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null | null | {} | ahnyeonchan/Sheared-Llama-1.3b-sft-lora-deepspeed-240401-test | null | [
"region:us"
] | null | 2024-05-03T00:59:39+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CodeLlama-7b-Instruct-hf - bnb 8bits
- Model creator: https://huggingface.co/codellama/
- Original model: https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf/
Original model description:
---
language:
- code
pipeline_tag: text-generation
tags:
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
> [!NOTE]
> This is a non-official Code Llama repo. You can find the official Meta repository in the [Meta Llama organization](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf).
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) |
| 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) |
| 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) |
| 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers:
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [x] Infilling.
- [x] Instructions / chat.
- [ ] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Instruct version of the 7B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
| {} | RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2308.12950",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-03T00:59:48+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama-email-model-full - GGUF
- Model creator: https://huggingface.co/amichalski2/
- Original model: https://huggingface.co/amichalski2/tinyllama-email-model-full/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-email-model-full.Q2_K.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q2_K.gguf) | Q2_K | 0.4GB |
| [tinyllama-email-model-full.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [tinyllama-email-model-full.IQ3_S.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [tinyllama-email-model-full.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [tinyllama-email-model-full.IQ3_M.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [tinyllama-email-model-full.Q3_K.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q3_K.gguf) | Q3_K | 0.51GB |
| [tinyllama-email-model-full.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [tinyllama-email-model-full.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [tinyllama-email-model-full.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [tinyllama-email-model-full.Q4_0.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q4_0.gguf) | Q4_0 | 0.59GB |
| [tinyllama-email-model-full.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [tinyllama-email-model-full.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [tinyllama-email-model-full.Q4_K.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q4_K.gguf) | Q4_K | 0.62GB |
| [tinyllama-email-model-full.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [tinyllama-email-model-full.Q4_1.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q4_1.gguf) | Q4_1 | 0.65GB |
| [tinyllama-email-model-full.Q5_0.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q5_0.gguf) | Q5_0 | 0.71GB |
| [tinyllama-email-model-full.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [tinyllama-email-model-full.Q5_K.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q5_K.gguf) | Q5_K | 0.73GB |
| [tinyllama-email-model-full.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [tinyllama-email-model-full.Q5_1.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q5_1.gguf) | Q5_1 | 0.77GB |
| [tinyllama-email-model-full.Q6_K.gguf](https://huggingface.co/RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf/blob/main/tinyllama-email-model-full.Q6_K.gguf) | Q6_K | 0.84GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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
Use the code below to get started with the model.
[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
## 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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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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| {} | RichardErkhov/amichalski2_-_tinyllama-email-model-full-gguf | null | [
"gguf",
"arxiv:1910.09700",
"region:us"
] | null | 2024-05-03T01:00:07+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
mistral-7b-instruct-v0.2-bnb-4bit - bnb 4bits
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit/
Original model description:
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- unsloth
- transformers
- mistral
- mistral-7b
- mistral-instruct
- instruct
- bnb
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Mistral 7b here: https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
| {} | RichardErkhov/unsloth_-_mistral-7b-instruct-v0.2-bnb-4bit-4bits | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T01:00:13+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]
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### Direct Use
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[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]
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<!-- 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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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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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| {"library_name": "transformers", "tags": []} | shallow6414/07cldkx | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:01:03+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-new-v2-3e-05_SGD_938 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:01:23+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | cilantro9246/liidkst | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:01:27+00:00 |
text-generation | transformers | {} | TwinDoc/H100_stage1_checkpoint-53280 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:02:08+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Zlovoblachko/Transliteration_ver2_L1_sent_generator | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:02:12+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SOLAR-10.7B-Instruct-v1.0 - bnb 4bits
- Model creator: https://huggingface.co/upstage/
- Original model: https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/
Original model description:
---
datasets:
- c-s-ale/alpaca-gpt4-data
- Open-Orca/OpenOrca
- Intel/orca_dpo_pairs
- allenai/ultrafeedback_binarized_cleaned
language:
- en
license: cc-by-nc-4.0
base_model:
- upstage/SOLAR-10.7B-v1.0
---
<p align="left">
<a href="https://go.upstage.ai/solar-obt-hf-modelcardv1-instruct">
<img src="https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/resolve/main/solar-api-banner.png" width="100%"/>
</a>
<p>
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation.)**
# **Introduction**
We introduce SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We present a methodology for scaling LLMs called depth up-scaling (DUS) , which encompasses architectural modifications and continued pretraining. In other words, we integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table.
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements.
For full details of this model please read our [paper](https://arxiv.org/abs/2312.15166).
# **Instruction Fine-Tuning Strategy**
We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO) [1].
We used a mixture of the following datasets
- c-s-ale/alpaca-gpt4-data (SFT)
- Open-Orca/OpenOrca (SFT)
- in-house generated data utilizing Metamath [2] (SFT, DPO)
- Intel/orca_dpo_pairs (DPO)
- allenai/ultrafeedback_binarized_cleaned (DPO)
where we were careful of data contamination by not using GSM8K samples when generating data and filtering tasks when applicable via the following list.
```python
filtering_task_list = [
'task228_arc_answer_generation_easy',
'ai2_arc/ARC-Challenge:1.0.0',
'ai2_arc/ARC-Easy:1.0.0',
'task229_arc_answer_generation_hard',
'hellaswag:1.1.0',
'task1389_hellaswag_completion',
'cot_gsm8k',
'cot_gsm8k_ii',
'drop:2.0.0',
'winogrande:1.1.0'
]
```
Using the datasets mentioned above, we applied SFT and iterative DPO training, a proprietary alignment strategy, to maximize the performance of our resulting model.
[1] Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C.D. and Finn, C., 2023. Direct preference optimization: Your language model is secretly a reward model. NeurIPS.
[2] Yu, L., Jiang, W., Shi, H., Yu, J., Liu, Z., Zhang, Y., Kwok, J.T., Li, Z., Weller, A. and Liu, W., 2023. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284.
# **Data Contamination Test Results**
Recently, there have been contamination issues in some models on the LLM leaderboard.
We note that we made every effort to exclude any benchmark-related datasets from training.
We also ensured the integrity of our model by conducting a data contamination test [3] that is also used by the HuggingFace team [4, 5].
Our results, with `result < 0.1, %:` being well below 0.9, indicate that our model is free from contamination.
*The data contamination test results of HellaSwag and Winograde will be added once [3] supports them.*
| Model | ARC | MMLU | TruthfulQA | GSM8K |
|------------------------------|-------|-------|-------|-------|
| **SOLAR-10.7B-Instruct-v1.0**| result < 0.1, %: 0.06 |result < 0.1, %: 0.15 | result < 0.1, %: 0.28 | result < 0.1, %: 0.70 |
[3] https://github.com/swj0419/detect-pretrain-code-contamination
[4] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/474#657f2245365456e362412a06
[5] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/265#657b6debf81f6b44b8966230
# **Evaluation Results**
| Model | H6 | Model Size |
|----------------------------------------|-------|------------|
| **SOLAR-10.7B-Instruct-v1.0** | **74.20** | **~ 11B** |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.62 | ~ 46.7B |
| 01-ai/Yi-34B-200K | 70.81 | ~ 34B |
| 01-ai/Yi-34B | 69.42 | ~ 34B |
| mistralai/Mixtral-8x7B-v0.1 | 68.42 | ~ 46.7B |
| meta-llama/Llama-2-70b-hf | 67.87 | ~ 70B |
| tiiuae/falcon-180B | 67.85 | ~ 180B |
| **SOLAR-10.7B-v1.0** | **66.04** | **~11B** |
| mistralai/Mistral-7B-Instruct-v0.2 | 65.71 | ~ 7B |
| Qwen/Qwen-14B | 65.86 | ~ 14B |
| 01-ai/Yi-34B-Chat | 65.32 | ~34B |
| meta-llama/Llama-2-70b-chat-hf | 62.4 | ~ 70B |
| mistralai/Mistral-7B-v0.1 | 60.97 | ~ 7B |
| mistralai/Mistral-7B-Instruct-v0.1 | 54.96 | ~ 7B |
# **Usage Instructions**
This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-Instruct-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Conducting Single-Turn Conversation**
```python
conversation = [ {'role': 'user', 'content': 'Hello?'} ]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_cache=True, max_length=4096)
output_text = tokenizer.decode(outputs[0])
print(output_text)
```
Below is an example of the output.
```
<s> ### User:
Hello?
### Assistant:
Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s>
```
### **License**
- [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0): apache-2.0
- [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0): cc-by-nc-4.0
- Since some non-commercial datasets such as Alpaca are used for fine-tuning, we release this model as cc-by-nc-4.0.
### **How to Cite**
Please cite the following papers using the below format when using this model.
```bibtex
@misc{kim2023solar,
title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling},
author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
year={2023},
eprint={2312.15166},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtext
@misc{kim2024sdpo,
title={sDPO: Don't Use Your Data All at Once},
author={Dahyun Kim and Yungi Kim and Wonho Song and Hyeonwoo Kim and Yunsu Kim and Sanghoon Kim and Chanjun Park},
year={2024},
eprint={2403.19270},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [[email protected]](mailto:[email protected])
| {} | RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2312.15166",
"arxiv:2403.19270",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T01:02:43+00:00 |
null | diffusers | {} | camenduru/ThemeStation | null | [
"diffusers",
"region:us"
] | null | 2024-05-03T01:03:02+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. -->
# Llama-3-8B-sft-lora-ultrachat
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4905 | 1.0 | 278 | 1.3188 |
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Llama-3-8B-sft-lora-ultrachat", "results": []}]} | fortymiles/Llama-3-8B-sft-lora-ultrachat | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | 2024-05-03T01:06:08+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | shallow6414/1iqj8lb | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:06:43+00:00 |
null | mlx |
# mlx-community/Mixtral-8x7B-Instruct-v0.1-4bit
This model was converted to MLX format from [`mistralai/Mixtral-8x7B-Instruct-v0.1`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Mixtral-8x7B-Instruct-v0.1-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["fr", "it", "de", "es", "en"], "license": "apache-2.0", "tags": ["mlx"], "inference": {"parameters": {"temperature": 0.5}}, "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | mlx-community/Mixtral-8x7B-Instruct-v0.1-4bit | null | [
"mlx",
"safetensors",
"mixtral",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T01:06:59+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
mistral-7b-instruct-v0.2-bnb-4bit - bnb 8bits
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit/
Original model description:
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- unsloth
- transformers
- mistral
- mistral-7b
- mistral-instruct
- instruct
- bnb
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Mistral 7b here: https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
| {} | RichardErkhov/unsloth_-_mistral-7b-instruct-v0.2-bnb-4bit-8bits | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T01:07:16+00:00 |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 87 with parameters:
```
{'batch_size': 3, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ranjith999/tamil-base-sentence-transformer | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:09:32+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | golf2248/a12y6nc | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:10:13+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Justin-Y
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"} | Justin-Y/ChatML_llama8b_lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:10:26+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": []} | TrevorAsbery/Mistral-7b-wdc-products-v2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:10:28+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SOLAR-10.7B-Instruct-v1.0 - bnb 8bits
- Model creator: https://huggingface.co/upstage/
- Original model: https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/
Original model description:
---
datasets:
- c-s-ale/alpaca-gpt4-data
- Open-Orca/OpenOrca
- Intel/orca_dpo_pairs
- allenai/ultrafeedback_binarized_cleaned
language:
- en
license: cc-by-nc-4.0
base_model:
- upstage/SOLAR-10.7B-v1.0
---
<p align="left">
<a href="https://go.upstage.ai/solar-obt-hf-modelcardv1-instruct">
<img src="https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/resolve/main/solar-api-banner.png" width="100%"/>
</a>
<p>
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation.)**
# **Introduction**
We introduce SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We present a methodology for scaling LLMs called depth up-scaling (DUS) , which encompasses architectural modifications and continued pretraining. In other words, we integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table.
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements.
For full details of this model please read our [paper](https://arxiv.org/abs/2312.15166).
# **Instruction Fine-Tuning Strategy**
We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO) [1].
We used a mixture of the following datasets
- c-s-ale/alpaca-gpt4-data (SFT)
- Open-Orca/OpenOrca (SFT)
- in-house generated data utilizing Metamath [2] (SFT, DPO)
- Intel/orca_dpo_pairs (DPO)
- allenai/ultrafeedback_binarized_cleaned (DPO)
where we were careful of data contamination by not using GSM8K samples when generating data and filtering tasks when applicable via the following list.
```python
filtering_task_list = [
'task228_arc_answer_generation_easy',
'ai2_arc/ARC-Challenge:1.0.0',
'ai2_arc/ARC-Easy:1.0.0',
'task229_arc_answer_generation_hard',
'hellaswag:1.1.0',
'task1389_hellaswag_completion',
'cot_gsm8k',
'cot_gsm8k_ii',
'drop:2.0.0',
'winogrande:1.1.0'
]
```
Using the datasets mentioned above, we applied SFT and iterative DPO training, a proprietary alignment strategy, to maximize the performance of our resulting model.
[1] Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C.D. and Finn, C., 2023. Direct preference optimization: Your language model is secretly a reward model. NeurIPS.
[2] Yu, L., Jiang, W., Shi, H., Yu, J., Liu, Z., Zhang, Y., Kwok, J.T., Li, Z., Weller, A. and Liu, W., 2023. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284.
# **Data Contamination Test Results**
Recently, there have been contamination issues in some models on the LLM leaderboard.
We note that we made every effort to exclude any benchmark-related datasets from training.
We also ensured the integrity of our model by conducting a data contamination test [3] that is also used by the HuggingFace team [4, 5].
Our results, with `result < 0.1, %:` being well below 0.9, indicate that our model is free from contamination.
*The data contamination test results of HellaSwag and Winograde will be added once [3] supports them.*
| Model | ARC | MMLU | TruthfulQA | GSM8K |
|------------------------------|-------|-------|-------|-------|
| **SOLAR-10.7B-Instruct-v1.0**| result < 0.1, %: 0.06 |result < 0.1, %: 0.15 | result < 0.1, %: 0.28 | result < 0.1, %: 0.70 |
[3] https://github.com/swj0419/detect-pretrain-code-contamination
[4] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/474#657f2245365456e362412a06
[5] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/265#657b6debf81f6b44b8966230
# **Evaluation Results**
| Model | H6 | Model Size |
|----------------------------------------|-------|------------|
| **SOLAR-10.7B-Instruct-v1.0** | **74.20** | **~ 11B** |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.62 | ~ 46.7B |
| 01-ai/Yi-34B-200K | 70.81 | ~ 34B |
| 01-ai/Yi-34B | 69.42 | ~ 34B |
| mistralai/Mixtral-8x7B-v0.1 | 68.42 | ~ 46.7B |
| meta-llama/Llama-2-70b-hf | 67.87 | ~ 70B |
| tiiuae/falcon-180B | 67.85 | ~ 180B |
| **SOLAR-10.7B-v1.0** | **66.04** | **~11B** |
| mistralai/Mistral-7B-Instruct-v0.2 | 65.71 | ~ 7B |
| Qwen/Qwen-14B | 65.86 | ~ 14B |
| 01-ai/Yi-34B-Chat | 65.32 | ~34B |
| meta-llama/Llama-2-70b-chat-hf | 62.4 | ~ 70B |
| mistralai/Mistral-7B-v0.1 | 60.97 | ~ 7B |
| mistralai/Mistral-7B-Instruct-v0.1 | 54.96 | ~ 7B |
# **Usage Instructions**
This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-Instruct-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Conducting Single-Turn Conversation**
```python
conversation = [ {'role': 'user', 'content': 'Hello?'} ]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_cache=True, max_length=4096)
output_text = tokenizer.decode(outputs[0])
print(output_text)
```
Below is an example of the output.
```
<s> ### User:
Hello?
### Assistant:
Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s>
```
### **License**
- [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0): apache-2.0
- [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0): cc-by-nc-4.0
- Since some non-commercial datasets such as Alpaca are used for fine-tuning, we release this model as cc-by-nc-4.0.
### **How to Cite**
Please cite the following papers using the below format when using this model.
```bibtex
@misc{kim2023solar,
title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling},
author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
year={2023},
eprint={2312.15166},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtext
@misc{kim2024sdpo,
title={sDPO: Don't Use Your Data All at Once},
author={Dahyun Kim and Yungi Kim and Wonho Song and Hyeonwoo Kim and Yunsu Kim and Sanghoon Kim and Chanjun Park},
year={2024},
eprint={2403.19270},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [[email protected]](mailto:[email protected])
| {} | RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2312.15166",
"arxiv:2403.19270",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-03T01:14:25+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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | golf2248/on8v5z4 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:15:10+00:00 |
text-generation | transformers | {} | AKILESH18/FinBot | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:15:19+00:00 |
|
null | transformers | {"license": "mit"} | Goodarc/teddydountmodel1 | null | [
"transformers",
"pytorch",
"tensorboard",
"donut",
"license:mit",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2024-05-03T01:15:50+00:00 |
|
null | mlx |
# hyperspaceai/hyperEngine_8B_v2
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b`]() using mlx-lm version **0.11.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("hyperspaceai/hyperEngine_8B_v2")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "other", "tags": ["generated_from_trainer", "axolotl", "mlx"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | hyperspaceai/hyperEngine_8B_v2 | null | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"axolotl",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-05-03T01:16:24+00:00 |
null | null | {"license": "openrail"} | iskaa/arianagrande | null | [
"license:openrail",
"region:us"
] | null | 2024-05-03T01:16:25+00:00 |
|
null | null | {"license": "llama3"} | l3utterfly/suzume-llama-3-8B-multilingual-gguf | null | [
"gguf",
"license:llama3",
"region:us"
] | null | 2024-05-03T01:16:59+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. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4835
- Accuracy: 0.92
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2788 | 1.0 | 225 | 0.4533 | 0.88 |
| 0.3838 | 2.0 | 450 | 1.0800 | 0.75 |
| 0.3945 | 3.0 | 675 | 0.9446 | 0.76 |
| 0.0219 | 4.0 | 900 | 0.6243 | 0.89 |
| 0.0005 | 5.0 | 1125 | 0.4831 | 0.91 |
| 0.0 | 6.0 | 1350 | 0.6262 | 0.88 |
| 0.0001 | 7.0 | 1575 | 0.4827 | 0.93 |
| 0.0 | 8.0 | 1800 | 0.4794 | 0.93 |
| 0.0 | 9.0 | 2025 | 0.4814 | 0.92 |
| 0.0 | 10.0 | 2250 | 0.4835 | 0.92 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "MIT/ast-finetuned-audioset-10-10-0.4593", "model-index": [{"name": "ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "GTZAN", "type": "marsyas/gtzan", "config": "all", "split": "train", "args": "all"}, "metrics": [{"type": "accuracy", "value": 0.92, "name": "Accuracy"}]}]}]} | Gunnika/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan | null | [
"transformers",
"tensorboard",
"safetensors",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:MIT/ast-finetuned-audioset-10-10-0.4593",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:19:52+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CodeLlama-7b-Instruct-hf - GGUF
- Model creator: https://huggingface.co/codellama/
- Original model: https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [CodeLlama-7b-Instruct-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q2_K.gguf) | Q2_K | 2.36GB |
| [CodeLlama-7b-Instruct-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ3_XS.gguf) | IQ3_XS | 2.6GB |
| [CodeLlama-7b-Instruct-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ3_S.gguf) | IQ3_S | 2.75GB |
| [CodeLlama-7b-Instruct-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
| [CodeLlama-7b-Instruct-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ3_M.gguf) | IQ3_M | 2.9GB |
| [CodeLlama-7b-Instruct-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q3_K.gguf) | Q3_K | 3.07GB |
| [CodeLlama-7b-Instruct-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
| [CodeLlama-7b-Instruct-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
| [CodeLlama-7b-Instruct-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
| [CodeLlama-7b-Instruct-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_0.gguf) | Q4_0 | 3.56GB |
| [CodeLlama-7b-Instruct-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
| [CodeLlama-7b-Instruct-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
| [CodeLlama-7b-Instruct-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_K.gguf) | Q4_K | 3.8GB |
| [CodeLlama-7b-Instruct-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
| [CodeLlama-7b-Instruct-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_1.gguf) | Q4_1 | 3.95GB |
| [CodeLlama-7b-Instruct-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_0.gguf) | Q5_0 | 4.33GB |
| [CodeLlama-7b-Instruct-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
| [CodeLlama-7b-Instruct-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_K.gguf) | Q5_K | 4.45GB |
| [CodeLlama-7b-Instruct-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
| [CodeLlama-7b-Instruct-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_1.gguf) | Q5_1 | 4.72GB |
| [CodeLlama-7b-Instruct-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q6_K.gguf) | Q6_K | 5.15GB |
Original model description:
---
language:
- code
pipeline_tag: text-generation
tags:
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
> [!NOTE]
> This is a non-official Code Llama repo. You can find the official Meta repository in the [Meta Llama organization](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf).
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) |
| 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) |
| 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) |
| 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers:
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [x] Infilling.
- [x] Instructions / chat.
- [ ] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Instruct version of the 7B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
| {} | RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-gguf | null | [
"gguf",
"arxiv:2308.12950",
"region:us"
] | null | 2024-05-03T01:20:53+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## How to Get Started with the Model
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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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| {"library_name": "transformers", "tags": []} | shallow6414/xg4mivx | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:22:02+00:00 |
null | null | {"license": "openrail"} | Blocktoast64/MundoEdd | null | [
"license:openrail",
"region:us"
] | null | 2024-05-03T01:22:22+00:00 |
|
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/athirdpath/Llama-3-15b-Instruct
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q2_K.gguf) | Q2_K | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.IQ3_XS.gguf) | IQ3_XS | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q3_K_S.gguf) | Q3_K_S | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.IQ3_S.gguf) | IQ3_S | 7.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.IQ3_M.gguf) | IQ3_M | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q3_K_M.gguf) | Q3_K_M | 7.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q3_K_L.gguf) | Q3_K_L | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.IQ4_XS.gguf) | IQ4_XS | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q4_K_S.gguf) | Q4_K_S | 9.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q4_K_M.gguf) | Q4_K_M | 9.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q5_K_S.gguf) | Q5_K_S | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q5_K_M.gguf) | Q5_K_M | 11.1 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q6_K.gguf) | Q6_K | 12.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-15b-Instruct-GGUF/resolve/main/Llama-3-15b-Instruct.Q8_0.gguf) | Q8_0 | 16.5 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "athirdpath/Llama-3-15b-Instruct", "quantized_by": "mradermacher"} | mradermacher/Llama-3-15b-Instruct-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:athirdpath/Llama-3-15b-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:23:53+00:00 |
text2text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | ankitgu3/t5_pubmed_qa | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:24:18+00:00 |
null | transformers |
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## 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": []} | azhara001/donut-base-demo-new-v2-3e-05_SGD_1876 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:25:25+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_CyberLord
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLord-GGUF/resolve/main/Mixtral_AI_CyberLord.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "LeroyDyer/Mixtral_AI_CyberLord", "quantized_by": "mradermacher"} | mradermacher/Mixtral_AI_CyberLord-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:LeroyDyer/Mixtral_AI_CyberLord",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:25:37+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/DeepMount00/Llama-3-8b-Ita
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8b-Ita-GGUF/resolve/main/Llama-3-8b-Ita.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "datasets": ["DeepMount00/llm_ita_ultra"], "base_model": "DeepMount00/Llama-3-8b-Ita", "quantized_by": "mradermacher"} | mradermacher/Llama-3-8b-Ita-GGUF | null | [
"transformers",
"gguf",
"en",
"dataset:DeepMount00/llm_ita_ultra",
"base_model:DeepMount00/Llama-3-8b-Ita",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:27:19+00:00 |
automatic-speech-recognition | 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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### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shtapm/whisper-large_0502_decoder6_200steps | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:28:14+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-14m_niki-041a_imdb_random-token-1280_10-rounds_seed-3
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-14m_niki-041a_imdb_random-token-1280_10-rounds_seed-3", "results": []}]} | AlignmentResearch/robust_llm_pythia-14m_niki-041a_imdb_random-token-1280_10-rounds_seed-3 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:28:48+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.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
| {"library_name": "transformers", "tags": []} | cilantro9246/s6siwrd | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:29:17+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
NeuralDaredevil-7B - bnb 4bits
- Model creator: https://huggingface.co/mlabonne/
- Original model: https://huggingface.co/mlabonne/NeuralDaredevil-7B/
Original model description:
---
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- lazymergekit
- dpo
- rlhf
- mlabonne/example
base_model: mlabonne/Daredevil-7B
model-index:
- name: NeuralDaredevil-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.88
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.62
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.12
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 66.85
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.08
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 73.16
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
---
# NeuralDaredevil-7B
NeuralDaredevil-7B is a DPO fine-tune of [mlabonne/Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B) using the [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference dataset and my DPO notebook from [this article](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac).
Thanks [Argilla](https://huggingface.co/argilla) for providing the dataset and the training recipe [here](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp). 💪
## 🏆 Evaluation
### Nous
The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite.
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---:|---:|---:|---:|---:|
| [**mlabonne/NeuralDaredevil-7B**](https://huggingface.co/mlabonne/NeuralDaredevil-7B) [📄](https://gist.github.com/mlabonne/cbeb077d1df71cb81c78f742f19f4155) | **59.39** | **45.23** | **76.2** | **67.61** | **48.52** |
| [mlabonne/Beagle14-7B](https://huggingface.co/mlabonne/Beagle14-7B) [📄](https://gist.github.com/mlabonne/f5a5bf8c0827bbec2f05b97cc62d642c) | 59.4 | 44.38 | 76.53 | 69.44 | 47.25 |
| [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) [📄](https://gist.github.com/mlabonne/9082c4e59f4d3f3543c5eda3f4807040) | 58.93 | 45.38 | 76.48 | 65.68 | 48.18 |
| [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B) [📄](https://gist.github.com/mlabonne/b31572a4711c945a4827e7242cfc4b9d) | 58.4 | 44.59 | 76.17 | 65.94 | 46.9 |
| [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) [📄](https://gist.github.com/mlabonne/1afab87b543b0717ec08722cf086dcc3) | 53.71 | 44.17 | 73.72 | 52.53 | 44.4 |
| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
You can find the complete benchmark on [YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
# [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mlabonne__NeuralDaredevil-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.12|
|AI2 Reasoning Challenge (25-Shot)|69.88|
|HellaSwag (10-Shot) |87.62|
|MMLU (5-Shot) |65.12|
|TruthfulQA (0-shot) |66.85|
|Winogrande (5-shot) |82.08|
|GSM8k (5-shot) |73.16|
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/NeuralDaredevil-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"])
```
<p align="center">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
| {} | RichardErkhov/mlabonne_-_NeuralDaredevil-7B-4bits | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T01:29:34+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tiny-random-GemmaForCausalLM - bnb 4bits
- Model creator: https://huggingface.co/fxmarty/
- Original model: https://huggingface.co/fxmarty/tiny-random-GemmaForCausalLM/
Original model description:
---
license: mit
---
This one with a custom `config.head_dim` as allowed by the architecture (see 7b model).
| {} | RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-4bits | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T01:29:38+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tiny-random-GemmaForCausalLM - bnb 8bits
- Model creator: https://huggingface.co/fxmarty/
- Original model: https://huggingface.co/fxmarty/tiny-random-GemmaForCausalLM/
Original model description:
---
license: mit
---
This one with a custom `config.head_dim` as allowed by the architecture (see 7b model).
| {} | RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-8bits | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-03T01:30:00+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. -->
# gpt2Kaggle3
This model is a fine-tuned version of [ytu-ce-cosmos/turkish-gpt2](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 10.9494
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0271 | 1.0 | 1 | 10.9494 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "ytu-ce-cosmos/turkish-gpt2", "model-index": [{"name": "gpt2Kaggle3", "results": []}]} | eminAydin/gpt2Kaggle3 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:ytu-ce-cosmos/turkish-gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:30:38+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SOLAR-10.7B-Instruct-v1.0 - GGUF
- Model creator: https://huggingface.co/upstage/
- Original model: https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [SOLAR-10.7B-Instruct-v1.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q2_K.gguf) | Q2_K | 3.73GB |
| [SOLAR-10.7B-Instruct-v1.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.IQ3_XS.gguf) | IQ3_XS | 4.14GB |
| [SOLAR-10.7B-Instruct-v1.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.IQ3_S.gguf) | IQ3_S | 4.37GB |
| [SOLAR-10.7B-Instruct-v1.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q3_K_S.gguf) | Q3_K_S | 4.34GB |
| [SOLAR-10.7B-Instruct-v1.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.IQ3_M.gguf) | IQ3_M | 4.51GB |
| [SOLAR-10.7B-Instruct-v1.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q3_K.gguf) | Q3_K | 4.84GB |
| [SOLAR-10.7B-Instruct-v1.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q3_K_M.gguf) | Q3_K_M | 4.84GB |
| [SOLAR-10.7B-Instruct-v1.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q3_K_L.gguf) | Q3_K_L | 5.26GB |
| [SOLAR-10.7B-Instruct-v1.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.IQ4_XS.gguf) | IQ4_XS | 5.43GB |
| [SOLAR-10.7B-Instruct-v1.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q4_0.gguf) | Q4_0 | 5.66GB |
| [SOLAR-10.7B-Instruct-v1.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.IQ4_NL.gguf) | IQ4_NL | 5.72GB |
| [SOLAR-10.7B-Instruct-v1.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q4_K_S.gguf) | Q4_K_S | 5.7GB |
| [SOLAR-10.7B-Instruct-v1.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q4_K.gguf) | Q4_K | 6.02GB |
| [SOLAR-10.7B-Instruct-v1.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q4_K_M.gguf) | Q4_K_M | 6.02GB |
| [SOLAR-10.7B-Instruct-v1.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q4_1.gguf) | Q4_1 | 6.27GB |
| [SOLAR-10.7B-Instruct-v1.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q5_0.gguf) | Q5_0 | 6.89GB |
| [SOLAR-10.7B-Instruct-v1.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q5_K_S.gguf) | Q5_K_S | 6.89GB |
| [SOLAR-10.7B-Instruct-v1.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q5_K.gguf) | Q5_K | 7.08GB |
| [SOLAR-10.7B-Instruct-v1.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q5_K_M.gguf) | Q5_K_M | 7.08GB |
| [SOLAR-10.7B-Instruct-v1.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q5_1.gguf) | Q5_1 | 7.51GB |
| [SOLAR-10.7B-Instruct-v1.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf/blob/main/SOLAR-10.7B-Instruct-v1.0.Q6_K.gguf) | Q6_K | 8.2GB |
Original model description:
---
datasets:
- c-s-ale/alpaca-gpt4-data
- Open-Orca/OpenOrca
- Intel/orca_dpo_pairs
- allenai/ultrafeedback_binarized_cleaned
language:
- en
license: cc-by-nc-4.0
base_model:
- upstage/SOLAR-10.7B-v1.0
---
<p align="left">
<a href="https://go.upstage.ai/solar-obt-hf-modelcardv1-instruct">
<img src="https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/resolve/main/solar-api-banner.png" width="100%"/>
</a>
<p>
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation.)**
# **Introduction**
We introduce SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We present a methodology for scaling LLMs called depth up-scaling (DUS) , which encompasses architectural modifications and continued pretraining. In other words, we integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table.
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements.
For full details of this model please read our [paper](https://arxiv.org/abs/2312.15166).
# **Instruction Fine-Tuning Strategy**
We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO) [1].
We used a mixture of the following datasets
- c-s-ale/alpaca-gpt4-data (SFT)
- Open-Orca/OpenOrca (SFT)
- in-house generated data utilizing Metamath [2] (SFT, DPO)
- Intel/orca_dpo_pairs (DPO)
- allenai/ultrafeedback_binarized_cleaned (DPO)
where we were careful of data contamination by not using GSM8K samples when generating data and filtering tasks when applicable via the following list.
```python
filtering_task_list = [
'task228_arc_answer_generation_easy',
'ai2_arc/ARC-Challenge:1.0.0',
'ai2_arc/ARC-Easy:1.0.0',
'task229_arc_answer_generation_hard',
'hellaswag:1.1.0',
'task1389_hellaswag_completion',
'cot_gsm8k',
'cot_gsm8k_ii',
'drop:2.0.0',
'winogrande:1.1.0'
]
```
Using the datasets mentioned above, we applied SFT and iterative DPO training, a proprietary alignment strategy, to maximize the performance of our resulting model.
[1] Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C.D. and Finn, C., 2023. Direct preference optimization: Your language model is secretly a reward model. NeurIPS.
[2] Yu, L., Jiang, W., Shi, H., Yu, J., Liu, Z., Zhang, Y., Kwok, J.T., Li, Z., Weller, A. and Liu, W., 2023. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284.
# **Data Contamination Test Results**
Recently, there have been contamination issues in some models on the LLM leaderboard.
We note that we made every effort to exclude any benchmark-related datasets from training.
We also ensured the integrity of our model by conducting a data contamination test [3] that is also used by the HuggingFace team [4, 5].
Our results, with `result < 0.1, %:` being well below 0.9, indicate that our model is free from contamination.
*The data contamination test results of HellaSwag and Winograde will be added once [3] supports them.*
| Model | ARC | MMLU | TruthfulQA | GSM8K |
|------------------------------|-------|-------|-------|-------|
| **SOLAR-10.7B-Instruct-v1.0**| result < 0.1, %: 0.06 |result < 0.1, %: 0.15 | result < 0.1, %: 0.28 | result < 0.1, %: 0.70 |
[3] https://github.com/swj0419/detect-pretrain-code-contamination
[4] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/474#657f2245365456e362412a06
[5] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/265#657b6debf81f6b44b8966230
# **Evaluation Results**
| Model | H6 | Model Size |
|----------------------------------------|-------|------------|
| **SOLAR-10.7B-Instruct-v1.0** | **74.20** | **~ 11B** |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.62 | ~ 46.7B |
| 01-ai/Yi-34B-200K | 70.81 | ~ 34B |
| 01-ai/Yi-34B | 69.42 | ~ 34B |
| mistralai/Mixtral-8x7B-v0.1 | 68.42 | ~ 46.7B |
| meta-llama/Llama-2-70b-hf | 67.87 | ~ 70B |
| tiiuae/falcon-180B | 67.85 | ~ 180B |
| **SOLAR-10.7B-v1.0** | **66.04** | **~11B** |
| mistralai/Mistral-7B-Instruct-v0.2 | 65.71 | ~ 7B |
| Qwen/Qwen-14B | 65.86 | ~ 14B |
| 01-ai/Yi-34B-Chat | 65.32 | ~34B |
| meta-llama/Llama-2-70b-chat-hf | 62.4 | ~ 70B |
| mistralai/Mistral-7B-v0.1 | 60.97 | ~ 7B |
| mistralai/Mistral-7B-Instruct-v0.1 | 54.96 | ~ 7B |
# **Usage Instructions**
This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-Instruct-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Conducting Single-Turn Conversation**
```python
conversation = [ {'role': 'user', 'content': 'Hello?'} ]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_cache=True, max_length=4096)
output_text = tokenizer.decode(outputs[0])
print(output_text)
```
Below is an example of the output.
```
<s> ### User:
Hello?
### Assistant:
Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s>
```
### **License**
- [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0): apache-2.0
- [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0): cc-by-nc-4.0
- Since some non-commercial datasets such as Alpaca are used for fine-tuning, we release this model as cc-by-nc-4.0.
### **How to Cite**
Please cite the following papers using the below format when using this model.
```bibtex
@misc{kim2023solar,
title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling},
author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
year={2023},
eprint={2312.15166},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtext
@misc{kim2024sdpo,
title={sDPO: Don't Use Your Data All at Once},
author={Dahyun Kim and Yungi Kim and Wonho Song and Hyeonwoo Kim and Yunsu Kim and Sanghoon Kim and Chanjun Park},
year={2024},
eprint={2403.19270},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [[email protected]](mailto:[email protected])
| {} | RichardErkhov/upstage_-_SOLAR-10.7B-Instruct-v1.0-gguf | null | [
"gguf",
"arxiv:2312.15166",
"arxiv:2403.19270",
"region:us"
] | null | 2024-05-03T01:30:38+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tiny-random-GemmaForCausalLM - GGUF
- Model creator: https://huggingface.co/fxmarty/
- Original model: https://huggingface.co/fxmarty/tiny-random-GemmaForCausalLM/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tiny-random-GemmaForCausalLM.Q2_K.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q2_K.gguf) | Q2_K | 0.01GB |
| [tiny-random-GemmaForCausalLM.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.IQ3_XS.gguf) | IQ3_XS | 0.01GB |
| [tiny-random-GemmaForCausalLM.IQ3_S.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.IQ3_S.gguf) | IQ3_S | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q3_K_S.gguf) | Q3_K_S | 0.01GB |
| [tiny-random-GemmaForCausalLM.IQ3_M.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.IQ3_M.gguf) | IQ3_M | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q3_K.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q3_K.gguf) | Q3_K | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q3_K_M.gguf) | Q3_K_M | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q3_K_L.gguf) | Q3_K_L | 0.01GB |
| [tiny-random-GemmaForCausalLM.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.IQ4_XS.gguf) | IQ4_XS | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q4_0.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q4_0.gguf) | Q4_0 | 0.01GB |
| [tiny-random-GemmaForCausalLM.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.IQ4_NL.gguf) | IQ4_NL | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q4_K_S.gguf) | Q4_K_S | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q4_K.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q4_K.gguf) | Q4_K | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q4_K_M.gguf) | Q4_K_M | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q4_1.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q4_1.gguf) | Q4_1 | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q5_0.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q5_0.gguf) | Q5_0 | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q5_K_S.gguf) | Q5_K_S | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q5_K.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q5_K.gguf) | Q5_K | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q5_K_M.gguf) | Q5_K_M | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q5_1.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q5_1.gguf) | Q5_1 | 0.01GB |
| [tiny-random-GemmaForCausalLM.Q6_K.gguf](https://huggingface.co/RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf/blob/main/tiny-random-GemmaForCausalLM.Q6_K.gguf) | Q6_K | 0.01GB |
Original model description:
---
license: mit
---
This one with a custom `config.head_dim` as allowed by the architecture (see 7b model).
| {} | RichardErkhov/fxmarty_-_tiny-random-GemmaForCausalLM-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-03T01:30:46+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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[More Information Needed] | {"library_name": "transformers", "tags": []} | ajay-airrived/mistral_airrived_tpfp | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T01:33:06+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 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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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | austin/admission_reason_generator | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:33:55+00:00 |
null | null | {} | Wilailack/TAIDE-7B-Thai-Pretrain-LoRA | null | [
"region:us"
] | null | 2024-05-03T01:34:06+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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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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| {"library_name": "transformers", "tags": []} | golf2248/upn483h | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:35:03+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
NeuralDaredevil-7B - bnb 8bits
- Model creator: https://huggingface.co/mlabonne/
- Original model: https://huggingface.co/mlabonne/NeuralDaredevil-7B/
Original model description:
---
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- lazymergekit
- dpo
- rlhf
- mlabonne/example
base_model: mlabonne/Daredevil-7B
model-index:
- name: NeuralDaredevil-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.88
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.62
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.12
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 66.85
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.08
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 73.16
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
---
# NeuralDaredevil-7B
NeuralDaredevil-7B is a DPO fine-tune of [mlabonne/Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B) using the [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference dataset and my DPO notebook from [this article](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac).
Thanks [Argilla](https://huggingface.co/argilla) for providing the dataset and the training recipe [here](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp). 💪
## 🏆 Evaluation
### Nous
The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite.
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---:|---:|---:|---:|---:|
| [**mlabonne/NeuralDaredevil-7B**](https://huggingface.co/mlabonne/NeuralDaredevil-7B) [📄](https://gist.github.com/mlabonne/cbeb077d1df71cb81c78f742f19f4155) | **59.39** | **45.23** | **76.2** | **67.61** | **48.52** |
| [mlabonne/Beagle14-7B](https://huggingface.co/mlabonne/Beagle14-7B) [📄](https://gist.github.com/mlabonne/f5a5bf8c0827bbec2f05b97cc62d642c) | 59.4 | 44.38 | 76.53 | 69.44 | 47.25 |
| [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) [📄](https://gist.github.com/mlabonne/9082c4e59f4d3f3543c5eda3f4807040) | 58.93 | 45.38 | 76.48 | 65.68 | 48.18 |
| [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B) [📄](https://gist.github.com/mlabonne/b31572a4711c945a4827e7242cfc4b9d) | 58.4 | 44.59 | 76.17 | 65.94 | 46.9 |
| [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) [📄](https://gist.github.com/mlabonne/1afab87b543b0717ec08722cf086dcc3) | 53.71 | 44.17 | 73.72 | 52.53 | 44.4 |
| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
You can find the complete benchmark on [YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
# [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mlabonne__NeuralDaredevil-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.12|
|AI2 Reasoning Challenge (25-Shot)|69.88|
|HellaSwag (10-Shot) |87.62|
|MMLU (5-Shot) |65.12|
|TruthfulQA (0-shot) |66.85|
|Winogrande (5-shot) |82.08|
|GSM8k (5-shot) |73.16|
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/NeuralDaredevil-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"])
```
<p align="center">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
| {} | RichardErkhov/mlabonne_-_NeuralDaredevil-7B-8bits | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-03T01:36:47+00:00 |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
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:
- eval_loss: 4.4483
- eval_runtime: 4.5879
- eval_samples_per_second: 108.983
- eval_steps_per_second: 1.744
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | rajabilalnazir/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:37:15+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
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[More Information Needed]
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## Model Examination [optional]
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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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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-final_v23e-05_SGD | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:39:44+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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| {"library_name": "transformers", "tags": []} | golf2248/nrmy8hw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:39:50+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-translation
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1835
- Pearsonr: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearsonr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 69 | 0.1835 | nan |
| No log | 2.0 | 138 | 0.1742 | nan |
| No log | 3.0 | 207 | 0.2082 | nan |
| No log | 4.0 | 276 | 0.1975 | -0.0260 |
| No log | 5.0 | 345 | 0.2118 | -0.0260 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["pearsonr"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-finetuned-translation", "results": []}]} | aabid123/roberta-base-finetuned-translation | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:40:19+00:00 |
null | null | {} | Pekarnick/multilingual-e5-large-gguf | null | [
"region:us"
] | null | 2024-05-03T01:43:54+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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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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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
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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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| {"library_name": "transformers", "tags": []} | shallow6414/3c60m76 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:45:20+00:00 |
null | null | {} | Sricharannama/nlp-grammar-correction | null | [
"region:us"
] | null | 2024-05-03T01:45:36+00:00 |
|
question-answering | transformers | ## Model Description
I fineturned it from https://huggingface.co/Fsoft-AIC/videberta-xsmall.
I am using it for relation extraction task (information extraction).
| {"language": ["vi", "vn", "en"], "license": "cc-by-nc-4.0", "tags": ["question-answering", "pytorch"], "datasets": ["NghiemAbe/viquad"], "metrics": ["squad"], "pipeline_tag": "question-answering", "widget": [{"text": "Vi\u1ec7c \u0111\u01b0a ra c\u00e1c Ch\u00ednh s\u00e1ch \u0111\u00e3 t\u00e1c \u0111\u1ed9ng \u0111i\u1ec1u g\u00ec v\u1edbi Malaysia?", "context": "S\u1eafc t\u1ed9c c\u00f3 \u1ea3nh h\u01b0\u1edfng l\u1edbn trong ch\u00ednh tr\u1ecb Malaysia, nhi\u1ec1u ch\u00ednh \u0111\u1ea3ng d\u1ef1a tr\u00ean n\u1ec1n t\u1ea3ng d\u00e2n t\u1ed9c. C\u00e1c h\u00e0nh \u0111\u1ed9ng qu\u1ea3 quy\u1ebft nh\u01b0 Ch\u00ednh s\u00e1ch Kinh t\u1ebf m\u1edbi v\u00e0 thay th\u1ebf n\u00f3 l\u00e0 Ch\u00ednh s\u00e1ch Ph\u00e1t tri\u1ec3n Qu\u1ed1c gia, \u0111\u01b0\u1ee3c th\u1ef1c hi\u1ec7n nh\u1eb1m th\u00fac \u0111\u1ea9y \u0111\u1ecba v\u1ecb c\u1ee7a bumiputera, bao g\u1ed3m ng\u01b0\u1eddi M\u00e3 Lai v\u00e0 c\u00e1c b\u1ed9 l\u1ea1c b\u1ea3n \u0111\u1ecba, tr\u01b0\u1edbc nh\u1eefng ng\u01b0\u1eddi phi bumiputera nh\u01b0 ng\u01b0\u1eddi Malaysia g\u1ed1c Hoa v\u00e0 ng\u01b0\u1eddi Malaysia g\u1ed1c \u1ea4n. C\u00e1c ch\u00ednh s\u00e1ch n\u00e0y quy \u0111\u1ecbnh \u01b0u \u0111\u00e3i cho bumiputera trong vi\u1ec7c l\u00e0m, gi\u00e1o d\u1ee5c, h\u1ecdc b\u1ed5ng, kinh doanh, ti\u1ebfp c\u1eadn nh\u00e0 gi\u00e1 r\u1ebb h\u01a1n v\u00e0 h\u1ed7 tr\u1ee3 ti\u1ebft ki\u1ec7m. Tuy nhi\u00ean, n\u00f3 g\u00e2y ra o\u00e1n gi\u1eadn r\u1ea5t l\u1edbn gi\u1eefa c\u00e1c d\u00e2n t\u1ed9c."}]} | lqbin/videberta-xsmall_batch24_epoch30v6 | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"vi",
"vn",
"en",
"dataset:NghiemAbe/viquad",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:46:21+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
NeuralDaredevil-7B - GGUF
- Model creator: https://huggingface.co/mlabonne/
- Original model: https://huggingface.co/mlabonne/NeuralDaredevil-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [NeuralDaredevil-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [NeuralDaredevil-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [NeuralDaredevil-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [NeuralDaredevil-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [NeuralDaredevil-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [NeuralDaredevil-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [NeuralDaredevil-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [NeuralDaredevil-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [NeuralDaredevil-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [NeuralDaredevil-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [NeuralDaredevil-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [NeuralDaredevil-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [NeuralDaredevil-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [NeuralDaredevil-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [NeuralDaredevil-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [NeuralDaredevil-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [NeuralDaredevil-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [NeuralDaredevil-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [NeuralDaredevil-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [NeuralDaredevil-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [NeuralDaredevil-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf/blob/main/NeuralDaredevil-7B.Q6_K.gguf) | Q6_K | 5.53GB |
Original model description:
---
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- lazymergekit
- dpo
- rlhf
- mlabonne/example
base_model: mlabonne/Daredevil-7B
model-index:
- name: NeuralDaredevil-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.88
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.62
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.12
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 66.85
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.08
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 73.16
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralDaredevil-7B
name: Open LLM Leaderboard
---
# NeuralDaredevil-7B
NeuralDaredevil-7B is a DPO fine-tune of [mlabonne/Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B) using the [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference dataset and my DPO notebook from [this article](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac).
Thanks [Argilla](https://huggingface.co/argilla) for providing the dataset and the training recipe [here](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp). 💪
## 🏆 Evaluation
### Nous
The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite.
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---:|---:|---:|---:|---:|
| [**mlabonne/NeuralDaredevil-7B**](https://huggingface.co/mlabonne/NeuralDaredevil-7B) [📄](https://gist.github.com/mlabonne/cbeb077d1df71cb81c78f742f19f4155) | **59.39** | **45.23** | **76.2** | **67.61** | **48.52** |
| [mlabonne/Beagle14-7B](https://huggingface.co/mlabonne/Beagle14-7B) [📄](https://gist.github.com/mlabonne/f5a5bf8c0827bbec2f05b97cc62d642c) | 59.4 | 44.38 | 76.53 | 69.44 | 47.25 |
| [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) [📄](https://gist.github.com/mlabonne/9082c4e59f4d3f3543c5eda3f4807040) | 58.93 | 45.38 | 76.48 | 65.68 | 48.18 |
| [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B) [📄](https://gist.github.com/mlabonne/b31572a4711c945a4827e7242cfc4b9d) | 58.4 | 44.59 | 76.17 | 65.94 | 46.9 |
| [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) [📄](https://gist.github.com/mlabonne/1afab87b543b0717ec08722cf086dcc3) | 53.71 | 44.17 | 73.72 | 52.53 | 44.4 |
| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
You can find the complete benchmark on [YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
# [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mlabonne__NeuralDaredevil-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.12|
|AI2 Reasoning Challenge (25-Shot)|69.88|
|HellaSwag (10-Shot) |87.62|
|MMLU (5-Shot) |65.12|
|TruthfulQA (0-shot) |66.85|
|Winogrande (5-shot) |82.08|
|GSM8k (5-shot) |73.16|
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/NeuralDaredevil-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"])
```
<p align="center">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
| {} | RichardErkhov/mlabonne_-_NeuralDaredevil-7B-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-03T01:46:37+00:00 |
text-generation | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | shallow6414/49wkre2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:47:28+00:00 |
text-generation | transformers |
# Model Card for Model ID
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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| {"library_name": "transformers", "tags": []} | shallow6414/2r5cl8z | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:48:30+00:00 |
sentence-similarity | sentence-transformers |
# Nithin29/snowflake-ft-camelids-l
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('Nithin29/snowflake-ft-camelids-l')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Nithin29/snowflake-ft-camelids-l)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 12 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Nithin29/snowflake-ft-camelids-l | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:49:13+00:00 |
fill-mask | transformers | {"license": "apache-2.0"} | rajabilalnazir/distilbert-base-uncased-finetuned-imdb-accelerate | null | [
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:51:05+00:00 |
|
fill-mask | 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. -->
# pre-train_mBERT
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1971
- Perplexity 3.31
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 1.4994 | 1.0 | 368814 | 1.3694 |
| 1.3718 | 2.0 | 737628 | 1.2540 |
| 1.2979 | 3.0 | 1106442 | 1.1986 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0a0+ebedce2
- Datasets 2.17.1
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google-bert/bert-base-multilingual-cased", "model-index": [{"name": "pre-train_mBERT", "results": []}]} | morten-j/pre-train_mBERT | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:51:10+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ibivibiv/llama3-8b-ultrafeedback-dpo
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-8b-ultrafeedback-dpo-GGUF/resolve/main/llama3-8b-ultrafeedback-dpo.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "ibivibiv/llama3-8b-ultrafeedback-dpo", "quantized_by": "mradermacher"} | mradermacher/llama3-8b-ultrafeedback-dpo-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:ibivibiv/llama3-8b-ultrafeedback-dpo",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:53:05+00:00 |
null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | abhayesian/Bobzilla_DPO2 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:54:09+00:00 |
automatic-speech-recognition | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shtapm/whisper-large_0502_decoder7_200steps | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T01:55:44+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | cilantro9246/dm40ipr | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:55:47+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | ryanyeo/kirnect-2-koAlpaca-polyglot-5.8B-remote-0501-SFT | null | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:56:42+00:00 |
text-generation | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | golf2248/2tp9zah | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T01:59:34+00:00 |
null | null | {"license": "apache-2.0"} | Honggui/ddpm-butterflies-128 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T02:01:19+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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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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| {"library_name": "transformers", "tags": []} | golf2248/v1mdkzq | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:04:12+00:00 |
text2text-generation | transformers | {} | thaddeuspaez/tlatolibot-nllb-spa-nah | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:05:36+00:00 |
|
null | null | {"license": "openrail"} | Coolwowsocoolwow/Mutahar | null | [
"license:openrail",
"region:us"
] | null | 2024-05-03T02:06:28+00:00 |
|
null | null | {"license": "openrail"} | weillon/arigato | null | [
"license:openrail",
"region:us"
] | null | 2024-05-03T02:08:05+00:00 |
|
null | null |
```
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: 0.60
weight: 0.30
- model: winglian/llama-3-8b-1m-PoSE
parameters:
density: 0.55
weight: 0.15
- model: MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3
parameters:
density: 0.55
weight: 0.15
- model: asiansoul/Llama-3-Open-Ko-Linear-8B
parameters:
density: 0.55
weight: 0.2
- model: nayohan/llama3-8b-it-translation-general-en-ko-1sent
parameters:
density: 0.55
weight: 0.1
- model: cognitivecomputations/dolphin-2.9-llama3-8b
parameters:
density: 0.55
weight: 0.1
- model: Danielbrdz/Barcenas-Llama3-8b-ORPO
parameters:
density: 0.55
weight: 0.05
- model: vicgalle/Configurable-Llama-3-8B-v0.3
parameters:
density: 0.55
weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: bfloat16
``` | {"license": "other", "license_name": "other", "license_link": "LICENSE"} | asiansoul/Versatile-Llama-3-8B | null | [
"license:other",
"region:us"
] | null | 2024-05-03T02:10:22+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Atom-7B - bnb 4bits
- Model creator: https://huggingface.co/FlagAlpha/
- Original model: https://huggingface.co/FlagAlpha/Atom-7B/
Original model description:
---
developers: [https://huggingface.co/FlagAlphaAI]
license: apache-2.0
language:
- zh
- en
pipeline_tag: question-answering
library_name: transformers
---
# Atom-7B
Atom-7B完全开源可商用，由Llama中文社区和AtomEcho（原子回声）联合研发，基于Llama2-7B采用大规模的中文数据进行了继续预训练，我们会持续提供更新的模型参数，模型训练过程见[llama.family](https://llama.family)。
模型的部署、训练、微调等方法详见Llama中文社区GitHub仓库：[**Llama-Chinese**](https://github.com/LlamaFamily/Llama-Chinese)。
## 📝 中文数据
| 类型 | 描述 |
| ---------------------------------------------------------- | ------------------------------------------------------------ |
| 网络数据 | 互联网上公开的网络数据，挑选出去重后的高质量中文数据，涉及到百科、书籍、博客、新闻、公告、小说等高质量长文本数据。 |
| [Wikipedia](https://github.com/goldsmith/Wikipedia) | 中文Wikipedia的数据 |
| [悟道](https://github.com/BAAI-WuDao/Model) | 中文悟道开源的200G数据 |
| [Clue](https://github.com/CLUEbenchmark/CLUEDatasetSearch) | Clue开放的中文预训练数据，进行清洗后的高质量中文长文本数据 |
| 竞赛数据集 | 近年来中文自然语言处理多任务竞赛数据集，约150个 |
| [MNBVC](https://github.com/esbatmop/MNBVC) | MNBVC 中清洗出来的部分数据集 |
**我们也欢迎大家在[llama.family](https://llama.family)中贡献自己的数据，您的数据通过审核后会加入模型训练，也将影响模型未来的能力走向。**
## 📚 中文词表
为了提高中文文本处理的效率，我们针对Llama2模型的词表进行了深度优化。
首先，我们基于数百G的中文文本，**在Llama2词表的基础上扩展词库至65,000个单词**。
经过测试，我们的改进使得**中文编码/解码速度提高了约350％**。
此外，我们还扩大了中文字符集的覆盖范围，包括所有**emoji符号**，这使的生成带有表情符号的文章更加高效。
对于Llama2原生词表中的一些特殊情况，如数字、英文等，我们尽可能地避免对其进行修改或替换。
最终，成功地实现了一种既能提高中文处理效率又能保持Llama2原有性能的方法。
## 📈 训练过程
**模型结构**
基于当前最优秀的开源模型Llama2，使用主流Decoder-only的标准Transformer网络结构，支持4K的上下文长度（Context Length），为同尺寸模型中最长，能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
**FlashAttention-2高效训练**
Atom-7B采用了FlashAttention-2技术进行训练。由于在处理较长的输入序列时，内存消耗的问题可能会导致“内存爆炸”现象。FlashAttention-2是一种高效注意力机制的实现方式之一，相较于传统的注意力技术（Attention），它拥有更快速的速度以及更加优化的内存占用率。
**基于NTK的自适应上下文扩展技术**
- 可在不继续训练模型的情况下支持更长的上下文
- 本项目中模型默认支持4K上下文，利用上述技术可扩展至18K+
- 经过微调可以支持到32K+
## 💻 推理配置
实际应用中，消费级显卡要比专业显卡便宜的多（比如3090相比A10，同样都是24G显存）。
对于消费级显卡，直接FP32肯定放不下，一般最基本的是FP16，而INT8和INT4量化就很有用，例如：
- 对于3080显卡（10G显存），Atom-7B的INT8只需要8G显存可以直接部署。
- 对于3080显卡（10G显存），Atom-7B的INT4只需要5G显存可以直接部署。
---
# Llama中文社区
## 🚀 社区地址：
Github：[**Llama-Chinese**](https://github.com/LlamaFamily/Llama-Chinese)
在线体验链接：[**llama.family**](https://llama.family/)
## 🔥 社区介绍
欢迎来到Llama中文社区！
我们是一个专注于Llama模型在中文方面的优化和上层建设的高级技术社区。
**基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级**。
我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。
## 🐼 社区资源
- Llama2在线体验链接[**llama.family**](https://llama.family/)，同时包含Meta原版和中文微调版本！
- Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！
- [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！
| {} | RichardErkhov/FlagAlpha_-_Atom-7B-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T02:10:30+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Out-of-Scope Use
<!-- 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
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[More Information Needed] | {"library_name": "transformers", "tags": []} | zbigi/BART_raw | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:10:54+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** xkiwilabs
- **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"} | xkiwilabs/lora_opLLama3_modelv9 | 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 | 2024-05-03T02:11:15+00:00 |
text-generation | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | shallow6414/49n3588 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:11:28+00:00 |
feature-extraction | sentence-transformers |
# WhereIsAI/UAE-Code-Large-V1
📢 `WhereIsAI/UAE-Code-Large-V1` **is licensed under MIT. Feel free to use it in any scenario.**
If you use it for academic papers, we would greatly appreciate it if you could cite us. 👉 [citation info](#citation).
This model builds upon [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) and is fine-tuned on the [GIS: Github Issue Similarity](https://huggingface.co/datasets/WhereIsAI/github-issue-similarity) dataset using [AnglE](https://github.com/SeanLee97/AnglE) loss (https://arxiv.org/abs/2309.12871).
It can be used to measure **code/issue similarity**.
Results (test set):
- Spearman correlation: 71.19
- Accuracy: 84.37
## Usage
### 1. angle-emb
You can use it via `angle-emb` as follows:
install:
```
python -m pip install -U angle-emb
```
example:
```python
from scipy import spatial
from angle_emb import AnglE
model = AnglE.from_pretrained('WhereIsAI/UAE-Code-Large-V1').cuda()
quick_sort = '''# Approach 2: Quicksort using list comprehension
def quicksort(arr):
if len(arr) <= 1:
return arr
else:
pivot = arr[0]
left = [x for x in arr[1:] if x < pivot]
right = [x for x in arr[1:] if x >= pivot]
return quicksort(left) + [pivot] + quicksort(right)
# Example usage
arr = [1, 7, 4, 1, 10, 9, -2]
sorted_arr = quicksort(arr)
print("Sorted Array in Ascending Order:")
print(sorted_arr)'''
bubble_sort = '''def bubblesort(elements):
# Looping from size of array from last index[-1] to index [0]
for n in range(len(elements)-1, 0, -1):
swapped = False
for i in range(n):
if elements[i] > elements[i + 1]:
swapped = True
# swapping data if the element is less than next element in the array
elements[i], elements[i + 1] = elements[i + 1], elements[i]
if not swapped:
# exiting the function if we didn't make a single swap
# meaning that the array is already sorted.
return
elements = [39, 12, 18, 85, 72, 10, 2, 18]
print("Unsorted list is,")
print(elements)
bubblesort(elements)
print("Sorted Array is, ")
print(elements)'''
vecs = model.encode([
'def echo(): print("hello world")',
quick_sort,
bubble_sort
])
print('cos sim (0, 1):', 1 - spatial.distance.cosine(vecs[0], vecs[1]))
print('cos sim (0, 2)', 1 - spatial.distance.cosine(vecs[0], vecs[2]))
print('cos sim (1, 2):', 1 - spatial.distance.cosine(vecs[1], vecs[2]))
```
output:
```
cos sim (0, 1): 0.34329649806022644
cos sim (0, 2) 0.3627094626426697
cos sim (1, 2): 0.6972219347953796
```
## sentence-transformers
You can also use it via `sentence-transformers`
```python
from scipy import spatial
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('WhereIsAI/UAE-Code-Large-V1').cuda()
quick_sort = '''# Approach 2: Quicksort using list comprehension
def quicksort(arr):
if len(arr) <= 1:
return arr
else:
pivot = arr[0]
left = [x for x in arr[1:] if x < pivot]
right = [x for x in arr[1:] if x >= pivot]
return quicksort(left) + [pivot] + quicksort(right)
# Example usage
arr = [1, 7, 4, 1, 10, 9, -2]
sorted_arr = quicksort(arr)
print("Sorted Array in Ascending Order:")
print(sorted_arr)'''
bubble_sort = '''def bubblesort(elements):
# Looping from size of array from last index[-1] to index [0]
for n in range(len(elements)-1, 0, -1):
swapped = False
for i in range(n):
if elements[i] > elements[i + 1]:
swapped = True
# swapping data if the element is less than next element in the array
elements[i], elements[i + 1] = elements[i + 1], elements[i]
if not swapped:
# exiting the function if we didn't make a single swap
# meaning that the array is already sorted.
return
elements = [39, 12, 18, 85, 72, 10, 2, 18]
print("Unsorted list is,")
print(elements)
bubblesort(elements)
print("Sorted Array is, ")
print(elements)'''
vecs = model.encode([
'def echo(): print("hello world")',
quick_sort,
bubble_sort
])
print('cos sim (0, 1):', 1 - spatial.distance.cosine(vecs[0], vecs[1]))
print('cos sim (0, 2)', 1 - spatial.distance.cosine(vecs[0], vecs[2]))
print('cos sim (1, 2):', 1 - spatial.distance.cosine(vecs[1], vecs[2]))
```
output:
```
cos sim (0, 1): 0.34329649806022644
cos sim (0, 2) 0.3627094626426697
cos sim (1, 2): 0.6972219347953796
```
# Citation
```bibtex
@article{li2023angle,
title={AnglE-optimized Text Embeddings},
author={Li, Xianming and Li, Jing},
journal={arXiv preprint arXiv:2309.12871},
year={2023}
}
``` | {"language": ["en"], "license": "mit", "library_name": "sentence-transformers", "datasets": ["WhereIsAI/github-issue-similarity"], "pipeline_tag": "feature-extraction"} | WhereIsAI/UAE-Code-Large-V1 | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"en",
"dataset:WhereIsAI/github-issue-similarity",
"arxiv:2309.12871",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:12:21+00:00 |
null | null | {} | kdwivedi1985/ai-for-ecomm | null | [
"region:us"
] | null | 2024-05-03T02:13:58+00:00 |
|
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | shallow6414/301yys7 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:15:07+00:00 |
null | null | {} | ngl18/long-t5-loRa-pubmed | null | [
"region:us"
] | null | 2024-05-03T02:16:05+00:00 |
|
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | shallow6414/y75qn1g | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:16:09+00:00 |
text-generation | transformers |
# Model Summary
Hanscripter is an instruction-tuned language model focused on translation classical Chinese (i.e WenYanwen 文言文) to English
- Base Model: Meta-Llama-3-8B-Instruct
- SFT Dataset: KaifengGGG/WenYanWen_English_Parallel
- Fine-tune Method: QLoRA
# Version
# Usage
# Fine-tuning Details
Below are detailed descriptions of the various parameters and technologies used.
## LoRA Parameters
- **lora_r**: 64
- **lora_alpha**: 16
- **lora_dropout**: 0.1
## Quantization
The model uses Bitsandbytes for state-of-the-art model quantization, enhancing computational efficiency:
- **use_4bit**: `True` - Enables the use of 4-bit quantization.
- **bnb_4bit_compute_dtype**: "float16" - The datatype used for computation in quantized state.
- **bnb_4bit_quant_type**: "nf4" - Specifies the quantization type.
- **use_nested_quant**: `False` - Nested quantization is not used.
## Training Arguments
Settings for training the model are as follows:
- **num_train_epochs**: 10
- **fp16**: `False`
- **bf16**: `True` - Optimized for use with A100 GPUs, employing Brain Floating Point (bf16).
- **per_device_train_batch_size**: 2
- **per_device_eval_batch_size**: 2
- **gradient_accumulation_steps**: 4
- **gradient_checkpointing**: `True`
- **max_grad_norm**: 0.3
- **learning_rate**: 0.0002
- **weight_decay**: 0.001
- **optim**: "paged_adamw_32bit"
- **lr_scheduler_type**: "cosine"
- **max_steps**: -1
- **warmup_ratio**: 0.03
- **group_by_length**: `True`
| {"language": ["zh", "en"], "license": "llama3", "datasets": ["KaifengGGG/WenYanWen_English_Parallel"], "metrics": ["bleu", "chrf", "meteor", "bertscore"]} | KaifengGGG/Llama3-8b-Hanscripter | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"zh",
"en",
"dataset:KaifengGGG/WenYanWen_English_Parallel",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:16:17+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-test-ver2
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0883
- Precision: 0.9894
- Recall: 0.9894
- F1: 0.9894
- Accuracy: 0.9894
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1048 | 1.0 | 1195 | 0.0778 | 0.9849 | 0.9849 | 0.9849 | 0.9849 |
| 0.0364 | 2.0 | 2390 | 0.0856 | 0.9824 | 0.9824 | 0.9824 | 0.9824 |
| 0.0218 | 3.0 | 3585 | 0.0863 | 0.9874 | 0.9874 | 0.9874 | 0.9874 |
| 0.0043 | 4.0 | 4780 | 0.0959 | 0.9872 | 0.9872 | 0.9872 | 0.9872 |
| 0.0011 | 5.0 | 5975 | 0.0883 | 0.9894 | 0.9894 | 0.9894 | 0.9894 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large-test-ver2", "results": []}]} | obamaTeo/deberta-v3-large-test-ver2 | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:18:08+00:00 |
automatic-speech-recognition | transformers | {} | Gizachew/whisper-tiny-amharic | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:18:34+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]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | dyaniahealth/preadapted_llama3_8b_corr | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:18:50+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/theGhoul21/OrpoMistral-8B-SRL
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/OrpoMistral-8B-SRL-GGUF/resolve/main/OrpoMistral-8B-SRL.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": [], "base_model": "theGhoul21/OrpoMistral-8B-SRL", "quantized_by": "mradermacher"} | mradermacher/OrpoMistral-8B-SRL-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:theGhoul21/OrpoMistral-8B-SRL",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:18:53+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-finetune
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4927
- eval_wer: 20.7731
- eval_runtime: 53.0796
- eval_samples_per_second: 9.42
- eval_steps_per_second: 1.187
- epoch: 13.6111
- step: 490
## 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: 128
- 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: 500
- training_steps: 1000
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-tiny.en", "model-index": [{"name": "whisper-tiny-finetune", "results": []}]} | anjalirgpt/whisper-tiny-finetune | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny.en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:19:17+00:00 |
automatic-speech-recognition | transformers |
# Model Card for Model ID
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## 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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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
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<!-- Relevant interpretability work for the model goes here -->
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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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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | anjalirgpt/fine-tuned-whisper | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:20:26+00:00 |
null | transformers |
# Model Card for Model ID
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model 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": []} | anjalirgpt/fine-tuned-whisper-proc | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:20:39+00:00 |
text-generation | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | cilantro9246/7bvym3q | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:21:35+00:00 |
text-generation | transformers | Expanded 20B Base for future merges and experiments.
This version works like 13B Tiefighter Version.
Perplexity and evaluations between it and this version are underway.
# D_AU-Tiefighter-4Seater-20B-pass3
D_AU-Tiefighter-4Seater-20B-pass3 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter)
* [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter)
* [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter)
* [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter)
* [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: KoboldAI/LLaMA2-13B-Tiefighter
layer_range: [0, 12]
- sources:
- model: KoboldAI/LLaMA2-13B-Tiefighter
layer_range: [8, 20]
- sources:
- model: KoboldAI/LLaMA2-13B-Tiefighter
layer_range: [16, 28]
- sources:
- model: KoboldAI/LLaMA2-13B-Tiefighter
layer_range: [20, 32]
- sources:
- model: KoboldAI/LLaMA2-13B-Tiefighter
layer_range: [28, 40]
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "DavidAU/D_AU-Tiefighter-4Seater-20B-pass3"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter"], "base_model": ["KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Tiefighter"]} | DavidAU/D_AU-Tiefighter-4Seater-20B-pass3 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"KoboldAI/LLaMA2-13B-Tiefighter",
"base_model:KoboldAI/LLaMA2-13B-Tiefighter",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:21:41+00:00 |
text2text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | ankitgu3/t5_small_pubmed_qa_artificial_finetuned | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:21:51+00:00 |
automatic-speech-recognition | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shtapm/whisper-large_0502_decoder8_200steps | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:22:37+00:00 |
null | null | {"license": "openrail"} | Homiebear/VenomTITAN | null | [
"license:openrail",
"region:us"
] | null | 2024-05-03T02:22:50+00:00 |
|
null | null | {} | Subre-Moktar/AnimalDetectionYolov7Model | null | [
"region:us"
] | null | 2024-05-03T02:23:15+00:00 |
|
feature-extraction | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep45 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T02:24:45+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | golf2248/p1o5cuv | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T02:24:46+00:00 |
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