RenCoder-Ministral-8B-Instruct-2410

RenCoder-Ministral-8B-Instruct-2410 is fine-tuned version of mistralai/Ministral-8B-Instruct-2410 on muliple agentic coding datasets.

"Obsessed with building Open Source AGI, So am I ! Let's create together 🚀 https://www.linkedin.com/in/pankajam"

It achieves the following results on the evaluation set:

Loss: 0.1539

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 106
training_steps: 1068

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0	0	0.4748
0.1752	0.9988	356	0.1681
0.1518	1.9960	712	0.1557
0.1284	2.9932	1068	0.1539

Framework versions

PEFT 0.18.0
Transformers 4.57.1
Pytorch 2.9.1+cu128
Datasets 4.4.1
Tokenizers 0.22.1

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

Feature	Value
Architecture	Dense Transformer
Parameters	8,019,808,256
Layers	36
Heads	32
Dim	4096
KV Heads (GQA)	8
Hidden Dim	12288
Head Dim	128
Vocab Size	131,072
Context Length	128k
Attention Pattern	Ragged (128k,32k,32k,32k)

Benchmarks

Base Models

Knowledge & Commonsense

Model	MMLU	AGIEval	Winogrande	Arc-c	TriviaQA
Mistral 7B Base	62.5	42.5	74.2	67.9	62.5
Llama 3.1 8B Base	64.7	44.4	74.6	46.0	60.2
Ministral 8B Base	65.0	48.3	75.3	71.9	65.5

Gemma 2 2B Base	52.4	33.8	68.7	42.6	47.8
Llama 3.2 3B Base	56.2	37.4	59.6	43.1	50.7
Ministral 3B Base	60.9	42.1	72.7	64.2	56.7

Code & Math

Model	HumanEval pass@1	GSM8K maj@8
Mistral 7B Base	26.8	32.0
Llama 3.1 8B Base	37.8	42.2
Ministral 8B Base	34.8	64.5

Gemma 2 2B	20.1	35.5
Llama 3.2 3B	14.6	33.5
Ministral 3B	34.2	50.9

Multilingual

Model	French MMLU	German MMLU	Spanish MMLU
Mistral 7B Base	50.6	49.6	51.4
Llama 3.1 8B Base	50.8	52.8	54.6
Ministral 8B Base	57.5	57.4	59.6

Gemma 2 2B Base	41.0	40.1	41.7
Llama 3.2 3B Base	42.3	42.2	43.1
Ministral 3B Base	49.1	48.3	49.5

Instruct Models

Chat/Arena (gpt-4o judge)

Model	MTBench	Arena Hard	Wild bench
Mistral 7B Instruct v0.3	6.7	44.3	33.1
Llama 3.1 8B Instruct	7.5	62.4	37.0
Gemma 2 9B Instruct	7.6	68.7	43.8
Ministral 8B Instruct	8.3	70.9	41.3

Gemma 2 2B Instruct	7.5	51.7	32.5
Llama 3.2 3B Instruct	7.2	46.0	27.2
Ministral 3B Instruct	8.1	64.3	36.3

Code & Math

Model	MBPP pass@1	HumanEval pass@1	Math maj@1
Mistral 7B Instruct v0.3	50.2	38.4	13.2
Gemma 2 9B Instruct	68.5	67.7	47.4
Llama 3.1 8B Instruct	69.7	67.1	49.3
Ministral 8B Instruct	70.0	76.8	54.5

Gemma 2 2B Instruct	54.5	42.7	22.8
Llama 3.2 3B Instruct	64.6	61.0	38.4
Ministral 3B Instruct	67.7	77.4	51.7

Function calling

Model	Internal bench
Mistral 7B Instruct v0.3	6.9
Llama 3.1 8B Instruct	N/A
Gemma 2 9B Instruct	N/A
Ministral 8B Instruct	31.6

Gemma 2 2B Instruct	N/A
Llama 3.2 3B Instruct	N/A
Ministral 3B Instruct	28.4

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 >= v0.6.4:

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.

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

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

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

Feature	Value
Architecture	Dense Transformer
Parameters	8,019,808,256
Layers	36
Heads	32
Dim	4096
KV Heads (GQA)	8
Hidden Dim	12288
Head Dim	128
Vocab Size	131,072
Context Length	128k
Attention Pattern	Ragged (128k,32k,32k,32k)

Benchmarks

Base Models

Knowledge & Commonsense

Model	MMLU	AGIEval	Winogrande	Arc-c	TriviaQA
Mistral 7B Base	62.5	42.5	74.2	67.9	62.5
Llama 3.1 8B Base	64.7	44.4	74.6	46.0	60.2
Ministral 8B Base	65.0	48.3	75.3	71.9	65.5

Gemma 2 2B Base	52.4	33.8	68.7	42.6	47.8
Llama 3.2 3B Base	56.2	37.4	59.6	43.1	50.7
Ministral 3B Base	60.9	42.1	72.7	64.2	56.7

Code & Math

Model	HumanEval pass@1	GSM8K maj@8
Mistral 7B Base	26.8	32.0
Llama 3.1 8B Base	37.8	42.2
Ministral 8B Base	34.8	64.5

Gemma 2 2B	20.1	35.5
Llama 3.2 3B	14.6	33.5
Ministral 3B	34.2	50.9

Multilingual

Model	French MMLU	German MMLU	Spanish MMLU
Mistral 7B Base	50.6	49.6	51.4
Llama 3.1 8B Base	50.8	52.8	54.6
Ministral 8B Base	57.5	57.4	59.6

Gemma 2 2B Base	41.0	40.1	41.7
Llama 3.2 3B Base	42.3	42.2	43.1
Ministral 3B Base	49.1	48.3	49.5

Instruct Models

Chat/Arena (gpt-4o judge)

Model	MTBench	Arena Hard	Wild bench
Mistral 7B Instruct v0.3	6.7	44.3	33.1
Llama 3.1 8B Instruct	7.5	62.4	37.0
Gemma 2 9B Instruct	7.6	68.7	43.8
Ministral 8B Instruct	8.3	70.9	41.3

Gemma 2 2B Instruct	7.5	51.7	32.5
Llama 3.2 3B Instruct	7.2	46.0	27.2
Ministral 3B Instruct	8.1	64.3	36.3

Code & Math

Model	MBPP pass@1	HumanEval pass@1	Math maj@1
Mistral 7B Instruct v0.3	50.2	38.4	13.2
Gemma 2 9B Instruct	68.5	67.7	47.4
Llama 3.1 8B Instruct	69.7	67.1	49.3
Ministral 8B Instruct	70.0	76.8	54.5

Gemma 2 2B Instruct	54.5	42.7	22.8
Llama 3.2 3B Instruct	64.6	61.0	38.4
Ministral 3B Instruct	67.7	77.4	51.7

Function calling

Model	Internal bench
Mistral 7B Instruct v0.3	6.9
Llama 3.1 8B Instruct	N/A
Gemma 2 9B Instruct	N/A
Ministral 8B Instruct	31.6

Gemma 2 2B Instruct	N/A
Llama 3.2 3B Instruct	N/A
Ministral 3B Instruct	28.4

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 >= v0.6.4:

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.

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

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)

pankajmathur/RenCoder-Ministral-8B-Instruct-2410

RenCoder-Ministral-8B-Instruct-2410

Training procedure

Training hyperparameters

Training results

Framework versions

Basic Instruct Template (V3-Tekken)

Ministral 8B Architecture

Benchmarks

Base Models

Instruct Models

Usage Examples

vLLM (recommended)

Mistral-inference

Chat

Passkey detection

Instruct following

Function calling

The Mistral AI Team

Basic Instruct Template (V3-Tekken)

Ministral 8B Architecture

Benchmarks

Base Models

Instruct Models

Usage Examples

vLLM (recommended)

Mistral-inference

Chat

Passkey detection

Instruct following

Function calling

The Mistral AI Team

为此模型做种