Mistral-Large-Instruct-2407-GGUF
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.
Key features
- Multi-lingual by design: Dozens of languages supported, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch and Polish.
- Proficient in coding: Trained on 80+ coding languages such as Python, Java, C, C++, Javacsript, and Bash. Also trained on more specific languages such as Swift and Fortran.
- Agentic-centric: Best-in-class agentic capabilities with native function calling and JSON outputting.
- Advanced Reasoning: State-of-the-art mathematical and reasoning capabilities.
- Mistral Research License: Allows usage and modification for research and non-commercial usages.
- Large Context: A large 128k context window.
Metrics
Base Pretrained Benchmarks
| Benchmark | Score |
|---|---|
| MMLU | 84.0% |
Base Pretrained Multilingual Benchmarks (MMLU)
| 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% |
Instruction Benchmarks
| Benchmark | Score |
|---|---|
| MT Bench | 8.63 |
| Wild Bench | 56.3 |
| Arena Hard | 73.2 |
Code & Reasoning Benchmarks
| Benchmark | Score |
|---|---|
| Human Eval | 92% |
| Human Eval Plus | 87% |
| MBPP Base | 80% |
| MBPP Plus | 69% |
Math Benchmarks
| Benchmark | Score |
|---|---|
| GSM8K | 93% |
| Math Instruct (0-shot, no CoT) | 70% |
| Math Instruct (0-shot, CoT) | 71.5% |
Limitations
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.
The Mistral AI Team
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