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Cataloged from mistralai/Mistral-Small-4-119B-2603-NVFP4
All models by mistralai →Mistral Small 4 is a powerful hybrid model capable of acting as both a general instruction model and a reasoning model. It unifies the capabilities of three different model families—Instruct, Reasoning (previously called Magistral), and Devstral—into a single, unified model.
With its multimodal capabilities, efficient architecture, and flexible mode switching, it is a powerful general-purpose model for any task. In a latency-optimized setup, Mistral Small 4 achieves a 40% reduction in end-to-end completion time, and in a throughput-optimized setup, it handles 3x more requests per second compared to Mistral Small 3.
[!Tip] This checkpoint in particular is a post-training-activation quantized version of mistralai/Mistral-Small-4-119B-2603. It was created using llm-compressor as part of a collaboration with teams from vLLM & Red Hat. Special thanks goes out to Dipika Sikka. We also would like to thank NVIDIA for their expertise and contributions to SGLang as well as vLLM to ensure kernel optimizations.
Mistral Small 4 includes the following architectural choices:
Mistral Small 4 offers the following capabilities:
'none' → Do not use reasoning'high' → Use reasoning (recommended for complex prompts)
Use reasoning_effort="high" for complex tasksreasoning_effort="high". Temp between 0.0 and 0.7 for reasoning_effort="none" depending on task.Mistral Small 4 is designed for general chat assistants, coding, agentic tasks, and reasoning tasks (with reasoning mode toggled). Its multimodal capabilities also enable document and image understanding for data extraction and analysis.
Its capabilities are ideal for:
Mistral Small 4 is also well-suited for customization and fine-tuning for more specialized tasks.
You can find Mistral Small 4 support on multiple libraries for inference:
vllm (recommended): See here.SGLang: (WIP ⏳ – follow updates here)We recommend using Mistral Small 4 with the vLLM library for production-ready inference.
Install latest vllm:
Doing so should automatically install mistral_common >= 1.11.0.
To check:
You can also make use of a ready-to-go docker image or on the docker hub.
We recommend a server/client setup:
Mistral Small 4 can follow your instructions to the letter.
Let's solve some equations thanks to our simple Python calculator tool.
Let's see if the Mistral Small 4 knows when to pick a fight !
This model is licensed under the Apache 2.0 License.
You must not use this model in a manner that infringes, misappropriates, or violates any third party’s rights, including intellectual property rights.
uv pip install -U vllmpython -c "import mistral_common; print(mistral_common.__version__)"vllm serve mistralai/Mistral-Small-4-119B-2603-NVFP4 --max-model-len 262144 --tensor-parallel-size 2 --attention-backend TRITON_MLA \ --tool-call-parser mistral --enable-auto-tool-choice --reasoning-parser mistral --max_num_batched_tokens 16384 --max_num_seqs 128 \ --gpu_memory_utilization 0.8from datetime import datetime, timedelta
from openai import OpenAIfrom 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 = 0.1
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")
messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.", },]
response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, reasoning_effort="none",)
assistant_message = response.choices[0].message.contentprint(assistant_message)import jsonfrom datetime import datetime, timedelta
from openai import OpenAIfrom 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 = 0.1
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://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"
def my_calculator(expression: str) -> str: return str(eval(expression))
tools = [ { "type": "function", "function": { "name": "my_calculator", "description": "A calculator that can evaluate a mathematical expression.", "parameters": { "type": "object", "properties": { "expression": { "type": "string", "description": "The mathematical expression to evaluate.", }, }, "required": ["expression"], }, }, }, { "type": "function", "function": { "name": "rewrite", "description": "Rewrite a given text for improved clarity", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The input text to rewrite", } }, }, }, },]
messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "Thanks to your calculator, compute the results for the equations that involve numbers displayed in the image.", }, { "type": "image_url", "image_url": { "url": image_url, }, }, ], },]
response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, tools=tools, tool_choice="auto", reasoning_effort="none",)
tool_calls = response.choices[0].message.tool_calls
results = []for tool_call in tool_calls: function_name = tool_call.function.name function_args = tool_call.function.arguments if function_name == "my_calculator": result = my_calculator(**json.loads(function_args)) results.append(result)
messages.append({"role": "assistant", "tool_calls": tool_calls})for tool_call, result in zip(tool_calls, results): messages.append( { "role": "tool", "tool_call_id": tool_call.id, "name": tool_call.function.name, "content": result, } )
response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, reasoning_effort="none",)
print(response.choices[0].message.content)from datetime import datetime, timedelta
from openai import OpenAIfrom 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 = 0.7
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, reasoning_effort="high",)
print(response.choices[0].message.content)