Triangle104/Mistral-Small-24B-Instruct-2501-Q4_K_M-GGUF

This model was converted to GGUF format from mistralai/Mistral-Small-24B-Instruct-2501 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models! This model is an instruction-fine-tuned version of the base model: Mistral-Small-24B-Base-2501.

Mistral Small can be deployed locally and is exceptionally "knowledge-dense", fitting in a single RTX 4090 or a 32GB RAM MacBook once quantized.

Perfect for:

Fast response conversational agents.
Low latency function calling.
Subject matter experts via fine-tuning.
Local inference for hobbyists and organizations handling sensitive data.

For enterprises that need specialized capabilities (increased context, particular modalities, domain specific knowledge, etc.), we will be releasing commercial models beyond what Mistral AI contributes to the community.

This release demonstrates our commitment to open source, serving as a strong base model.

Learn more about Mistral Small in our blog post.

Model developper: Mistral AI Team

Key Features

Multilingual: Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
Agent-Centric: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
Advanced Reasoning: State-of-the-art conversational and reasoning capabilities.
Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
Context Window: A 32k context window.
System Prompt: Maintains strong adherence and support for system prompts.
Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Mistral-Small-24B-Instruct-2501-Q4_K_M-GGUF --hf-file mistral-small-24b-instruct-2501-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Mistral-Small-24B-Instruct-2501-Q4_K_M-GGUF --hf-file mistral-small-24b-instruct-2501-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Mistral-Small-24B-Instruct-2501-Q4_K_M-GGUF --hf-file mistral-small-24b-instruct-2501-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Mistral-Small-24B-Instruct-2501-Q4_K_M-GGUF --hf-file mistral-small-24b-instruct-2501-q4_k_m.gguf -c 2048