--- title: Mistral-Large-3-675B-Instruct-2512-Eagle canonical_url: "https://www.modelscope.cn/models/unsloth/Mistral-Large-3-675B-Instruct-2512-Eagle" md_url: "https://www.modelscope.cn/models/unsloth/Mistral-Large-3-675B-Instruct-2512-Eagle.md" repository: unsloth/Mistral-Large-3-675B-Instruct-2512-Eagle last_updated: 2025-12-07 license: apache-2.0 base_model: - mistralai/Mistral-Large-3-675B-Instruct-2512 base_model_relation: finetune parameters: 11.7B tensor_type: - F8_E4M3 - BF16 library_name: - safetensors - pytorch frameworks: - pytorch language: - en - fr - es - de - it - pt - nl - zh - ja - ko - ar downloads: 102 stars: 0 tags: - mistral-common - mistral --- # Mistral-Large-3-675B-Instruct-2512-Eagle > Mistral-Large-3-675B-Instruct-2512-Eagle - unsloth 在 ModelScope 开源的模型。Mistral Large 3 675B Instruct 2512 Eagle unsloth/Mistral-Large-3-675B-Instruct-2512-Eagle 是 ModelScope 魔搭社区上的 11.7B 参数机器学习模型，采用 apache-2.0 许可，基于 mistralai/Mistral-Large-3-675B-Instruct-2512 构建。 - **Repository**: unsloth/Mistral-Large-3-675B-Instruct-2512-Eagle - **License**: apache-2.0 - **Parameters**: 11.7B - **Base model**: mistralai/Mistral-Large-3-675B-Instruct-2512 - **Tags**: mistral-common, mistral - **Downloads**: 102 - **Stars**: 0 - **Last updated**: 2025-12-07 Source: https://www.modelscope.cn/models/unsloth/Mistral-Large-3-675B-Instruct-2512-Eagle --- # Mistral Large 3 675B Instruct 2512 Eagle This model is the Eagle speculator for [Mistral Large 3 Instruct](https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512). Depending on the task, you can expect noticeable speed-ups on your generations. ## Mistral Large 3 675B Instruct 2512 From our family of large models, **Mistral Large 3** is a state-of-the-art general-purpose **Multimodal granular Mixture-of-Experts** model with **41B active parameters** and **675B total parameters** trained from the ground up with 3000 H200s This model is the instruct post-trained version in **FP8**, fine-tuned for instruction tasks, making it ideal for chat, agentic and instruction based use cases. Designed for reliability and long-context comprehension - It is engineered for production-grade assistants, retrieval-augmented systems, scientific workloads, and complex enterprise workflows. Mistral Large 3 is deployable on-premises in: - [FP8](https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512) on a single node of B200s or H200s. - [NVFP4](https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512-NVFP4) on a single node of H100s or A100s. We provide a [BF16](https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512-BF16) version if needed. ### Key Features Mistral Large 3 consists of two main architectural components: - **A Granular MoE Language Model with 673B params and 39B active** - **A 2.5B Vision Encoder** The Mistral Large 3 Instruct model offers the following capabilities: - **Vision**: Enables the model to analyze images and provide insights based on visual content, in addition to text. - **Multilingual**: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic. - **System Prompt**: Maintains strong adherence and support for system prompts. - **Agentic**: Offers best-in-class agentic capabilities with native function calling and JSON outputting. - **Frontier**: Delivers best-in-class performance. - **Apache 2.0 License**: Open-source license allowing usage and modification for both commercial and non-commercial purposes. - **Large Context Window**: Supports a 256k context window. ### Recommended Settings We recommend deploying Large 3 in a client-server configuration with the following best practices: - **System Prompt**: Define a clear environment and use case, including guidance on how to effectively leverage tools in agentic systems. - **Sampling Parameters**: Use a temperature below 0.1 for daily-driver and production environments ; Higher temperatures may be explored for creative use cases - developers are encouraged to experiment with alternative settings. - **Tools**: Keep the set of tools well-defined and limit their number to the minimum required for the use case - Avoiding overloading the model with an excessive number of tools. - **Vision**: When deploying with vision capabilities, we recommend maintaining an aspect ratio close to 1:1 (width-to-height) for images. Avoiding the use of overly thin or wide images - crop them as needed to ensure optimal performance. ## Usage The model can be used with the following frameworks; - [`vllm`](https://github.com/vllm-project/vllm): See [here](#vllm) ### vLLM We recommend using this model with [vLLM](https://github.com/vllm-project/vllm). #### Installation Make sure to install **vllm >= 1.12.0**: ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.8.6`](https://github.com/mistralai/mistral-common/releases/tag/v1.8.6). 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/docker/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest). #### Serve We recommend that you use Mistral Large 3 in a server/client setting. 1. Spin up a server: ```bash vllm serve mistralai/Mistral-Large-3-675B-Instruct-2512 \ --tensor-parallel-size 8 \ --load-format mistral \ --tokenizer-mode mistral \ --config-format mistral \ --enable-auto-tool-choice \ --tool-call-parser mistral \ --limit-mm-per-prompt '{"image": 10}' \ --speculative_config '{ "model": "mistralai/Mistral-Large-3-675B-Instruct-2512-Eagle", "num_speculative_tokens": 3, "method": "eagle", "max_model_len": "16384" }' ``` **Note:** Running mistralai/Mistral-Large-3-675B-Instruct-2512 on GPU requires a node of B200s or H200s. 2. To ping the client you can use a simple Python snippet. You can find an example below. ```py from datetime import datetime, timedelta from openai import OpenAI from huggingface_hub import hf_hub_download # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" TEMP = 0.15 MAX_TOK = 262144 client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) models = client.models.list() model = models.data[0].id def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() today = datetime.today().strftime("%Y-%m-%d") yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d") model_name = repo_id.split("/")[-1] return system_prompt.format(name=model_name, today=today, yesterday=yesterday) SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438" messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.", }, {"type": "image_url", "image_url": {"url": image_url}}, ], }, ] response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, max_tokens=MAX_TOK, ) print(response.choices[0].message.content) ``` ## License This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt). *You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.*