Create README.md

README.md

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+---
+base_model: TencentARC/Mistral_Pro_8B_v0.1
+language:
+- en
+pipeline_tag: text-generation
+license: apache-2.0
+model_type: mistral
+library_name: transformers
+inference: false
+datasets:
+- HuggingFaceTB/cosmopedia
+---
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/65aa2d4b356bf23b4a4da247/NQAvp6NRHlNILyWWFlrA7.webp)
+## Mistral Pro 8B v0.1
+- **Model creator:** [Google](https://huggingface.co/TencentARC)
+- **Original model:** [gemma-7b-it](https://huggingface.co/TencentARC/Mistral_Pro_8B_v0.1)
+<!-- description start -->
+## Description
+This repo contains GGUF format model files for [TencentARC's Mistral Pro 8B v0.1](https://huggingface.co/TencentARC/Mistral_Pro_8B_v0.1)
+
+## Original model
+- **Developed by:** [TencentARC](https://huggingface.co/TencentARC)
+
+### Description
+#### Model Description
+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.
+
+#### Development and Training
+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.
+
+#### Intended Use
+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.
+
+#### Performance
+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).
+##### Overall Performance on Languages, math and code tasks
+  | Model | ARC | Hellaswag | MMLU | TruthfulQA | Winogrande | GSM8K  | HumanEval | 
+  | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | 
+  | Gemma-7B | 61.9 | 82.2 | 64.6 | 44.8 | 79.0 | 50.9 | 32.3 | 
+  | Mistral-7B | 60.8 | 83.3 | 62.7 | 42.6 | 78.0 | 39.2 | 28.7 |
+  | Mistral_Pro_8B_v0.1 | 63.2 | 82.6 | 60.6 | 48.3 | 78.9 | 50.6 | 32.9 | 
+
+
+#### Limitations
+While Mistral-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.
+
+#### Ethical Considerations
+Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications.
+
+## Quantizon types
+| quantization method | bits | size     | description                                            | recommended |
+|---------------------|------|----------|-----------------------------------------------------|-------------|
+| Q2_K                | 2    | 3.36     | very small, very high quality loss  |  ❌  |
+| Q3_K_S              | 3    | 3.91 GB  | very small, high quality loss                       | ❌         |
+| Q3_K_M              | 3    | 4.35 GB  | small, substantial quality loss  |  ❌  |
+| Q3_K_L              | 3    | 4.74 GB  | small, substantial quality loss                     | ❌         |
+| Q4_0                | 4    | 5.09 GB  | legacy; small, very high quality loss | ❌         |
+| Q4_K_S              | 4    | 5.13 GB  | medium, balanced quality   |  ✅  |
+| Q4_K_M              | 4    | 5.42 GB  | medium, balanced quality              | ✅         |
+| Q5_0                | 5    | 6.20 GB  | legacy; medium, balanced quality  | ❌         |
+| Q5_K_S              | 5    | 6.20 GB  | large, low quality loss | ✅         |
+| Q5_K_M              | 5    | 6.36 GB  | large, very low quality loss | ✅         |
+| Q6_K                | 6    | 7.37 GB  | very large, extremely low quality loss              | ❌         |
+| Q8_0                | 8    | 9.55 GB  | very large, extremely low quality loss | ❌         |
+| FP16                | 16   | 18 GB    | enormous, negligible quality loss |  ❌  |
+
+## Usage
+You can use this model with the latest builds of **LM Studio** and **llama.cpp**.  
+If you're new to the world of _large language models_, I recommend starting with **LM Studio**.
+<!-- description end -->