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--- |
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base_model: TencentARC/Mistral_Pro_8B_v0.1 |
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language: |
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- en |
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pipeline_tag: text-generation |
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license: apache-2.0 |
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model_type: mistral |
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library_name: transformers |
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inference: false |
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datasets: |
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- HuggingFaceTB/cosmopedia |
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--- |
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## Mistral Pro 8B v0.1 |
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- **Model creator:** [TencentARC](https://huggingface.co/TencentARC) |
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- **Original model:** [Mistral_Pro_8B_v0.1-7b-it](https://huggingface.co/TencentARC/Mistral_Pro_8B_v0.1) |
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## Description |
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This repo contains GGUF format model files for [TencentARC's Mistral Pro 8B v0.1](https://huggingface.co/TencentARC/Mistral_Pro_8B_v0.1) |
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## Original model |
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- **Developed by:** [TencentARC](https://huggingface.co/TencentARC) |
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### Description |
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#### Model Description |
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Mistral-Pro is a progressive version of the original [Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1) model, enhanced by the addition of Transformer blocks. It specializes in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics. |
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#### Development and Training |
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Developed by Tencent's ARC Lab, Mistral-Pro is an 8 billion parameter model. It's an expansion of Mistral-7B, further trained on code and math corpora. |
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#### Intended Use |
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This model is designed for a wide range of NLP tasks, with a focus on programming, mathematics, and general language tasks. It suits scenarios requiring integration of natural and programming languages. |
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#### Performance |
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Mistral_Pro_8B_v0.1 showcases superior performance on a range of benchmarks. It enhances the code and math performance of Mistral. Furthermore, it matches the performance of the recently dominant model, [Gemma](https://huggingface.co/google/gemma-7b). |
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##### Overall Performance on Languages, math and code tasks |
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| Model | ARC | Hellaswag | MMLU | TruthfulQA | Winogrande | GSM8K | HumanEval | |
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| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | |
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| Gemma-7B | 61.9 | 82.2 | 64.6 | 44.8 | 79.0 | 50.9 | 32.3 | |
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| Mistral-7B | 60.8 | 83.3 | 62.7 | 42.6 | 78.0 | 39.2 | 28.7 | |
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| Mistral_Pro_8B_v0.1 | 63.2 | 82.6 | 60.6 | 48.3 | 78.9 | 50.6 | 32.9 | |
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#### Limitations |
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While Mistral-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks. |
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#### Ethical Considerations |
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Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications. |
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## Quantizon types |
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| quantization method | bits | size | description | recommended | |
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|---------------------|------|----------|-----------------------------------------------------|-------------| |
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| Q2_K | 2 | 3.36 | very small, very high quality loss | ❌ | |
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| Q3_K_S | 3 | 3.91 GB | very small, high quality loss | ❌ | |
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| Q3_K_M | 3 | 4.35 GB | small, substantial quality loss | ❌ | |
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| Q3_K_L | 3 | 4.74 GB | small, substantial quality loss | ❌ | |
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| Q4_0 | 4 | 5.09 GB | legacy; small, very high quality loss | ❌ | |
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| Q4_K_S | 4 | 5.13 GB | medium, balanced quality | ✅ | |
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| Q4_K_M | 4 | 5.42 GB | medium, balanced quality | ✅ | |
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| Q5_0 | 5 | 6.20 GB | legacy; medium, balanced quality | ❌ | |
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| Q5_K_S | 5 | 6.20 GB | large, low quality loss | ✅ | |
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| Q5_K_M | 5 | 6.36 GB | large, very low quality loss | ✅ | |
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| Q6_K | 6 | 7.37 GB | very large, extremely low quality loss | ❌ | |
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| Q8_0 | 8 | 9.55 GB | very large, extremely low quality loss | ❌ | |
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| FP16 | 16 | 18 GB | enormous, negligible quality loss | ❌ | |
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## Usage |
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You can use this model with the latest builds of **LM Studio** and **llama.cpp**. |
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If you're new to the world of _large language models_, I recommend starting with **LM Studio**. |
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