Ministral-3-3B-Reasoning-UNCENSORED-heretic API & Inference Endpoint

Arbitrary-Rank Ablation (ARA) parameters

Table with columns: Parameter, Value
Parameter	Value
start_layer_index	9
end_layer_index	20
preserve_good_behavior_weight	0.8093
steer_bad_behavior_weight	0.0001
overcorrect_relative_weight	0.9247
neighbor_count	13

Performance

Table with columns: Metric, This model, Original model (Ministral-3-3B-Reasoning)
Metric	This model	Original model (Ministral-3-3B-Reasoning)
Refusals	1/100	91/100
KL divergence	0.0170	0 (by definition)

Ministral 3 3B Reasoning 2512

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

This model is the reasoning post-trained version, trained for reasoning tasks, making it ideal for math, coding and stem related use cases.

The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized.

Key Features

Ministral 3 3B consists of two main architectural components:

3.4B Language Model
0.4B Vision Encoder

The Ministral 3 3B Reasoning 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.
Reasoning: Excels at complex, multi-step reasoning and dynamic problem-solving.
Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
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.

Use Cases

Ideal for lightweight, real-time applications on edge or low-resource devices, such as:

Image captioning
Text classification
Real-time efficient translation
Data extraction
Short content generation
Fine-tuning and specialization
And more...

Bringing advanced AI capabilities to edge and distributed environments for embedded systems.

Recommended Settings

We recommend deploying with the following best practices:

System Prompt: Use our provided system prompt, and append it to your custom system prompt to define a clear environment and use case, including guidance on how to effectively leverage tools in agentic systems.
Multi-turn Traces: We highly recommend keeping the reasoning traces in context.
Sampling Parameters: Use a temperature of 0.7 for most environments ; Different temperatures may be explored for different 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.

Ministral 3 Family

Table with columns: Model Name, Type, Precision, Link
Model Name	Type	Precision	Link
Ministral 3 3B Base 2512	Base pre-trained	BF16	Hugging Face
Ministral 3 3B Instruct 2512	Instruct post-trained	FP8	Hugging Face
Ministral 3 3B Reasoning 2512	Reasoning capable	BF16	Hugging Face
Ministral 3 8B Base 2512	Base pre-trained	BF16	Hugging Face
Ministral 3 8B Instruct 2512	Instruct post-trained	FP8	Hugging Face
Ministral 3 8B Reasoning 2512	Reasoning capable	BF16	Hugging Face
Ministral 3 14B Base 2512	Base pre-trained	BF16	Hugging Face
Ministral 3 14B Instruct 2512	Instruct post-trained	FP8	Hugging Face
Ministral 3 14B Reasoning 2512	Reasoning capable	BF16	Hugging Face

Other formats available here.

Benchmark Results

We compare Ministral 3 to similar sized models.

Reasoning

Table with columns: Model, AIME25, AIME24, GPQA Diamond, LiveCodeBench
Model	AIME25	AIME24	GPQA Diamond	LiveCodeBench
Ministral 3 14B	0.850	0.898	0.712	0.646
Qwen3-14B (Thinking)	0.737	0.837	0.663	0.593

Ministral 3 8B	0.787	0.860	0.668	0.616
Qwen3-VL-8B-Thinking	0.798	0.860	0.671	0.580

Ministral 3 3B	0.721	0.775	0.534	0.548
Qwen3-VL-4B-Thinking	0.697	0.729	0.601	0.513

Instruct

Table with columns: Model, Arena Hard, WildBench, MATH Maj@1, MM MTBench
Model	Arena Hard	WildBench	MATH Maj@1	MM MTBench
Ministral 3 14B	0.551	68.5	0.904	8.49
Qwen3 14B (Non-Thinking)	0.427	65.1	0.870	NOT MULTIMODAL
Gemma3-12B-Instruct	0.436	63.2	0.854	6.70

Ministral 3 8B	0.509	66.8	0.876	8.08
Qwen3-VL-8B-Instruct	0.528	66.3	0.946	8.00

Ministral 3 3B	0.305	56.8	0.830	7.83
Qwen3-VL-4B-Instruct	0.438	56.8	0.900	8.01
Qwen3-VL-2B-Instruct	0.163	42.2	0.786	6.36
Gemma3-4B-Instruct	0.318	49.1	0.759	5.23

Base

Table with columns: Model, Multilingual MMLU, MATH CoT 2-Shot, AGIEval 5-shot, MMLU Redux 5-shot, MMLU 5-shot, TriviaQA 5-shot
Model	Multilingual MMLU	MATH CoT 2-Shot	AGIEval 5-shot	MMLU Redux 5-shot	MMLU 5-shot	TriviaQA 5-shot
Ministral 3 14B	0.742	0.676	0.648	0.820	0.794	0.749
Qwen3 14B Base	0.754	0.620	0.661	0.837	0.804	0.703
Gemma 3 12B Base	0.690	0.487	0.587	0.766	0.745	0.788

Ministral 3 8B	0.706	0.626	0.591	0.793	0.761	0.681
Qwen 3 8B Base	0.700	0.576	0.596	0.794	0.760	0.639

Ministral 3 3B	0.652	0.601	0.511	0.735	0.707	0.592
Qwen 3 4B Base	0.677	0.405	0.570	0.759	0.713	0.530
Gemma 3 4B Base	0.516	0.294	0.430	0.626	0.589	0.640

Usage

The model can be used with the following frameworks;

vllm: See here
transformers: See here

vLLM

We recommend using this model with vLLM.

Installation

Make sure to install vllm >= 0.12.0:

text

pip install vllm --upgrade

Doing so should automatically install mistral_common >= 1.8.6.

To check:

text

python -c "import mistral_common; print(mistral_common.__version__)"

You can also make use of a ready-to-go docker image or on the docker hub.

Serve

Due to their size, Ministral-3-3B-Reasoning-2512 and Ministral-3-8B-Reasoning-2512 can run on a single 1xH200 GPU.

A simple launch command is:

bash

vllm serve mistralai/Ministral-3-3B-Reasoning-2512 \  --tokenizer_mode mistral --config_format mistral --load_format mistral \  --enable-auto-tool-choice --tool-call-parser mistral \  --reasoning-parser mistral

Key parameter notes:

enable-auto-tool-choice: Required when enabling tool usage.
tool-call-parser mistral: Required when enabling tool usage.
reasoning-parser mistral: Required when enabling reasoning.

Additional flags:

You can set --max-model-len to preserve memory. By default it is set to 262144 which is quite large but not necessary for most scenarios.
You can set --max-num-batched-tokens to balance throughput and latency, higher means higher throughput but higher latency.

Recommended Sampling Settings:

We recommend starting with a Temperature of 0.7 for most use cases. Feel free to experiment with different settings to best suit your specific needs.

Usage of the model

Here we assume that the model mistralai/Ministral-3-3B-Reasoning-2512 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.

Let's see if the Ministral 3 model knows when to pick a fight !

python

from typing import Any 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 = 0.7TOP_P = 0.95MAX_TOK = 262144client = 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) -> dict[str, Any]:    file_path = hf_hub_download(repo_id=repo_id, filename=filename)    with open(file_path, "r") as file:        system_prompt = file.read()     index_begin_think = system_prompt.find("[THINK]")    index_end_think = system_prompt.find("[/THINK]")     return {        "role": "system",        "content": [            {"type": "text", "text": system_prompt[:index_begin_think]},            {                "type": "thinking",                "thinking": system_prompt[                    index_begin_think + len("[THINK]") : index_end_think                ],                "closed": True,            },            {                "type": "text",                "text": system_prompt[index_end_think + len("[/THINK]") :],            },        ],    }  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 = [    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}},        ],    },]  stream = client.chat.completions.create(    model=model,    messages=messages,    stream=True,    temperature=TEMP,    top_p=TOP_P,    max_tokens=MAX_TOK,) print("client: Start streaming chat completions...:\n")printed_reasoning_content = Falseanswer = [] for chunk in stream:    reasoning_content = None    content = None    # Check the content is reasoning_content or content    if hasattr(chunk.choices[0].delta, "reasoning_content"):        reasoning_content = chunk.choices[0].delta.reasoning_content    if hasattr(chunk.choices[0].delta, "content"):        content = chunk.choices[0].delta.content     if reasoning_content is not None:        if not printed_reasoning_content:            printed_reasoning_content = True            print("Start reasoning:\n", end="", flush=True)        print(reasoning_content, end="", flush=True)    elif content is not None:        # Extract and print the content        if not reasoning_content and printed_reasoning_content:            answer.extend(content)        print(content, end="", flush=True) if answer:    print("\n\n=============\nAnswer\n=============\n")    print("".join(answer))else:    print("\n\n=============\nNo Answer\n=============\n")    print(        "No answer was generated by the model, probably because the maximum number of tokens was reached."    )

Transformers

You can also use Ministral 3 3B Reasoning 2512 with Transformers ! Make sure to install Transformers from its first v5 release candidate or from "main":

text

pip install transformers==5.0.0rc0

To make the best use of our model with Transformers make sure to have installed mistral-common >= 1.8.6 to use our tokenizer.

bash

pip install mistral-common --upgrade

Then load our tokenizer along with the model and generate:

python

import torchfrom transformers import Mistral3ForConditionalGeneration, MistralCommonBackend model_id = "mistralai/Ministral-3-3B-Reasoning-2512" tokenizer = MistralCommonBackend.from_pretrained(model_id)model = Mistral3ForConditionalGeneration.from_pretrained(    model_id, torch_dtype=torch.bfloat16, device_map="auto") image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438" messages = [    {        "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}},        ],    },] tokenized = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True) tokenized["input_ids"] = tokenized["input_ids"].to(device="cuda")tokenized["pixel_values"] = tokenized["pixel_values"].to(dtype=torch.bfloat16, device="cuda")image_sizes = [tokenized["pixel_values"].shape[-2:]] output = model.generate(    **tokenized,    image_sizes=image_sizes,    max_new_tokens=8092,)[0] decoded_output = tokenizer.decode(output[len(tokenized["input_ids"][0]):])print(decoded_output)

License

This model is licensed under the Apache 2.0 License.

You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.

Arbitrary-Rank Ablation (ARA) parameters

Table with columns: Parameter, Value
Parameter	Value
start_layer_index	9
end_layer_index	20
preserve_good_behavior_weight	0.8093
steer_bad_behavior_weight	0.0001
overcorrect_relative_weight	0.9247
neighbor_count	13

Performance

Table with columns: Metric, This model, Original model (Ministral-3-3B-Reasoning)
Metric	This model	Original model (Ministral-3-3B-Reasoning)
Refusals	1/100	91/100
KL divergence	0.0170	0 (by definition)

Ministral 3 3B Reasoning 2512

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

This model is the reasoning post-trained version, trained for reasoning tasks, making it ideal for math, coding and stem related use cases.

Key Features

Ministral 3 3B consists of two main architectural components:

3.4B Language Model
0.4B Vision Encoder

The Ministral 3 3B Reasoning 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.
Reasoning: Excels at complex, multi-step reasoning and dynamic problem-solving.
Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
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.

Use Cases

Ideal for lightweight, real-time applications on edge or low-resource devices, such as:

Image captioning
Text classification
Real-time efficient translation
Data extraction
Short content generation
Fine-tuning and specialization
And more...

Bringing advanced AI capabilities to edge and distributed environments for embedded systems.

Recommended Settings

We recommend deploying with the following best practices:

System Prompt: Use our provided system prompt, and append it to your custom system prompt to define a clear environment and use case, including guidance on how to effectively leverage tools in agentic systems.
Multi-turn Traces: We highly recommend keeping the reasoning traces in context.
Sampling Parameters: Use a temperature of 0.7 for most environments ; Different temperatures may be explored for different 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.

Ministral 3 Family

Table with columns: Model Name, Type, Precision, Link
Model Name	Type	Precision	Link
Ministral 3 3B Base 2512	Base pre-trained	BF16	Hugging Face
Ministral 3 3B Instruct 2512	Instruct post-trained	FP8	Hugging Face
Ministral 3 3B Reasoning 2512	Reasoning capable	BF16	Hugging Face
Ministral 3 8B Base 2512	Base pre-trained	BF16	Hugging Face
Ministral 3 8B Instruct 2512	Instruct post-trained	FP8	Hugging Face
Ministral 3 8B Reasoning 2512	Reasoning capable	BF16	Hugging Face
Ministral 3 14B Base 2512	Base pre-trained	BF16	Hugging Face
Ministral 3 14B Instruct 2512	Instruct post-trained	FP8	Hugging Face
Ministral 3 14B Reasoning 2512	Reasoning capable	BF16	Hugging Face

Other formats available here.

Benchmark Results

We compare Ministral 3 to similar sized models.

Reasoning

Table with columns: Model, AIME25, AIME24, GPQA Diamond, LiveCodeBench
Model	AIME25	AIME24	GPQA Diamond	LiveCodeBench
Ministral 3 14B	0.850	0.898	0.712	0.646
Qwen3-14B (Thinking)	0.737	0.837	0.663	0.593

Ministral 3 8B	0.787	0.860	0.668	0.616
Qwen3-VL-8B-Thinking	0.798	0.860	0.671	0.580

Ministral 3 3B	0.721	0.775	0.534	0.548
Qwen3-VL-4B-Thinking	0.697	0.729	0.601	0.513

Instruct

Table with columns: Model, Arena Hard, WildBench, MATH Maj@1, MM MTBench
Model	Arena Hard	WildBench	MATH Maj@1	MM MTBench
Ministral 3 14B	0.551	68.5	0.904	8.49
Qwen3 14B (Non-Thinking)	0.427	65.1	0.870	NOT MULTIMODAL
Gemma3-12B-Instruct	0.436	63.2	0.854	6.70

Ministral 3 8B	0.509	66.8	0.876	8.08
Qwen3-VL-8B-Instruct	0.528	66.3	0.946	8.00

Ministral 3 3B	0.305	56.8	0.830	7.83
Qwen3-VL-4B-Instruct	0.438	56.8	0.900	8.01
Qwen3-VL-2B-Instruct	0.163	42.2	0.786	6.36
Gemma3-4B-Instruct	0.318	49.1	0.759	5.23

Base

Table with columns: Model, Multilingual MMLU, MATH CoT 2-Shot, AGIEval 5-shot, MMLU Redux 5-shot, MMLU 5-shot, TriviaQA 5-shot
Model	Multilingual MMLU	MATH CoT 2-Shot	AGIEval 5-shot	MMLU Redux 5-shot	MMLU 5-shot	TriviaQA 5-shot
Ministral 3 14B	0.742	0.676	0.648	0.820	0.794	0.749
Qwen3 14B Base	0.754	0.620	0.661	0.837	0.804	0.703
Gemma 3 12B Base	0.690	0.487	0.587	0.766	0.745	0.788

Ministral 3 8B	0.706	0.626	0.591	0.793	0.761	0.681
Qwen 3 8B Base	0.700	0.576	0.596	0.794	0.760	0.639

Ministral 3 3B	0.652	0.601	0.511	0.735	0.707	0.592
Qwen 3 4B Base	0.677	0.405	0.570	0.759	0.713	0.530
Gemma 3 4B Base	0.516	0.294	0.430	0.626	0.589	0.640

Usage

The model can be used with the following frameworks;

vllm: See here
transformers: See here

vLLM

We recommend using this model with vLLM.

Installation

Make sure to install vllm >= 0.12.0:

text

pip install vllm --upgrade

Doing so should automatically install mistral_common >= 1.8.6.

To check:

text

python -c "import mistral_common; print(mistral_common.__version__)"

You can also make use of a ready-to-go docker image or on the docker hub.

Serve

Due to their size, Ministral-3-3B-Reasoning-2512 and Ministral-3-8B-Reasoning-2512 can run on a single 1xH200 GPU.

A simple launch command is:

bash

vllm serve mistralai/Ministral-3-3B-Reasoning-2512 \  --tokenizer_mode mistral --config_format mistral --load_format mistral \  --enable-auto-tool-choice --tool-call-parser mistral \  --reasoning-parser mistral

vllm serve mistralai/Ministral-3-3B-Reasoning-2512 \  --tokenizer_mode mistral --config_format mistral --load_format mistral \  --enable-auto-tool-choice --tool-call-parser mistral \  --reasoning-parser mistral

Key parameter notes:

enable-auto-tool-choice: Required when enabling tool usage.
tool-call-parser mistral: Required when enabling tool usage.
reasoning-parser mistral: Required when enabling reasoning.

Additional flags:

You can set --max-model-len to preserve memory. By default it is set to 262144 which is quite large but not necessary for most scenarios.
You can set --max-num-batched-tokens to balance throughput and latency, higher means higher throughput but higher latency.

Recommended Sampling Settings:

We recommend starting with a Temperature of 0.7 for most use cases. Feel free to experiment with different settings to best suit your specific needs.

Usage of the model

Here we assume that the model mistralai/Ministral-3-3B-Reasoning-2512 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.

Let's see if the Ministral 3 model knows when to pick a fight !

python

from typing import Any 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 = 0.7TOP_P = 0.95MAX_TOK = 262144client = 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) -> dict[str, Any]:    file_path = hf_hub_download(repo_id=repo_id, filename=filename)    with open(file_path, "r") as file:        system_prompt = file.read()     index_begin_think = system_prompt.find("[THINK]")    index_end_think = system_prompt.find("[/THINK]")     return {        "role": "system",        "content": [            {"type": "text", "text": system_prompt[:index_begin_think]},            {                "type": "thinking",                "thinking": system_prompt[                    index_begin_think + len("[THINK]") : index_end_think                ],                "closed": True,            },            {                "type": "text",                "text": system_prompt[index_end_think + len("[/THINK]") :],            },        ],    }  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 = [    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}},        ],    },]  stream = client.chat.completions.create(    model=model,    messages=messages,    stream=True,    temperature=TEMP,    top_p=TOP_P,    max_tokens=MAX_TOK,) print("client: Start streaming chat completions...:\n")printed_reasoning_content = Falseanswer = [] for chunk in stream:    reasoning_content = None    content = None    # Check the content is reasoning_content or content    if hasattr(chunk.choices[0].delta, "reasoning_content"):        reasoning_content = chunk.choices[0].delta.reasoning_content    if hasattr(chunk.choices[0].delta, "content"):        content = chunk.choices[0].delta.content     if reasoning_content is not None:        if not printed_reasoning_content:            printed_reasoning_content = True            print("Start reasoning:\n", end="", flush=True)        print(reasoning_content, end="", flush=True)    elif content is not None:        # Extract and print the content        if not reasoning_content and printed_reasoning_content:            answer.extend(content)        print(content, end="", flush=True) if answer:    print("\n\n=============\nAnswer\n=============\n")    print("".join(answer))else:    print("\n\n=============\nNo Answer\n=============\n")    print(        "No answer was generated by the model, probably because the maximum number of tokens was reached."    )

from typing import Any 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 = 0.7TOP_P = 0.95MAX_TOK = 262144client = 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) -> dict[str, Any]:    file_path = hf_hub_download(repo_id=repo_id, filename=filename)    with open(file_path, "r") as file:        system_prompt = file.read()     index_begin_think = system_prompt.find("[THINK]")    index_end_think = system_prompt.find("[/THINK]")     return {        "role": "system",        "content": [            {"type": "text", "text": system_prompt[:index_begin_think]},            {                "type": "thinking",                "thinking": system_prompt[                    index_begin_think + len("[THINK]") : index_end_think                ],                "closed": True,            },            {                "type": "text",                "text": system_prompt[index_end_think + len("[/THINK]") :],            },        ],    }  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 = [    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}},        ],    },]  stream = client.chat.completions.create(    model=model,    messages=messages,    stream=True,    temperature=TEMP,    top_p=TOP_P,    max_tokens=MAX_TOK,) print("client: Start streaming chat completions...:\n")printed_reasoning_content = Falseanswer = [] for chunk in stream:    reasoning_content = None    content = None    # Check the content is reasoning_content or content    if hasattr(chunk.choices[0].delta, "reasoning_content"):        reasoning_content = chunk.choices[0].delta.reasoning_content    if hasattr(chunk.choices[0].delta, "content"):        content = chunk.choices[0].delta.content     if reasoning_content is not None:        if not printed_reasoning_content:            printed_reasoning_content = True            print("Start reasoning:\n", end="", flush=True)        print(reasoning_content, end="", flush=True)    elif content is not None:        # Extract and print the content        if not reasoning_content and printed_reasoning_content:            answer.extend(content)        print(content, end="", flush=True) if answer:    print("\n\n=============\nAnswer\n=============\n")    print("".join(answer))else:    print("\n\n=============\nNo Answer\n=============\n")    print(        "No answer was generated by the model, probably because the maximum number of tokens was reached."    )

Transformers

You can also use Ministral 3 3B Reasoning 2512 with Transformers ! Make sure to install Transformers from its first v5 release candidate or from "main":

text

pip install transformers==5.0.0rc0

To make the best use of our model with Transformers make sure to have installed mistral-common >= 1.8.6 to use our tokenizer.

bash

pip install mistral-common --upgrade

Then load our tokenizer along with the model and generate:

python

import torchfrom transformers import Mistral3ForConditionalGeneration, MistralCommonBackend model_id = "mistralai/Ministral-3-3B-Reasoning-2512" tokenizer = MistralCommonBackend.from_pretrained(model_id)model = Mistral3ForConditionalGeneration.from_pretrained(    model_id, torch_dtype=torch.bfloat16, device_map="auto") image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438" messages = [    {        "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}},        ],    },] tokenized = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True) tokenized["input_ids"] = tokenized["input_ids"].to(device="cuda")tokenized["pixel_values"] = tokenized["pixel_values"].to(dtype=torch.bfloat16, device="cuda")image_sizes = [tokenized["pixel_values"].shape[-2:]] output = model.generate(    **tokenized,    image_sizes=image_sizes,    max_new_tokens=8092,)[0] decoded_output = tokenizer.decode(output[len(tokenized["input_ids"][0]):])print(decoded_output)

import torchfrom transformers import Mistral3ForConditionalGeneration, MistralCommonBackend model_id = "mistralai/Ministral-3-3B-Reasoning-2512" tokenizer = MistralCommonBackend.from_pretrained(model_id)model = Mistral3ForConditionalGeneration.from_pretrained(    model_id, torch_dtype=torch.bfloat16, device_map="auto") image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438" messages = [    {        "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}},        ],    },] tokenized = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True) tokenized["input_ids"] = tokenized["input_ids"].to(device="cuda")tokenized["pixel_values"] = tokenized["pixel_values"].to(dtype=torch.bfloat16, device="cuda")image_sizes = [tokenized["pixel_values"].shape[-2:]] output = model.generate(    **tokenized,    image_sizes=image_sizes,    max_new_tokens=8092,)[0] decoded_output = tokenizer.decode(output[len(tokenized["input_ids"][0]):])print(decoded_output)

License

This model is licensed under the Apache 2.0 License.

You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.

Ministral-3-3B-Reasoning-UNCENSORED-heretic

README

Arbitrary-Rank Ablation (ARA) parameters

Performance

Ministral 3 3B Reasoning 2512

Key Features

Use Cases

Recommended Settings

Ministral 3 Family

Benchmark Results

Reasoning

Instruct

Base

Usage

vLLM

Installation

Serve

Usage of the model

Transformers

License

Explore FriendliAI today

README

Arbitrary-Rank Ablation (ARA) parameters

Performance

Ministral 3 3B Reasoning 2512

Key Features

Use Cases

Recommended Settings

Ministral 3 Family

Benchmark Results

Reasoning

Instruct

Base

Usage

vLLM

Installation

Serve

Usage of the model

Transformers

License