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Mistral-Large-Instruct-2407-GGUF

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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

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Dataset used to train leafspark/Mistral-Large-Instruct-2407-GGUF