:23:47+00:00 |
null | peft |
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"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
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null | peft |
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- PEFT 0.10.0 | {"library_name": "peft", "base_model": "gpt2"} | vincentoh/gpt2-lora-aligned-orpo | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | null | 2024-04-29T19:25:13+00:00 |
text-to-image | diffusers |
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| {"library_name": "diffusers"} | Niggendar/tPonynai3_v41OptimizedFromV4 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-29T19:27:12+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | namratanwani/fiqa2018-targetextraction | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:27:15+00:00 |
null | null | {} | crusoeai/Llama-3-8B-Instruct-Gradient-1048k | null | [
"gguf",
"region:us"
] | null | 2024-04-29T19:27:56+00:00 |
|
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Elhassnaoui-2001/mistral_7b-Finetuned-Screenplays_Completed | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:28:29+00:00 |
text-generation | transformers | {} | TitanML/Meta-Llama-3-8B-AWQ-4bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-29T19:31:24+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | cilantro9246/4tqefz7 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:31:57+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/a416bpt | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:32:09+00:00 |
null | null | {"license": "llama3"} | VenitaL/venital_vinallama-pef-7b-chat | null | [
"safetensors",
"license:llama3",
"region:us"
] | null | 2024-04-29T19:32:23+00:00 |
|
null | null | {"license": "apache-2.0"} | thedatag/MondayKnight | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T19:33:00+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-pubmed-20k-bottom-tokens
This model is a fine-tuned version of [tuner007/pegasus_summarizer](https://huggingface.co/tuner007/pegasus_summarizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3139
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.6173 | 0.7477 | 500 | 1.3139 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "tuner007/pegasus_summarizer", "model-index": [{"name": "pegasus-pubmed-20k-bottom-tokens", "results": []}]} | Chung-Fan/pegasus-pubmed-20k-bottom-tokens | null | [
"transformers",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:tuner007/pegasus_summarizer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:33:18+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. -->
# whisper-tiny-test2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- 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
- lr_scheduler_warmup_ratio: 0.3
- training_steps: 10000
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.10.0
- Transformers 4.41.0.dev0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-large-v2", "model-index": [{"name": "whisper-tiny-test2", "results": []}]} | sin2piusc/whisper-tiny-test2 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-large-v2",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T19:34:06+00:00 |
null | null | This model is fine tuned by the dataset which is produced by the chatgpt and is about the 'medical area' | {"license": "mit"} | SallySun/llama2_chatdoctor_finetuned | null | [
"license:mit",
"region:us"
] | null | 2024-04-29T19:35:33+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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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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## 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
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## 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/smdwtbn | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:37:33+00:00 |
null | null | {} | VasuJanapala/fifi_classification | null | [
"region:us"
] | null | 2024-04-29T19:38:12+00:00 |
|
null | null | # GGUF quants for [**bigcode/starcoder2-15b-instruct-v0.1**](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | {} | neopolita/starcoder2-15b-instruct-v0.1-gguf | null | [
"gguf",
"region:us"
] | null | 2024-04-29T19:39:52+00:00 |
text-to-image | diffusers | # Latex Dress
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Arczisan/latex_dress/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "UNICODE\u0000\u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000l\u0000t\u0000x\u0000d\u0000r\u00003\u0000s\u0000s\u0000v\u00004\u0000:\u00001\u0000>\u0000 \u0000 \u0000(\u0000(\u0000l\u0000t\u0000x\u0000d\u0000r\u00003\u0000s\u0000s\u0000)\u0000)\u0000,\u0000 \u0000b\u0000l\u0000u\u0000e\u0000 \u0000 \u0000l\u0000t\u0000x\u0000d\u0000r\u00003\u0000s\u0000s\u0000,\u0000 \u0000s\u0000t\u0000r\u0000a\u0000p\u0000l\u0000e\u0000s\u0000s\u0000,\u0000 \u0000s\u0000l\u0000e\u0000e\u0000v\u0000e\u0000l\u0000e\u0000s\u0000s\u0000,\u0000 \u0000b\u0000e\u0000s\u0000t\u0000 \u0000q\u0000u\u0000a\u0000l\u0000i\u0000t\u0000y\u0000,\u0000 \u0000m\u0000a\u0000s\u0000t\u0000e\u0000r\u0000p\u0000i\u0000e\u0000c\u0000e\u0000,\u0000 \u0000p\u0000h\u0000o\u0000t\u0000o\u0000r\u0000e\u0000a\u0000l\u0000i\u0000s\u0000t\u0000i\u0000c\u0000,\u0000 \u0000u\u0000l\u0000t\u0000r\u0000a\u0000r\u0000e\u0000a\u0000l\u0000i\u0000s\u0000t\u0000i\u0000c\u0000,\u0000 \u0000p\u0000r\u0000o\u0000f\u0000e\u0000s\u0000s\u0000i\u0000o\u0000n\u0000a\u0000l\u0000 \u0000p\u0000h\u0000o\u0000t\u0000o\u0000g\u0000r\u0000a\u0000p\u0000h\u0000 \u0000s\u0000h\u0000o\u0000t\u0000,\u0000 \u0000p\u0000h\u0000o\u0000t\u0000o\u0000g\u0000r\u0000a\u0000p\u0000h\u0000 \u0000o\u0000f\u0000 \u0000a\u0000 \u0000b\u0000r\u0000o\u0000w\u0000n\u0000 \u0000h\u0000a\u0000i\u0000r\u0000 \u0000g\u0000i\u0000r\u0000l\u0000,\u0000 \u0000v\u0000e\u0000r\u0000y\u0000 \u0000l\u0000o\u0000n\u0000g\u0000 \u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000s\u0000t\u0000r\u0000a\u0000i\u0000g\u0000h\u0000t\u0000 \u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000d\u0000a\u0000n\u0000c\u0000i\u0000n\u0000g\u0000,\u0000 \u0000a\u0000r\u0000m\u0000p\u0000i\u0000t\u0000s\u0000,\u0000 \u0000c\u0000r\u0000o\u0000w\u0000d\u0000,\u0000 \u0000b\u0000l\u0000u\u0000s\u0000h\u0000i\u0000n\u0000g\u0000,\u0000 \u0000s\u0000m\u0000i\u0000l\u0000e\u0000,\u0000 \u0000l\u0000o\u0000o\u0000k\u0000i\u0000n\u0000g\u0000 \u0000a\u0000t\u0000 \u0000v\u0000i\u0000e\u0000w\u0000e\u0000r\u0000,\u0000 \u0000c\u0000o\u0000l\u0000o\u0000r\u0000f\u0000u\u0000l\u0000,\u0000n\u0000i\u0000g\u0000h\u0000t\u0000c\u0000l\u0000u\u0000b\u0000,\u0000 \u0000n\u0000e\u0000o\u0000n\u0000 \u0000l\u0000i\u0000g\u0000h\u0000t\u0000s\u0000,\u0000 \u0000s\u0000p\u0000o\u0000t\u0000 \u0000l\u0000i\u0000g\u0000h\u0000t\u0000s\u0000,\u0000 \u0000l\u0000a\u0000s\u0000e\u0000r\u0000 \u0000l\u0000i\u0000g\u0000h\u0000t\u0000s\u0000,\u0000 \u0000d\u0000i\u0000m\u0000 \u0000l\u0000i\u0000g\u0000h\u0000t\u0000 \u0000", "output": {"url": "images/00011-55081254.jpeg"}}], "base_model": "runwayml/stable-diffusion-v1-5"} | Arczisan/latex_dress | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"region:us"
] | null | 2024-04-29T19:40:22+00:00 |
null | null | {"license": "mit"} | samsri01/sentianalysis-2024 | null | [
"license:mit",
"region:us"
] | null | 2024-04-29T19:40:56+00:00 |
|
text-generation | null | # Qwen1.5-110B-Chat-GGUF
- Original model: [Qwen1.5-110B-Chat](https://huggingface.co/Qwen/Qwen1.5-110B-Chat)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Qwen1.5-110B-Chat](https://huggingface.co/Qwen/Qwen1.5-110B-Chat).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
* [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
* [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
* [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
* [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
* [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.
<!-- README_GGUF.md-about-gguf end -->
<!-- compatibility_gguf start -->
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: LiteLLMs/Qwen1.5-110B-Chat-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download LiteLLMs/Qwen1.5-110B-Chat-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download LiteLLMs/Qwen1.5-110B-Chat-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install huggingface_hub[hf_transfer]
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Qwen1.5-110B-Chat-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<PROMPT>", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Qwen1.5-110B-Chat
# Qwen1.5-110B-Chat
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 9 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B, 72B, and 110B dense models, and an MoE model of 14B with 2.7B activated;
* Significant performance improvement in human preference for chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
<br>
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B and 110B) and the mixture of SWA and full attention.
## Training details
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen1.5-110B-Chat",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-110B-Chat")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Tips
* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in `generation_config.json`.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
<!-- original-model-card end -->
| {"language": ["en"], "license": "other", "tags": ["chat", "GGUF"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE", "pipeline_tag": "text-generation", "quantized_by": "andrijdavid"} | LiteLLMs/Qwen1.5-110B-Chat-GGUF | null | [
"chat",
"GGUF",
"text-generation",
"en",
"license:other",
"region:us"
] | null | 2024-04-29T19:41:24+00:00 |
text-generation | transformers | {} | genai-proj/gpt2 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:45:02+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v2-ng
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- 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
- lr_scheduler_warmup_ratio: 0.3
- training_steps: 10000
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-large-v2", "model-index": [{"name": "whisper-large-v2-ng", "results": []}]} | sin2piusc/whisper-large-v2-ng | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-large-v2",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T19:45:17+00:00 |
null | null | {} | bhanukedhar/classifier_results | null | [
"region:us"
] | null | 2024-04-29T19:45:17+00:00 |
|
text2text-generation | transformers | {} | gouravsinha/point_to_descriptionv3.0 | null | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:46:39+00:00 |
|
null | null | {"license": "mit"} | shayanathif/bartHydro | null | [
"license:mit",
"region:us"
] | null | 2024-04-29T19:49:15+00:00 |
|
text-to-image | diffusers |
# 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 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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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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## Model Card Contact
[More Information Needed] | {"library_name": "diffusers"} | Niggendar/js2prony_v10 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-29T19:49:45+00:00 |
null | null | {} | cstr/phi-3-orpo-v8_16-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-04-29T19:51:24+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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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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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | nalkhou/Hermes-2-Pro-Mistral-7B_DPO-92 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-29T19:54:00+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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[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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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | namratanwani/fiqa-2018-target-extraction | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:54:28+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** Cognitus-Stuti
- **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"} | Cognitus-Stuti/llama3-8b-oig-unsloth-merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:54:48+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- 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.
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/final64 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:55:03+00:00 |
sentence-similarity | sentence-transformers |
# SentenceTransformer based on distilbert/distilroberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/distilroberta-base-nli-matryoshka-v3")
# Run inference
sentences = [
'A man shoots a man.',
'A man is shooting off guns.',
'A man is erasing a chalk board.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8481 |
| **spearman_cosine** | **0.8519** |
| pearson_manhattan | 0.8393 |
| spearman_manhattan | 0.8385 |
| pearson_euclidean | 0.841 |
| spearman_euclidean | 0.8402 |
| pearson_dot | 0.7784 |
| spearman_dot | 0.778 |
| pearson_max | 0.8481 |
| spearman_max | 0.8519 |
#### Semantic Similarity
* Dataset: `sts-dev-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8481 |
| **spearman_cosine** | **0.8524** |
| pearson_manhattan | 0.8386 |
| spearman_manhattan | 0.8377 |
| pearson_euclidean | 0.8402 |
| spearman_euclidean | 0.8395 |
| pearson_dot | 0.7712 |
| spearman_dot | 0.7713 |
| pearson_max | 0.8481 |
| spearman_max | 0.8524 |
#### Semantic Similarity
* Dataset: `sts-dev-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8421 |
| **spearman_cosine** | **0.8488** |
| pearson_manhattan | 0.8313 |
| spearman_manhattan | 0.8316 |
| pearson_euclidean | 0.8333 |
| spearman_euclidean | 0.8335 |
| pearson_dot | 0.7446 |
| spearman_dot | 0.745 |
| pearson_max | 0.8421 |
| spearman_max | 0.8488 |
#### Semantic Similarity
* Dataset: `sts-dev-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8347 |
| **spearman_cosine** | **0.8445** |
| pearson_manhattan | 0.8241 |
| spearman_manhattan | 0.8248 |
| pearson_euclidean | 0.8254 |
| spearman_euclidean | 0.8262 |
| pearson_dot | 0.7084 |
| spearman_dot | 0.7093 |
| pearson_max | 0.8347 |
| spearman_max | 0.8445 |
#### Semantic Similarity
* Dataset: `sts-dev-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8201 |
| **spearman_cosine** | **0.8352** |
| pearson_manhattan | 0.8032 |
| spearman_manhattan | 0.8047 |
| pearson_euclidean | 0.806 |
| spearman_euclidean | 0.8072 |
| pearson_dot | 0.636 |
| spearman_dot | 0.6389 |
| pearson_max | 0.8201 |
| spearman_max | 0.8352 |
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8262 |
| **spearman_cosine** | **0.8298** |
| pearson_manhattan | 0.8104 |
| spearman_manhattan | 0.8033 |
| pearson_euclidean | 0.8114 |
| spearman_euclidean | 0.8048 |
| pearson_dot | 0.7351 |
| spearman_dot | 0.7223 |
| pearson_max | 0.8262 |
| spearman_max | 0.8298 |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8265 |
| **spearman_cosine** | **0.8303** |
| pearson_manhattan | 0.8092 |
| spearman_manhattan | 0.8022 |
| pearson_euclidean | 0.81 |
| spearman_euclidean | 0.8034 |
| pearson_dot | 0.7239 |
| spearman_dot | 0.7141 |
| pearson_max | 0.8265 |
| spearman_max | 0.8303 |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8248 |
| **spearman_cosine** | **0.8305** |
| pearson_manhattan | 0.8012 |
| spearman_manhattan | 0.7951 |
| pearson_euclidean | 0.8028 |
| spearman_euclidean | 0.7974 |
| pearson_dot | 0.7011 |
| spearman_dot | 0.6946 |
| pearson_max | 0.8248 |
| spearman_max | 0.8305 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8206 |
| **spearman_cosine** | **0.8284** |
| pearson_manhattan | 0.7932 |
| spearman_manhattan | 0.7878 |
| pearson_euclidean | 0.7947 |
| spearman_euclidean | 0.7891 |
| pearson_dot | 0.6618 |
| spearman_dot | 0.6586 |
| pearson_max | 0.8206 |
| spearman_max | 0.8284 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8119 |
| **spearman_cosine** | **0.8241** |
| pearson_manhattan | 0.7761 |
| spearman_manhattan | 0.7738 |
| pearson_euclidean | 0.7777 |
| spearman_euclidean | 0.7746 |
| pearson_dot | 0.5934 |
| spearman_dot | 0.5884 |
| pearson_max | 0.8119 |
| spearman_max | 0.8241 |
<!--
## Bias, Risks and Limitations
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [65dd388](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/65dd38867b600f42241d2066ba1a35fbd097fcfe)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.0 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.99 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------|:------------------------------------------------------|:------------------|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: False
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: None
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0229 | 100 | 19.9245 | 11.3900 | 0.7772 | 0.7998 | 0.8049 | 0.7902 | 0.7919 | - | - | - | - | - |
| 0.0459 | 200 | 10.6055 | 11.1510 | 0.7809 | 0.7996 | 0.8055 | 0.7954 | 0.7954 | - | - | - | - | - |
| 0.0688 | 300 | 9.6389 | 11.1229 | 0.7836 | 0.8029 | 0.8114 | 0.7923 | 0.8083 | - | - | - | - | - |
| 0.0918 | 400 | 8.6917 | 11.0299 | 0.7976 | 0.8117 | 0.8142 | 0.8002 | 0.8087 | - | - | - | - | - |
| 0.1147 | 500 | 8.3064 | 11.3586 | 0.7895 | 0.8058 | 0.8120 | 0.7978 | 0.8065 | - | - | - | - | - |
| 0.1376 | 600 | 7.8026 | 11.5047 | 0.7876 | 0.8015 | 0.8065 | 0.7934 | 0.8016 | - | - | - | - | - |
| 0.1606 | 700 | 7.9978 | 11.5823 | 0.7944 | 0.8067 | 0.8072 | 0.7994 | 0.8045 | - | - | - | - | - |
| 0.1835 | 800 | 6.9249 | 11.5862 | 0.7945 | 0.8054 | 0.8085 | 0.8012 | 0.8033 | - | - | - | - | - |
| 0.2065 | 900 | 7.1059 | 11.2365 | 0.7895 | 0.8035 | 0.8072 | 0.7956 | 0.8031 | - | - | - | - | - |
| 0.2294 | 1000 | 6.5483 | 11.3770 | 0.7853 | 0.7994 | 0.8039 | 0.7894 | 0.8024 | - | - | - | - | - |
| 0.2524 | 1100 | 6.6684 | 11.5038 | 0.7968 | 0.8087 | 0.8115 | 0.8002 | 0.8065 | - | - | - | - | - |
| 0.2753 | 1200 | 6.4661 | 11.4057 | 0.7980 | 0.8082 | 0.8103 | 0.8057 | 0.8070 | - | - | - | - | - |
| 0.2982 | 1300 | 6.501 | 11.2521 | 0.7974 | 0.8100 | 0.8111 | 0.8025 | 0.8079 | - | - | - | - | - |
| 0.3212 | 1400 | 6.0769 | 11.1458 | 0.7971 | 0.8103 | 0.8124 | 0.7982 | 0.8082 | - | - | - | - | - |
| 0.3441 | 1500 | 6.1919 | 11.3180 | 0.8039 | 0.8129 | 0.8144 | 0.8094 | 0.8098 | - | - | - | - | - |
| 0.3671 | 1600 | 5.8213 | 11.6196 | 0.7924 | 0.8072 | 0.8090 | 0.8003 | 0.8012 | - | - | - | - | - |
| 0.3900 | 1700 | 5.534 | 11.0700 | 0.7979 | 0.8104 | 0.8132 | 0.8028 | 0.8101 | - | - | - | - | - |
| 0.4129 | 1800 | 5.7536 | 11.0916 | 0.7934 | 0.8087 | 0.8149 | 0.8008 | 0.8085 | - | - | - | - | - |
| 0.4359 | 1900 | 5.3778 | 11.2658 | 0.7942 | 0.8084 | 0.8104 | 0.7980 | 0.8049 | - | - | - | - | - |
| 0.4588 | 2000 | 5.4925 | 11.4851 | 0.7932 | 0.8062 | 0.8086 | 0.7932 | 0.8057 | - | - | - | - | - |
| 0.4818 | 2100 | 5.3125 | 11.4833 | 0.7987 | 0.8119 | 0.8154 | 0.8012 | 0.8124 | - | - | - | - | - |
| 0.5047 | 2200 | 5.1914 | 11.2848 | 0.7784 | 0.7971 | 0.8037 | 0.7911 | 0.8004 | - | - | - | - | - |
| 0.5276 | 2300 | 5.2921 | 11.5364 | 0.7698 | 0.7910 | 0.7974 | 0.7839 | 0.7900 | - | - | - | - | - |
| 0.5506 | 2400 | 5.288 | 11.3944 | 0.7873 | 0.8011 | 0.8051 | 0.7877 | 0.8003 | - | - | - | - | - |
| 0.5735 | 2500 | 5.3697 | 11.4532 | 0.7949 | 0.8077 | 0.8111 | 0.7955 | 0.8069 | - | - | - | - | - |
| 0.5965 | 2600 | 5.1521 | 11.2788 | 0.7973 | 0.8095 | 0.8130 | 0.7940 | 0.8088 | - | - | - | - | - |
| 0.6194 | 2700 | 5.2316 | 11.2472 | 0.7948 | 0.8077 | 0.8102 | 0.7939 | 0.8053 | - | - | - | - | - |
| 0.6423 | 2800 | 5.2599 | 11.4171 | 0.7882 | 0.8029 | 0.8065 | 0.7888 | 0.8019 | - | - | - | - | - |
| 0.6653 | 2900 | 5.4052 | 11.4026 | 0.7871 | 0.8005 | 0.8021 | 0.7833 | 0.7985 | - | - | - | - | - |
| 0.6882 | 3000 | 5.3474 | 11.2084 | 0.7895 | 0.8047 | 0.8079 | 0.7928 | 0.8050 | - | - | - | - | - |
| 0.7112 | 3100 | 5.0336 | 11.3999 | 0.8023 | 0.8150 | 0.8182 | 0.8024 | 0.8168 | - | - | - | - | - |
| 0.7341 | 3200 | 5.2496 | 11.2307 | 0.8015 | 0.8137 | 0.8167 | 0.8000 | 0.8140 | - | - | - | - | - |
| 0.7571 | 3300 | 3.8712 | 10.9468 | 0.8396 | 0.8440 | 0.8471 | 0.8284 | 0.8479 | - | - | - | - | - |
| 0.7800 | 3400 | 2.7068 | 10.9292 | 0.8414 | 0.8453 | 0.8489 | 0.8305 | 0.8497 | - | - | - | - | - |
| 0.8029 | 3500 | 2.3418 | 10.8626 | 0.8427 | 0.8467 | 0.8504 | 0.8322 | 0.8504 | - | - | - | - | - |
| 0.8259 | 3600 | 2.2419 | 10.9065 | 0.8421 | 0.8467 | 0.8504 | 0.8320 | 0.8502 | - | - | - | - | - |
| 0.8488 | 3700 | 2.125 | 10.9517 | 0.8424 | 0.8472 | 0.8509 | 0.8324 | 0.8510 | - | - | - | - | - |
| 0.8718 | 3800 | 1.9942 | 11.0142 | 0.8438 | 0.8482 | 0.8519 | 0.8337 | 0.8517 | - | - | - | - | - |
| 0.8947 | 3900 | 2.031 | 10.9662 | 0.8433 | 0.8480 | 0.8519 | 0.8340 | 0.8515 | - | - | - | - | - |
| 0.9176 | 4000 | 1.9734 | 11.0054 | 0.8452 | 0.8495 | 0.8531 | 0.8354 | 0.8528 | - | - | - | - | - |
| 0.9406 | 4100 | 1.9468 | 11.0183 | 0.8447 | 0.8490 | 0.8526 | 0.8348 | 0.8522 | - | - | - | - | - |
| 0.9635 | 4200 | 1.9008 | 11.0154 | 0.8445 | 0.8485 | 0.8521 | 0.8352 | 0.8517 | - | - | - | - | - |
| 0.9865 | 4300 | 1.8511 | 10.9966 | 0.8445 | 0.8488 | 0.8524 | 0.8352 | 0.8519 | - | - | - | - | - |
| 1.0 | 4359 | - | - | - | - | - | - | - | 0.8284 | 0.8305 | 0.8303 | 0.8241 | 0.8298 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.346 kWh
- **Carbon Emitted**: 0.134 kg of CO2
- **Hours Used**: 1.296 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## 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.*
--> | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss"], "metrics": ["pearson_cosine", "spearman_cosine", "pearson_manhattan", "spearman_manhattan", "pearson_euclidean", "spearman_euclidean", "pearson_dot", "spearman_dot", "pearson_max", "spearman_max"], "base_model": "distilbert/distilroberta-base", "widget": [{"source_sentence": "The gate is yellow.", "sentences": ["A yellow dog is playing in the snow.", "A turtle walks over the ground.", "Three men are on stage playing guitars."]}, {"source_sentence": "A woman is reading.", "sentences": ["A woman is writing something.", "A tiger walks around aimlessly.", "Gunmen 'kill 10 tourists' in Kashmir"]}, {"source_sentence": "A man jumping rope", "sentences": ["A man is climbing a rope.", "Bombings kill 19 people in Iraq", "Kittens are eating from dishes."]}, {"source_sentence": "A baby is laughing.", "sentences": ["A baby is crawling happily.", "Kittens are eating from dishes.", "SFG meeting reviews situation in Mali"]}, {"source_sentence": "A man shoots a man.", "sentences": ["A man is shooting off guns.", "A man is erasing a chalk board.", "A girl is riding a bicycle."]}], "pipeline_tag": "sentence-similarity", "co2_eq_emissions": {"emissions": 134.46101750442273, "energy_consumed": 0.34592314293320514, "source": "codecarbon", "training_type": "fine-tuning", "on_cloud": false, "cpu_model": "13th Gen Intel(R) Core(TM) i7-13700K", "ram_total_size": 31.777088165283203, "hours_used": 1.296, "hardware_used": "1 x NVIDIA GeForce RTX 3090"}, "model-index": [{"name": "SentenceTransformer based on distilbert/distilroberta-base", "results": [{"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts dev 768", "type": "sts-dev-768"}, "metrics": [{"type": "pearson_cosine", "value": 0.8481251400932781, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.851870210632031, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8393267568646925, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8384807951588668, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8409860761844343, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8402437232149903, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.778375740024104, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.7779671330832745, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8481251400932781, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.851870210632031, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts dev 512", "type": "sts-dev-512"}, "metrics": [{"type": "pearson_cosine", "value": 0.8481027005283404, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8523762836460506, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8386304289845581, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8377488866945335, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8402060724091132, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8394674780683281, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7711669414347555, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.7713442697629354, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8481027005283404, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8523762836460506, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts dev 256", "type": "sts-dev-256"}, "metrics": [{"type": "pearson_cosine", "value": 0.842129976172463, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8488334736505414, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8313278330554295, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8315716535622544, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8333448222091957, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8335338271135746, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7445817504026263, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.7450058498333884, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.842129976172463, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8488334736505414, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts dev 128", "type": "sts-dev-128"}, "metrics": [{"type": "pearson_cosine", "value": 0.8346971467711455, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8445473333837453, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8240728025222037, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8248062249521573, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8254381823447683, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8261820268848477, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7083986436033697, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.7093343189476312, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8346971467711455, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8445473333837453, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts dev 64", "type": "sts-dev-64"}, "metrics": [{"type": "pearson_cosine", "value": 0.8201235619233855, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8352180907883887, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8032422421113089, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8047180797117756, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8059536263441476, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8072309964597537, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.6360301824635421, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.6388601952951507, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8201235619233855, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8352180907883887, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts test 768", "type": "sts-test-768"}, "metrics": [{"type": "pearson_cosine", "value": 0.8262197279185375, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8297611922199533, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8103738584802076, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8032653500693283, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8113711464219397, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8047844488402207, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7351063083543349, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.7222898603318773, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8262197279185375, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8297611922199533, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts test 512", "type": "sts-test-512"}, "metrics": [{"type": "pearson_cosine", "value": 0.8265289700873992, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8303420710627304, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8092042518460232, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8021561300791633, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8099517575676378, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8034311442407586, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7239156858292818, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.7141021600172974, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8265289700873992, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8303420710627304, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts test 256", "type": "sts-test-256"}, "metrics": [{"type": "pearson_cosine", "value": 0.8247713863827557, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8304669772286988, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8012313573943666, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.7951476656544464, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8028104839960224, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.7974260171623634, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7011271518071694, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.6946104528279369, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8247713863827557, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8304669772286988, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts test 128", "type": "sts-test-128"}, "metrics": [{"type": "pearson_cosine", "value": 0.8205553018873636, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8283987535951244, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.7931877193499666, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.7878356187942884, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.7946730313407452, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.7891423743206649, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.6617612604436709, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.658567255717814, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8205553018873636, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8283987535951244, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts test 64", "type": "sts-test-64"}, "metrics": [{"type": "pearson_cosine", "value": 0.8118818737650724, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8241392189948019, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.7761319753952881, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.7738169467058665, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.7777045912119006, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.7745630850628562, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.5934162536230442, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.5884207612393454, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8118818737650724, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8241392189948019, "name": "Spearman Max"}]}]}]} | tomaarsen/distilroberta-base-nli-matryoshka-v3 | null | [
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"en",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:distilbert/distilroberta-base",
"model-index",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:55:06+00:00 |
null | null | {} | ishaqpaktin/computerGraphics_proj | null | [
"region:us"
] | null | 2024-04-29T19:55:14+00:00 |
|
null | null | {} | dex777/test | null | [
"region:us"
] | null | 2024-04-29T19:55:27+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]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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<!-- 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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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | cilantro9246/71sgmw4 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T19:57:18+00:00 |
null | null | {"license": "apache-2.0"} | mmicali/LLM-Max | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T19:57:24+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. -->
# output_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2884
- Wer: 0.4752
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 2.8783 | 2.4752 | 500 | 2.7618 | 0.9999 |
| 1.4069 | 4.9505 | 1000 | 1.0853 | 0.6936 |
| 0.6264 | 7.4257 | 1500 | 0.9955 | 0.6014 |
| 0.3864 | 9.9010 | 2000 | 1.0460 | 0.5675 |
| 0.2714 | 12.3762 | 2500 | 0.9830 | 0.5422 |
| 0.2099 | 14.8515 | 3000 | 1.0333 | 0.5296 |
| 0.1615 | 17.3267 | 3500 | 1.1575 | 0.5203 |
| 0.1248 | 19.8020 | 4000 | 1.1311 | 0.4956 |
| 0.1032 | 22.2772 | 4500 | 1.3206 | 0.4953 |
| 0.0834 | 24.7525 | 5000 | 1.2094 | 0.4855 |
| 0.0655 | 27.2277 | 5500 | 1.2966 | 0.4763 |
| 0.052 | 29.7030 | 6000 | 1.2884 | 0.4752 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "output_model", "results": []}]} | Noorrabie/output_model | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:58:00+00:00 |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0858
- Accuracy: 0.9831
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.634 | 0.9994 | 399 | 0.4822 | 0.9709 |
| 0.2705 | 1.9987 | 798 | 0.1673 | 0.9772 |
| 0.1614 | 2.9981 | 1197 | 0.1051 | 0.9821 |
| 0.1297 | 4.0 | 1597 | 0.0931 | 0.9826 |
| 0.1273 | 4.9969 | 1995 | 0.0858 | 0.9831 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "superb", "type": "superb", "config": "ks", "split": "validation", "args": "ks"}, "metrics": [{"type": "accuracy", "value": 0.9830832597822889, "name": "Accuracy"}]}]}]} | KI22/wav2vec2-base-finetuned-ks | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:58:35+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Cognitus-Stuti
- **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"} | Cognitus-Stuti/llama3-8b-oig-unsloth | 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 | 2024-04-29T19:59:25+00:00 |
null | transformers | # Llama-3-8B-Web-GGUF
- Original model: [Llama-3-8B-Web](https://huggingface.co/McGill-NLP/Llama-3-8B-Web)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Llama-3-8B-Web](https://huggingface.co/McGill-NLP/Llama-3-8B-Web).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
* [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
* [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
* [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
* [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
* [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.
<!-- README_GGUF.md-about-gguf end -->
<!-- compatibility_gguf start -->
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: LiteLLMs/Llama-3-8B-Web-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download LiteLLMs/Llama-3-8B-Web-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download LiteLLMs/Llama-3-8B-Web-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install huggingface_hub[hf_transfer]
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Llama-3-8B-Web-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<PROMPT>", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Llama-3-8B-Web
<div align="center">
<h1>Llama-3-8B-Web</h1>
<table>
<tr>
<td>
<a href="https://github.com/McGill-NLP/webllama">💻 GitHub</a>
</td>
<td>
<a href="https://webllama.github.io">🏠 Homepage</a>
</td>
<td>
<a href="https://huggingface.co/McGill-NLP/Llama-3-8B-Web">🤗 Llama-3-8B-Web</a>
</td>
</tr>
</table>
<img src="assets/WebLlamaLogo.png" style="width: 400px;" />
*By using this model, you are accepting the terms of the [Meta Llama 3 Community License Agreement](https://llama.meta.com/llama3/license/).*
</div>
| `WebLlama` helps you build powerful agents, powered by Meta Llama 3, for browsing the web on your behalf | Our first model, [`Llama-3-8B-Web`](https://huggingface.co/McGill-NLP/Llama-3-8B-Web), surpasses GPT-4V (`*`zero-shot) by 18% on [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) |
| :: | : |
| Modeling | We are build on top of cutting edge libraries for training Llama agents on web navigation tasks. We will provide training scripts, optimized configs, and instructions for training cutting-edge Llamas. |
| Evaluation | Benchmarks for testing Llama models on real-world web browsing. This include *human-centric* browsing through dialogue ([`WebLINX`](https://mcgill-nlp.github.io/weblinx/)), and we will soon add more benchmarks for automatic web navigation (e.g. Mind2Web). |
| Data | Our first model is finetuned on over 24K instances of web interactions, including `click`, `textinput`, `submit`, and dialogue acts. We want to continuously curate, compile and release datasets for training better agents. |
| Deployment | We want to make it easy to integrate Llama models with existing deployment platforms, including Playwright, Selenium, and BrowserGym. We are currently focusing on making this a reality. |
## Evaluation
We believe short demo videos showing how well an agent performs is NOT enough to judge an agent. Simply put, **we do not know if we have a good agent if we do not have good benchmarks.** We need to systematically evaluate agents on wide range of tasks, spanning from simple instruction-following web navigation to complex dialogue-guided browsing.
<img src="assets/WebLINXTestSplits.png" style="width: 100%; max-width:800px"/>
This is why we chose [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) as our first benchmark. In addition to the training split, the benchmark has 4 real-world splits, with the goal of testing multiple dimensions of generalization: new websites, new domains, unseen geographic locations, and scenarios where the *user cannot see the screen and relies on dialogue*. It also covers 150 websites, including booking, shopping, writing, knowledge lookup, and even complex tasks like manipulating spreadsheets.
## Data
Although the 24K training examples from [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) provide a good starting point for training a capable agent, we believe that more data is needed to train agents that can generalize to a wide range of web navigation tasks. Although it has been trained and evaluated on 150 websites, there are millions of websites that has never been seen by the model, with new ones being created every day.
**This motivates us to continuously curate, compile and release datasets for training better agents.** As an immediate next step, we will be incorporating `Mind2Web`'s training data into the equation, which also covers over 100 websites.
## Deployment
We are working hard to make it easy for you to deploy Llama web agents to the web. We want to integrate `WebLlama` with existing deployment platforms, including Microsoft's Playwright, ServiceNow Research's BrowserGym, and other partners.
## Code
The code for finetuning the model and evaluating it on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) benchmark is available now. You can find the detailed instructions in [modeling](https://github.com/McGill-NLP/webllama/tree/main/modeling).
## Citation
If you use `WebLlama` in your research, please cite the following paper (upon which the data, training and evaluation are originally based on):
```
@misc{lù2024weblinx,
title={WebLINX: Real-World Website Navigation with Multi-Turn Dialogue},
author={Xing Han Lù and Zdeněk Kasner and Siva Reddy},
year={2024},
eprint={2402.05930},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!-- original-model-card end -->
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["agents", "agent", "llm", "llama", "GGUF"], "datasets": ["McGill-NLP/WebLINX"], "quantized_by": "andrijdavid"} | LiteLLMs/Llama-3-8B-Web-GGUF | null | [
"transformers",
"gguf",
"agents",
"agent",
"llm",
"llama",
"GGUF",
"en",
"dataset:McGill-NLP/WebLINX",
"arxiv:2402.05930",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:59:51+00:00 |
null | null | {"license": "apache-2.0"} | Ralakus/Mixtral-Test-Finetune | null | [
"safetensors",
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T20:00:30+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": []} | tomaszki/stablelm-48 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T20:00:33+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. -->
# mistral_on_scigen_v2
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 30
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral_on_scigen_v2", "results": []}]} | moetezsa/mistral_on_scigen_v2 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T20:03:17+00:00 |
null | null | {} | shallow6414/b24vhv4 | null | [
"region:us"
] | null | 2024-04-29T20:03:40+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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. -->
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### 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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## 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]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/rf0yi8l | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T20:05:14+00:00 |
null | null | {} | ohmpatel46/Contasnetnew | null | [
"region:us"
] | null | 2024-04-29T20:05:26+00:00 |
|
text-generation | transformers | # 🇰🇷 SmartLlama-3-Ko-8B
<a href="https://ibb.co/C8Tcw1F"><img src="https://i.ibb.co/QQ1gJbG/smartllama3.png" alt="smartllama3" border="0"></a><br />
SmartLlama-3-Ko-8B is a sophisticated AI model that integrates the capabilities of several advanced language models. This merged model is designed to excel in a variety of tasks ranging from technical problem-solving to multilingual communication.
## 📕 Merge Details
### Component Models and Contributions
### 1. NousResearch/Meta-Llama-3-8B and Meta-Llama-3-8B-Instruct
- **General Language Understanding and Instruction-Following**: These base models provide a robust foundation in general language understanding. The instruct version is optimized to follow detailed user instructions, enhancing the model's utility in task-oriented dialogues.
### 2. cognitivecomputations/dolphin-2.9-llama3-8b
- **Complex Problem-Solving and Depth of Understanding**: Enhances the model's capabilities in technical and scientific domains, improving its performance in complex problem-solving and areas requiring intricate understanding.
### 3. abacusai/Llama-3-Smaug-8B
- **Multi-Turn Conversational Abilities**: Improves performance in real-world multi-turn conversations, crucial for applications in customer service and interactive learning.A multi-turn conversation refers to a dialogue that consists of several back-and-forth exchanges between participants. Unlike a single-turn interaction, where the conversation might end after one question and one response, multi-turn conversations require ongoing engagement from both sides. In such conversations, the context from previous messages is often crucial in shaping the response of each participant, making it necessary for them to remember or keep track of what was said earlier.For AI systems like chatbots or virtual assistants, the ability to handle multi-turn conversations is crucial. It allows the AI to engage more naturally and effectively with users, simulating human-like interactions. This capability is particularly important in customer service, where understanding the history of a customer’s issue can lead to more accurate and helpful responses, or in scenarios like therapy or tutoring, where the depth of the conversation can significantly impact the effectiveness of the interaction.
### 4. Locutusque/Llama-3-Orca-1.0-8B
- **Specialization in Math, Coding, and Writing**: Enhances the model's ability to handle mathematical equations, generate computer code, and produce high-quality written content.
### 5. beomi/Llama-3-Open-Ko-8B-Instruct-preview
- **Enhanced Korean Language Capabilities**: Specifically trained to understand and generate Korean, valuable for bilingual or multilingual applications targeting Korean-speaking audiences.
### Merging Technique: DARE TIES
- **Balanced Integration**: The DARE TIES method ensures that each component model contributes its strengths in a balanced manner, maintaining a high level of performance across all integrated capabilities.
### Overall Capabilities
SmartLlama-3-Ko-8B is highly capable and versatile, suitable for:
- **Technical and Academic Applications**: Enhanced capabilities in math, coding, and technical writing.
- **Customer Service and Interactive Applications**: Advanced conversational skills and sustained interaction handling.
- **Multilingual Communication**: Specialized training in Korean enhances its utility in global or region-specific settings.
This comprehensive capability makes SmartLlama-3-Ko-8B not only a powerful tool for general-purpose AI tasks but also a specialized resource for industries and applications demanding high levels of technical and linguistic precision.
## 🖋️ Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) as a base.
## 🎭 Models Merged
The following models were included in the merge:
* [beomi/Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview)
* [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b)
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
* [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B)
* [Locutusque/Llama-3-Orca-1.0-8B](https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B)
## 🗞️ Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NousResearch/Meta-Llama-3-8B
# Base model providing a general foundation without specific parameters
- model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
density: 0.58
weight: 0.25
- model: cognitivecomputations/dolphin-2.9-llama3-8b
parameters:
density: 0.52
weight: 0.15
- model: Locutusque/Llama-3-Orca-1.0-8B
parameters:
density: 0.52
weight: 0.15
- model: abacusai/Llama-3-Smaug-8B
parameters:
density: 0.52
weight: 0.15
- model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
parameters:
density: 0.53
weight: 0.2
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: bfloat16
```
### 🎊 Test Result
**Korean Multi Turn Conversation**
<a href="https://ibb.co/TKPGx9G"><img src="https://i.ibb.co/0BYLRHL/Screenshot-2024-04-30-at-2-42-18-PM.png" alt="Screenshot-2024-04-30-at-2-42-18-PM" border="0"></a>
<a href="https://ibb.co/v40tkNj"><img src="https://i.ibb.co/hF3qVGm/Screenshot-2024-04-30-at-8-26-57-AM.png" alt="Screenshot-2024-04-30-at-8-26-57-AM" border="0"></a>
**Programming**
<a href="https://ibb.co/6tZLqwx"><img src="https://i.ibb.co/n10tKmv/Screenshot-2024-04-30-at-8-30-35-AM.png" alt="Screenshot-2024-04-30-at-8-30-35-AM" border="0"></a>
**Physics & Math**
<a href="https://ibb.co/jDhVNk0"><img src="https://i.ibb.co/jDhVNk0/Screenshot-2024-04-30-at-1-06-16-PM.png" alt="Screenshot-2024-04-30-at-1-06-16-PM" border="0"></a>
<a href="https://ibb.co/KKgN4j5"><img src="https://i.ibb.co/KKgN4j5/Screenshot-2024-04-30-at-1-06-31-PM.png" alt="Screenshot-2024-04-30-at-1-06-31-PM" border="0"></a>
<a href="https://ibb.co/ZzKHP5j"><img src="https://i.ibb.co/ZzKHP5j/Screenshot-2024-04-30-at-1-06-47-PM.png" alt="Screenshot-2024-04-30-at-1-06-47-PM" border="0"></a>
| {"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "llama"], "base_model": ["beomi/Llama-3-Open-Ko-8B-Instruct-preview", "cognitivecomputations/dolphin-2.9-llama3-8b", "NousResearch/Meta-Llama-3-8B-Instruct", "NousResearch/Meta-Llama-3-8B", "abacusai/Llama-3-Smaug-8B", "Locutusque/Llama-3-Orca-1.0-8B"]} | asiansoul/SmartLlama-3-Ko-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:abacusai/Llama-3-Smaug-8B",
"base_model:Locutusque/Llama-3-Orca-1.0-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T20:05:32+00:00 |
null | null | {} | NiWang2024/finetuning-sentiment-model-3000-samples | null | [
"region:us"
] | null | 2024-04-29T20:05:32+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/q4og37a | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T20:06:55+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | nalkhou/Hermes-2-Pro-Mistral-7B_DPO-156 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-29T20:07:03+00:00 |
null | transformers | # 🇰🇷 SmartLlama-3-Ko-8B
<a href="https://ibb.co/C8Tcw1F"><img src="https://i.ibb.co/QQ1gJbG/smartllama3.png" alt="smartllama3" border="0"></a><br />
SmartLlama-3-Ko-8B is a sophisticated AI model that integrates the capabilities of several advanced language models. This merged model is designed to excel in a variety of tasks ranging from technical problem-solving to multilingual communication.
## 📕 Merge Details
### Component Models and Contributions
### 1. NousResearch/Meta-Llama-3-8B and Meta-Llama-3-8B-Instruct
- **General Language Understanding and Instruction-Following**: These base models provide a robust foundation in general language understanding. The instruct version is optimized to follow detailed user instructions, enhancing the model's utility in task-oriented dialogues.
### 2. cognitivecomputations/dolphin-2.9-llama3-8b
- **Complex Problem-Solving and Depth of Understanding**: Enhances the model's capabilities in technical and scientific domains, improving its performance in complex problem-solving and areas requiring intricate understanding.
### 3. abacusai/Llama-3-Smaug-8B
- **Multi-Turn Conversational Abilities**: Improves performance in real-world multi-turn conversations, crucial for applications in customer service and interactive learning.A multi-turn conversation refers to a dialogue that consists of several back-and-forth exchanges between participants. Unlike a single-turn interaction, where the conversation might end after one question and one response, multi-turn conversations require ongoing engagement from both sides. In such conversations, the context from previous messages is often crucial in shaping the response of each participant, making it necessary for them to remember or keep track of what was said earlier.For AI systems like chatbots or virtual assistants, the ability to handle multi-turn conversations is crucial. It allows the AI to engage more naturally and effectively with users, simulating human-like interactions. This capability is particularly important in customer service, where understanding the history of a customer’s issue can lead to more accurate and helpful responses, or in scenarios like therapy or tutoring, where the depth of the conversation can significantly impact the effectiveness of the interaction.
### 4. Locutusque/Llama-3-Orca-1.0-8B
- **Specialization in Math, Coding, and Writing**: Enhances the model's ability to handle mathematical equations, generate computer code, and produce high-quality written content.
### 5. beomi/Llama-3-Open-Ko-8B-Instruct-preview
- **Enhanced Korean Language Capabilities**: Specifically trained to understand and generate Korean, valuable for bilingual or multilingual applications targeting Korean-speaking audiences.
### Merging Technique: DARE TIES
- **Balanced Integration**: The DARE TIES method ensures that each component model contributes its strengths in a balanced manner, maintaining a high level of performance across all integrated capabilities.
### Overall Capabilities
SmartLlama-3-Ko-8B is highly capable and versatile, suitable for:
- **Technical and Academic Applications**: Enhanced capabilities in math, coding, and technical writing.
- **Customer Service and Interactive Applications**: Advanced conversational skills and sustained interaction handling.
- **Multilingual Communication**: Specialized training in Korean enhances its utility in global or region-specific settings.
This comprehensive capability makes SmartLlama-3-Ko-8B not only a powerful tool for general-purpose AI tasks but also a specialized resource for industries and applications demanding high levels of technical and linguistic precision.
## 💻 Ollama
```
ollama create smartllama-3-ko-8b -f ./Modelfile_Q5_K_M
```
[Modelfile_Q5_K_M]
```
FROM smartllama-3-ko-8b-Q5_K_M.gguf
TEMPLATE """
{{- if .System }}
system
<s>{{ .System }}</s>
{{- end }}
user
<s>Human:
{{ .Prompt }}</s>
assistant
<s>Assistant:
"""
SYSTEM """
친절한 챗봇으로서 상대방의 요청에 최대한 자세하고 친절하게 답하자. 모든 대답은 한국어(Korean)으로 대답해줘.
"""
PARAMETER temperature 0.7
PARAMETER num_predict 256
PARAMETER num_ctx 4096
PARAMETER stop "<s>"
PARAMETER stop "</s>"
```
## 🖋️ Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) as a base.
## 🎭 Models Merged
The following models were included in the merge:
* [beomi/Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview)
* [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b)
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
* [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B)
* [Locutusque/Llama-3-Orca-1.0-8B](https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B)
## 🗞️ Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NousResearch/Meta-Llama-3-8B
# Base model providing a general foundation without specific parameters
- model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
density: 0.58
weight: 0.25
- model: cognitivecomputations/dolphin-2.9-llama3-8b
parameters:
density: 0.52
weight: 0.15
- model: Locutusque/Llama-3-Orca-1.0-8B
parameters:
density: 0.52
weight: 0.15
- model: abacusai/Llama-3-Smaug-8B
parameters:
density: 0.52
weight: 0.15
- model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
parameters:
density: 0.53
weight: 0.2
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: bfloat16
```
### 🎊 Test Result
**Korean Multi Turn Conversation**
<a href="https://ibb.co/TKPGx9G"><img src="https://i.ibb.co/0BYLRHL/Screenshot-2024-04-30-at-2-42-18-PM.png" alt="Screenshot-2024-04-30-at-2-42-18-PM" border="0"></a>
<a href="https://ibb.co/v40tkNj"><img src="https://i.ibb.co/hF3qVGm/Screenshot-2024-04-30-at-8-26-57-AM.png" alt="Screenshot-2024-04-30-at-8-26-57-AM" border="0"></a>
**Programming**
<a href="https://ibb.co/6tZLqwx"><img src="https://i.ibb.co/n10tKmv/Screenshot-2024-04-30-at-8-30-35-AM.png" alt="Screenshot-2024-04-30-at-8-30-35-AM" border="0"></a>
**Physics & Math**
<a href="https://ibb.co/jDhVNk0"><img src="https://i.ibb.co/jDhVNk0/Screenshot-2024-04-30-at-1-06-16-PM.png" alt="Screenshot-2024-04-30-at-1-06-16-PM" border="0"></a>
<a href="https://ibb.co/KKgN4j5"><img src="https://i.ibb.co/KKgN4j5/Screenshot-2024-04-30-at-1-06-31-PM.png" alt="Screenshot-2024-04-30-at-1-06-31-PM" border="0"></a>
<a href="https://ibb.co/ZzKHP5j"><img src="https://i.ibb.co/ZzKHP5j/Screenshot-2024-04-30-at-1-06-47-PM.png" alt="Screenshot-2024-04-30-at-1-06-47-PM" border="0"></a> | {"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "llama"], "base_model": ["beomi/Llama-3-Open-Ko-8B-Instruct-preview", "cognitivecomputations/dolphin-2.9-llama3-8b", "NousResearch/Meta-Llama-3-8B-Instruct", "NousResearch/Meta-Llama-3-8B", "abacusai/Llama-3-Smaug-8B", "Locutusque/Llama-3-Orca-1.0-8B"]} | asiansoul/SmartLlama-3-Ko-8B-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:abacusai/Llama-3-Smaug-8B",
"base_model:Locutusque/Llama-3-Orca-1.0-8B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T20:07:29+00:00 |
text-classification | transformers | {} | harplyon/roberta_ethics_test_dropout | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T20:08:33+00:00 |
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