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sayhan/Mistral_Pro_8B_v0.1-GGUF
Mistral_Pro_8B_v0.1-GGUF is a text generation model from sayhan. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
- Model creator: TencentARC - Original model: MistralPro8Bv0.1-7b-it This repo contains GGUF format model files for TencentARC's Mistral Pro 8B v0.1
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From the Hugging Face model README
This repo contains GGUF format model files for TencentARC's Mistral Pro 8B v0.1
Mistral-Pro is a progressive version of the original Mistral 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.
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.
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.
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.
| 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 |
While Mistral-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.
Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications.
| 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 | ❌ |
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.