base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
library_name: vllm
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- llama-cpp
- gguf-my-repo
inference: false
extra_gated_description: >-
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Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q6_K-GGUF
This model was converted to GGUF format from mistralai/Mistral-Small-3.1-24B-Instruct-2503 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks. This model is an instruction-finetuned version of: Mistral-Small-3.1-24B-Base-2503.
Mistral Small 3.1 can be deployed locally and is exceptionally "knowledge-dense," fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.
It is ideal for:
-Fast-response conversational agents.
-Low-latency function calling.
-Subject matter experts via fine-tuning.
-Local inference for hobbyists and organizations handling sensitive data.
-Programming and math reasoning.
-Long document understanding.
-Visual understanding.
For enterprises requiring specialized capabilities (increased context, specific modalities, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
Key Features
-Vision: Vision capabilities enable the model to analyze images and provide insights based on visual content in addition to text.
-Multilingual: Supports dozens of languages,including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi.
-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 128k context window.
-System Prompt: Maintains strong adherence and support for system prompts.
-Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.
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-3.1-24B-Instruct-2503-Q6_K-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q6_k.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q6_K-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q6_k.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-3.1-24B-Instruct-2503-Q6_K-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q6_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q6_K-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q6_k.gguf -c 2048