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leafspark/Mistral-Large-Instruct-2407-GGUF
Mistral-Large-Instruct-2407-GGUF is a text generation model from leafspark. Use it when you need the model to write or continue text. It is set up for ggml. The card lists the license as other.
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Updated Jul 24, 2024
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From the Hugging Face model README

Mistral-Large-Instruct-2407 is an advanced dense Large Language Model (LLM) of 123B parameters with state-of-the-art reasoning, knowledge and coding capabilities.
Quantized with llama.cpp b3452
| Quant | Notes |
|---|---|
| Q2_K | Usable for general inference tasks |
| IQ2_XXS | Ultra-low memory footprint |
| IQ2_S | Optimized for small VRAM environments |
| Q3_K_M | Good balance between speed and accuracy |
| Q3_K_S | Faster inference with minor quality loss |
| Q3_K_L | High-quality with more VRAM requirement |
| Q4_K_M | Superior balance, suitable for production |
| Q4_0 | Basic quantization, good for experimentation |
| Q4_K_S | Fast inference, efficient for scaling |
| Q8_0 | Highest quality |
| Q5_K_M | Higher quality |
| Q5_K_S | High quality |
For more details about this model please refer to Mistral's release blog post.
| Benchmark | Score |
|---|---|
| MMLU | 84.0% |
| Benchmark | Score |
|---|---|
| French | 82.8% |
| German | 81.6% |
| Spanish | 82.7% |
| Italian | 82.7% |
| Dutch | 80.7% |
| Portuguese | 81.6% |
| Russian | 79.0% |
| Korean | 60.1% |
| Japanese | 78.8% |
| Chinese | 74.8% |
| Benchmark | Score |
|---|---|
| MT Bench | 8.63 |
| Wild Bench | 56.3 |
| Arena Hard | 73.2 |
| Benchmark | Score |
|---|---|
| Human Eval | 92% |
| Human Eval Plus | 87% |
| MBPP Base | 80% |
| MBPP Plus | 69% |
| Benchmark | Score |
|---|---|
| GSM8K | 93% |
| Math Instruct (0-shot, no CoT) | 70% |
| Math Instruct (0-shot, CoT) | 71.5% |
The Mistral Large model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Diogo Costa, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall