Ministral-8B-Instruct-2410-GGUF

GGUF quants

These are (early testing) q4_k_m GGUF quants of Mistral/Ministral-8B-Instruct-2410.

Made with llama.cpp b3634, slightly modified.

They are for research use e.g. in llama.cpp and wrappers (like ollama), as covered by mrl license, as below.

Note that until llama.cpp implements sliding window, probably best use it with a context size <= 2k.

Original model card below.

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Model Card for Ministral-8B-Instruct-2410

We introduce two new state-of-the-art models for local intelligence, on-device computing, and at-the-edge use cases. We call them les Ministraux: Ministral 3B and Ministral 8B.

The Ministral-8B-Instruct-2410 Language Model is an instruct fine-tuned model significantly outperforming existing models of similar size, released under the Mistral Research License.

If you are interested in using Ministral-3B or Ministral-8B commercially, outperforming Mistral-7B, reach out to us.

For more details about les Ministraux please refer to our release blog post.

Ministral 8B Key features

• Released under the Mistral Research License, reach out to us for a commercial license
• Trained with a 128k context window with interleaved sliding-window attention
• Trained on a large proportion of multilingual and code data
• Supports function calling
• Vocabulary size of 131k, using the V3-Tekken tokenizer

Basic Instruct Template (V3-Tekken)

<s>[INST]user message[/INST]assistant response</s>[INST]new user message[/INST]

For more information about the tokenizer please refer to mistral-common

Ministral 8B Architecture

Benchmarks

Base Models

<u>Knowledge & Commonsense</u>

<u>Code & Math</u>

<u>Multilingual</u>

Instruct Models

<u>Chat/Arena (gpt-4o judge)</u>

<u>Code & Math</u>

<u>Function calling</u>

Usage Examples

vLLM (recommended)

We recommend using this model with the vLLM library to implement production-ready inference pipelines.

[!IMPORTANT] Currently vLLM is capped at 32k context size because interleaved attention kernels for paged attention are not yet implemented in vLLM. Attention kernels for paged attention are being worked on and as soon as it is fully supported in vLLM, this model card will be updated. To take advantage of the full 128k context size we recommend Mistral Inference

Installation

Make sure you install vLLM >= v0.6.2:

pip install --upgrade vllm

Also make sure you have mistral_common >= 1.4.4 installed:

pip install --upgrade mistral_common

You can also make use of a ready-to-go docker image.

Offline

from vllm import LLM
from vllm.sampling_params import SamplingParams

model_name = "mistralai/Ministral-8B-Instruct-2410"

sampling_params = SamplingParams(max_tokens=8192)

# note that running Ministral 8B on a single GPU requires 24 GB of GPU RAM
# If you want to divide the GPU requirement over multiple devices, please add *e.g.* `tensor_parallel=2`
llm = LLM(model=model_name, tokenizer_mode="mistral", config_format="mistral", load_format="mistral")

prompt = "Do we need to think for 10 seconds to find the answer of 1 + 1?"

messages = [
    {
        "role": "user",
        "content": prompt
    },
]

outputs = llm.chat(messages, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)
# You don't need to think for 10 seconds to find the answer to 1 + 1. The answer is 2,
# and you can easily add these two numbers in your mind very quickly without any delay.

Server

You can also use Ministral-8B in a server/client setting.

1. Spin up a server:

vllm serve mistralai/Ministral-8B-Instruct-2410 --tokenizer_mode mistral --config_format mistral --load_format mistral

Note: Running Ministral-8B on a single GPU requires 24 GB of GPU RAM.

If you want to divide the GPU requirement over multiple devices, please add e.g. --tensor_parallel=2

1. And ping the client:

curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer token' \
--data '{
    "model": "mistralai/Ministral-8B-Instruct-2410",
    "messages": [
      {
        "role": "user",
        "content": "Do we need to think for 10 seconds to find the answer of 1 + 1?"
      }
    ]
}'

Mistral-inference

We recommend using mistral-inference to quickly try out / "vibe-check" the model.

Install

Make sure to have mistral_inference >= 1.5.0 installed.

pip install mistral_inference --upgrade

Download

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', '8B-Instruct')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Ministral-8B-Instruct-2410", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)

Chat

After installing mistral_inference, a mistral-chat CLI command should be available in your environment. You can chat with the model using

mistral-chat $HOME/mistral_models/8B-Instruct --instruct --max_tokens 256

Passkey detection

[!IMPORTANT] In this example the passkey message has over >100k tokens and mistral-inference does not have a chunked pre-fill mechanism. Therefore you will need a lot of GPU memory in order to run the below example (80 GB). For a more memory-efficient solution we recommend using vLLM.

from mistral_inference.transformer import Transformer
from pathlib import Path
import json
from mistral_inference.generate import generate
from huggingface_hub import hf_hub_download

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest

def load_passkey_request() -> ChatCompletionRequest:
    passkey_file = hf_hub_download(repo_id="mistralai/Ministral-8B-Instruct-2410", filename="passkey_example.json")

    with open(passkey_file, "r") as f:
        data = json.load(f)

    message_content = data["messages"][0]["content"]
    return ChatCompletionRequest(messages=[UserMessage(content=message_content)])

tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path, softmax_fp32=False)

completion_request = load_passkey_request()

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)  # The pass key is 13005.

Instruct following

from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest


tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path)

completion_request = ChatCompletionRequest(messages=[UserMessage(content="How often does the letter r occur in Mistral?")])

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)

Function calling

from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy


tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
tekken = tokenizer.instruct_tokenizer.tokenizer
tekken.special_token_policy = SpecialTokenPolicy.IGNORE

model = Transformer.from_folder(mistral_models_path)

completion_request = ChatCompletionRequest(
    tools=[
        Tool(
            function=Function(
                name="get_current_weather",
                description="Get the current weather",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "format": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"],
                            "description": "The temperature unit to use. Infer this from the users location.",
                        },
                    },
                    "required": ["location", "format"],
                },
            )
        )
    ],
    messages=[
        UserMessage(content="What's the weather like today in Paris?"),
        ],
)

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)

The Mistral AI Team

Albert Jiang, Alexandre Abou Chahine, Alexandre Sablayrolles, Alexis Tacnet, Alodie Boissonnet, Alok Kothari, Amélie Héliou, Andy Lo, Anna Peronnin, Antoine Meunier, Antoine Roux, Antonin Faure, Aritra Paul, Arthur Darcet, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Avinash Sooriyarachchi, Baptiste Rozière, Barry Conklin, Bastien Bouillon, Blanche Savary de Beauregard, Carole Rambaud, Caroline Feldman, Charles de Freminville, Charline Mauro, Chih-Kuan Yeh, Chris Bamford, Clement Auguy, Corentin Heintz, Cyriaque Dubois, Devendra Singh Chaplot, Diego Las Casas, Diogo Costa, Eléonore Arcelin, Emma Bou Hanna, Etienne Metzger, Fanny Olivier Autran, Francois Lesage, Garance Gourdel, Gaspard Blanchet, Gaspard Donada Vidal, Gianna Maria Lengyel, Guillaume Bour, Guillaume Lample, Gustave Denis, Harizo Rajaona, Himanshu Jaju, Ian Mack, Ian Mathew, Jean-Malo Delignon, Jeremy Facchetti, Jessica Chudnovsky, Joachim Studnia, Justus Murke, Kartik Khandelwal, Kenneth Chiu, Kevin Riera, Leonard Blier, Leonard Suslian, Leonardo Deschaseaux, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Sophia Yang, Margaret Jennings, Marie Pellat, Marie Torelli, Marjorie Janiewicz, Mathis Felardos, Maxime Darrin, Michael Hoff, Mickaël Seznec, Misha Jessel Kenyon, Nayef Derwiche, Nicolas Carmont Zaragoza, Nicolas Faurie, Nicolas Moreau, Nicolas Schuhl, Nikhil Raghuraman, Niklas Muhs, Olivier de Garrigues, Patricia Rozé, Patricia Wang, Patrick von Platen, Paul Jacob, Pauline Buche, Pavankumar Reddy Muddireddy, Perry Savas, Pierre Stock, Pravesh Agrawal, Renaud de Peretti, Romain Sauvestre, Romain Sinthe, Roman Soletskyi, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Soham Ghosh, Sylvain Regnier, Szymon Antoniak, Teven Le Scao, Theophile Gervet, Thibault Schueller, Thibaut Lavril, Thomas Wang, Timothée Lacroix, Valeriia Nemychnikova, Wendy Shang, William El Sayed, William Marshall

Model Card for Ministral-8B-Instruct-2410

We introduce two new state-of-the-art models for local intelligence, on-device computing, and at-the-edge use cases. We call them les Ministraux: Ministral 3B and Ministral 8B.

The Ministral-8B-Instruct-2410 Language Model is an instruct fine-tuned model significantly outperforming existing models of similar size, released under the Mistral Research License.

If you are interested in using Ministral-3B or Ministral-8B commercially, outperforming Mistral-7B, reach out to us.

For more details about les Ministraux please refer to our release blog post.

Ministral 8B Key features

• Released under the Mistral Research License, reach out to us for a commercial license
• Trained with a 128k context window with interleaved sliding-window attention
• Trained on a large proportion of multilingual and code data
• Supports function calling
• Vocabulary size of 131k, using the V3-Tekken tokenizer

Basic Instruct Template (V3-Tekken)

<s>[INST]user message[/INST]assistant response</s>[INST]new user message[/INST]

For more information about the tokenizer please refer to mistral-common

Ministral 8B Architecture

Benchmarks

Base Models

<u>Knowledge & Commonsense</u>

<u>Code & Math</u>

<u>Multilingual</u>

Instruct Models

<u>Chat/Arena (gpt-4o judge)</u>

<u>Code & Math</u>

<u>Function calling</u>

Usage Examples

vLLM (recommended)

We recommend using this model with the vLLM library to implement production-ready inference pipelines.

[!IMPORTANT] Currently vLLM is capped at 32k context size because interleaved attention kernels for paged attention are not yet implemented in vLLM. Attention kernels for paged attention are being worked on and as soon as it is fully supported in vLLM, this model card will be updated. To take advantage of the full 128k context size we recommend Mistral Inference

Installation

Make sure you install vLLM >= v0.6.2:

pip install --upgrade vllm

Also make sure you have mistral_common >= 1.4.4 installed:

pip install --upgrade mistral_common

You can also make use of a ready-to-go docker image.

Offline

from vllm import LLM
from vllm.sampling_params import SamplingParams

model_name = "mistralai/Ministral-8B-Instruct-2410"

sampling_params = SamplingParams(max_tokens=8192)

# note that running Ministral 8B on a single GPU requires 24 GB of GPU RAM
# If you want to divide the GPU requirement over multiple devices, please add *e.g.* `tensor_parallel=2`
llm = LLM(model=model_name, tokenizer_mode="mistral", config_format="mistral", load_format="mistral")

prompt = "Do we need to think for 10 seconds to find the answer of 1 + 1?"

messages = [
    {
        "role": "user",
        "content": prompt
    },
]

outputs = llm.chat(messages, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)
# You don't need to think for 10 seconds to find the answer to 1 + 1. The answer is 2,
# and you can easily add these two numbers in your mind very quickly without any delay.

Server

You can also use Ministral-8B in a server/client setting.

1. Spin up a server:

vllm serve mistralai/Ministral-8B-Instruct-2410 --tokenizer_mode mistral --config_format mistral --load_format mistral

Note: Running Ministral-8B on a single GPU requires 24 GB of GPU RAM.

If you want to divide the GPU requirement over multiple devices, please add e.g. --tensor_parallel=2

1. And ping the client:

curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer token' \
--data '{
    "model": "mistralai/Ministral-8B-Instruct-2410",
    "messages": [
      {
        "role": "user",
        "content": "Do we need to think for 10 seconds to find the answer of 1 + 1?"
      }
    ]
}'

Mistral-inference

We recommend using mistral-inference to quickly try out / "vibe-check" the model.

Install

Make sure to have mistral_inference >= 1.5.0 installed.

pip install mistral_inference --upgrade

Download

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', '8B-Instruct')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Ministral-8B-Instruct-2410", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)

Chat

After installing mistral_inference, a mistral-chat CLI command should be available in your environment. You can chat with the model using

mistral-chat $HOME/mistral_models/8B-Instruct --instruct --max_tokens 256

Passkey detection

[!IMPORTANT] In this example the passkey message has over >100k tokens and mistral-inference does not have a chunked pre-fill mechanism. Therefore you will need a lot of GPU memory in order to run the below example (80 GB). For a more memory-efficient solution we recommend using vLLM.

from mistral_inference.transformer import Transformer
from pathlib import Path
import json
from mistral_inference.generate import generate
from huggingface_hub import hf_hub_download

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest

def load_passkey_request() -> ChatCompletionRequest:
    passkey_file = hf_hub_download(repo_id="mistralai/Ministral-8B-Instruct-2410", filename="passkey_example.json")

    with open(passkey_file, "r") as f:
        data = json.load(f)

    message_content = data["messages"][0]["content"]
    return ChatCompletionRequest(messages=[UserMessage(content=message_content)])

tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path, softmax_fp32=False)

completion_request = load_passkey_request()

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)  # The pass key is 13005.

Instruct following

from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest


tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path)

completion_request = ChatCompletionRequest(messages=[UserMessage(content="How often does the letter r occur in Mistral?")])

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)

Function calling

from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy


tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
tekken = tokenizer.instruct_tokenizer.tokenizer
tekken.special_token_policy = SpecialTokenPolicy.IGNORE

model = Transformer.from_folder(mistral_models_path)

completion_request = ChatCompletionRequest(
    tools=[
        Tool(
            function=Function(
                name="get_current_weather",
                description="Get the current weather",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "format": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"],
                            "description": "The temperature unit to use. Infer this from the users location.",
                        },
                    },
                    "required": ["location", "format"],
                },
            )
        )
    ],
    messages=[
        UserMessage(content="What's the weather like today in Paris?"),
        ],
)

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)

The Mistral AI Team

Albert Jiang, Alexandre Abou Chahine, Alexandre Sablayrolles, Alexis Tacnet, Alodie Boissonnet, Alok Kothari, Amélie Héliou, Andy Lo, Anna Peronnin, Antoine Meunier, Antoine Roux, Antonin Faure, Aritra Paul, Arthur Darcet, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Avinash Sooriyarachchi, Baptiste Rozière, Barry Conklin, Bastien Bouillon, Blanche Savary de Beauregard, Carole Rambaud, Caroline Feldman, Charles de Freminville, Charline Mauro, Chih-Kuan Yeh, Chris Bamford, Clement Auguy, Corentin Heintz, Cyriaque Dubois, Devendra Singh Chaplot, Diego Las Casas, Diogo Costa, Eléonore Arcelin, Emma Bou Hanna, Etienne Metzger, Fanny Olivier Autran, Francois Lesage, Garance Gourdel, Gaspard Blanchet, Gaspard Donada Vidal, Gianna Maria Lengyel, Guillaume Bour, Guillaume Lample, Gustave Denis, Harizo Rajaona, Himanshu Jaju, Ian Mack, Ian Mathew, Jean-Malo Delignon, Jeremy Facchetti, Jessica Chudnovsky, Joachim Studnia, Justus Murke, Kartik Khandelwal, Kenneth Chiu, Kevin Riera, Leonard Blier, Leonard Suslian, Leonardo Deschaseaux, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Sophia Yang, Margaret Jennings, Marie Pellat, Marie Torelli, Marjorie Janiewicz, Mathis Felardos, Maxime Darrin, Michael Hoff, Mickaël Seznec, Misha Jessel Kenyon, Nayef Derwiche, Nicolas Carmont Zaragoza, Nicolas Faurie, Nicolas Moreau, Nicolas Schuhl, Nikhil Raghuraman, Niklas Muhs, Olivier de Garrigues, Patricia Rozé, Patricia Wang, Patrick von Platen, Paul Jacob, Pauline Buche, Pavankumar Reddy Muddireddy, Perry Savas, Pierre Stock, Pravesh Agrawal, Renaud de Peretti, Romain Sauvestre, Romain Sinthe, Roman Soletskyi, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Soham Ghosh, Sylvain Regnier, Szymon Antoniak, Teven Le Scao, Theophile Gervet, Thibault Schueller, Thibaut Lavril, Thomas Wang, Timothée Lacroix, Valeriia Nemychnikova, Wendy Shang, William El Sayed, William Marshall

Provenance and EU AI Act Art. 53 note

• Upstream model: mistralai/Ministral-8B-Instruct-2410 — published by mistralai.
• Upstream licence: other. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.