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Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF

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GGUF
vllm
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gguf-my-repo
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Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF
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---
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: If you want to learn more about how we process your personal
  data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---

# Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF
This model was converted to GGUF format from [`mistralai/Mistral-Small-3.1-24B-Instruct-2503`](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) 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)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -c 2048
```