metadata

license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
language:
  - en
  - fr
  - de
  - es
  - it
  - pt
  - zh
  - ja
  - ru
  - ko
library_name: ggml
tags:
  - mistral
  - gguf
pipeline_tag: text-generation
datasets:
  - froggeric/imatrix

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