Create README.md

README.md

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+---
+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
+license: apache-2.0
+library_name: vllm
+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>.
+---
+
+# Model Card for Mistral-Small-3.1-24B-Base-2503
+
+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 the base model of [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503).
+
+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.
+
+Learn more about Mistral Small 3.1 in our [blog post](https://mistral.ai/news/mistral-small-3-1/).
+
+## 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, Farshi.
+- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
+- **Context Window:** A 128k context window.
+- **Tokenizer:** Utilizes a Tekken tokenizer with a 131k vocabulary size.
+
+## Benchmark Results
+
+When available, we report numbers previously published by other model providers, otherwise we re-evaluate them using our own evaluation harness.
+
+### Pretrain Evals
+
+| Model                          | MMLU (5-shot) | MMLU Pro (5-shot CoT) | TriviaQA   | GPQA Main (5-shot CoT)| MMMU      |
+|--------------------------------|---------------|-----------------------|------------|-----------------------|-----------|
+| **Small 3.1 24B Base**         | **81.01%**    | **56.03%**            | 80.50%     | **37.50%**            | **59.27%**|
+| Gemma 3 27B PT                 | 78.60%        | 52.20%                | **81.30%** | 24.30%                | 56.10%    |
+
+## Usage Examples
+
+### vLLM (recommended)
+
+We recommend using Mistral-Small 3.1 Base with the [vLLM library](https://github.com/vllm-project/vllm).
+_Note_ however that this is a pretrained-only checkpoint and thus not ready to work as an instruction model out-of-the-box.
+For a production-ready instruction model please use [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503).
+
+**_Installation_**
+
+Make sure you install [`vLLM nightly`](https://github.com/vllm-project/vllm/):
+
+```
+pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly --upgrade
+```
+
+Doing so should automatically install [`mistral_common >= 1.5.4`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.4).
+
+To check:
+```
+python -c "import mistral_common; print(mistral_common.__version__)"
+```
+
+You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39) followed by a nightly install of vllm as shown above.
+
+**_Example_**
+
+```py
+from vllm import LLM
+from vllm.sampling_params import SamplingParams
+from vllm.inputs.data import TokensPrompt
+import requests
+from PIL import Image
+from io import BytesIO
+from vllm.multimodal import MultiModalDataBuiltins
+
+from mistral_common.protocol.instruct.messages import TextChunk, ImageURLChunk
+
+model_name = "mistralai/Mistral-Small-3.1-24B-Base-2503"
+sampling_params = SamplingParams(max_tokens=8192)
+
+llm = LLM(model=model_name, tokenizer_mode="mistral")
+
+url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
+response = requests.get(url)
+image = Image.open(BytesIO(response.content))
+
+prompt = "The image shows a"
+
+user_content = [ImageURLChunk(image_url=url), TextChunk(text=prompt)]
+
+tokenizer = llm.llm_engine.tokenizer.tokenizer.mistral.instruct_tokenizer
+tokens, _ = tokenizer.encode_user_content(user_content, False)
+
+prompt = TokensPrompt(
+    prompt_token_ids=tokens, multi_modal_data=MultiModalDataBuiltins(image=[image])
+)
+outputs = llm.generate(prompt, sampling_params=sampling_params)
+
+print(outputs[0].outputs[0].text)
+# ' scene in Yosemite Valley and was taken at ISO 250 with an aperture of f/16 and a shutter speed of 1/18 second. ...'
+```
+
+### Transformers (untested)
+
+Transformers-compatible model weights are also uploaded (thanks a lot @cyrilvallez). 
+However the transformers implementation was **not throughly tested**, but only on "vibe-checks".
+Hence, we can only ensure 100% correct behavior when using the original weight format with vllm (see above).