coder3101
/

Ministral-3-14B-Reasoning-2512-heretic

@@ -1,199 +1,620 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+library_name: vllm
+language:
+- en
+- fr
+- es
+- de
+- it
+- pt
+- nl
+- zh
+- ja
+- ko
+- ar
+license: apache-2.0
+inference: false
+base_model:
+- mistralai/Ministral-3-14B-Base-2512
+extra_gated_description: If you want to learn more about how we process your personal
+  data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
+tags:
+- mistral-common
+- heretic
+- uncensored
+- decensored
+- abliterated
 ---
+# This is a decensored version of [mistralai/Ministral-3-14B-Reasoning-2512](https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512), made using [Heretic](https://github.com/p-e-w/heretic) v1.1.0
+## Abliteration parameters
+| Parameter | Value |
+| :-------- | :---: |
+| **direction_index** | 17.52 |
+| **attn.o_proj.max_weight** | 1.46 |
+| **attn.o_proj.max_weight_position** | 25.35 |
+| **attn.o_proj.min_weight** | 0.09 |
+| **attn.o_proj.min_weight_distance** | 18.63 |
+| **mlp.down_proj.max_weight** | 1.19 |
+| **mlp.down_proj.max_weight_position** | 30.03 |
+| **mlp.down_proj.min_weight** | 0.79 |
+| **mlp.down_proj.min_weight_distance** | 22.65 |
+## Performance
+| Metric | This model | Original model ([mistralai/Ministral-3-14B-Reasoning-2512](https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 0.0172 | 0 *(by definition)* |
+| **Refusals** | 7/100 | 97/100 |
+-----
+# Ministral 3 14B Reasoning 2512
+The largest model in the Ministral 3 family, **Ministral 3 14B** offers frontier capabilities and performance comparable to its larger [Mistral Small 3.2 24B](https://huggingface.co/mistralai/Mistral-Small-3.2-Instruct-2506) counterpart. A powerful and efficient 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 14B can even be deployed locally, capable of fitting in 32GB of VRAM in BF16, and less than 24GB of RAM/VRAM when quantized.
+Learn more in our [blog post](https://mistral.ai/news/mistral-3) and [paper](https://arxiv.org/abs/2601.08584).
+## Key Features
+Ministral 3 14B consists of two main architectural components:
+- **13.5B Language Model**
+- **0.4B Vision Encoder**
+The Ministral 3 14B 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
+Private AI deployments where advanced capabilities meet practical hardware constraints:
+- Private/custom chat and AI assistant deployments in constrained environments
+- Advanced local agentic use cases
+- Fine-tuning and specialization
+- And more...
+Bringing advanced AI capabilities to most environments.
+### Recommended Settings
+We recommend deploying with the following best practices:
+- System Prompt: Use our provided [system prompt](https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512/blob/main/SYSTEM_PROMPT.txt), 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 1** 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
+| Model Name                     | Type               | Precision | Link                                                                                     |
+|--------------------------------|--------------------|-----------|------------------------------------------------------------------------------------------|
+| Ministral 3 3B Base 2512       | Base pre-trained   | BF16      | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-3B-Base-2512)                |
+| Ministral 3 3B Instruct 2512   | Instruct post-trained | FP8   | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512)            |
+| Ministral 3 3B Reasoning 2512  | Reasoning capable  | BF16      | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512)           |
+| Ministral 3 8B Base 2512       | Base pre-trained   | BF16      | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-8B-Base-2512)                |
+| Ministral 3 8B Instruct 2512   | Instruct post-trained | FP8    | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512)            |
+| Ministral 3 8B Reasoning 2512  | Reasoning capable  | BF16      | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-8B-Reasoning-2512)           |
+| Ministral 3 14B Base 2512      | Base pre-trained   | BF16      | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-14B-Base-2512)               |
+| Ministral 3 14B Instruct 2512  | Instruct post-trained | FP8    | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512)           |
+| **Ministral 3 14B Reasoning 2512** | **Reasoning capable**  | **BF16**      | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512)          |
+Other formats available [here](https://huggingface.co/collections/mistralai/ministral-3-additional-checkpoints).
+## Benchmark Results
+We compare Ministral 3 to similar sized models.
+### Reasoning
+| Model                     | AIME25      | AIME24      | GPQA Diamond | LiveCodeBench |
+|---------------------------|-------------|-------------|--------------|---------------|
+| **Ministral 3 14B**       | <u>0.850</u>| <u>0.898</u>| <u>0.712</u> | <u>0.646</u>  |
+| Qwen3-14B (Thinking)      | 0.737       | 0.837       | 0.663        | 0.593         |
+|                           |             |             |              |               |
+| **Ministral 3 8B**        | 0.787       | <u>0.860</u>| 0.668        | <u>0.616</u>  |
+| Qwen3-VL-8B-Thinking      | <u>0.798</u>| <u>0.860</u>| <u>0.671</u> | 0.580         |
+|                           |             |             |              |               |
+| **Ministral 3 3B**        | <u>0.721</u>| <u>0.775</u>| 0.534        | <u>0.548</u>  |
+| Qwen3-VL-4B-Thinking      | 0.697       | 0.729       | <u>0.601</u> | 0.513         |
+### Instruct
+| Model                     | Arena Hard  | WildBench  | MATH Maj@1  | MM MTBench       |
+|---------------------------|-------------|------------|-------------|------------------|
+| **Ministral 3 14B**       | <u>0.551</u>| <u>68.5</u>| <u>0.904</u>| <u>8.49</u>      |
+| 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       | <u>66.8</u>| 0.876       | <u>8.08</u>      |
+| Qwen3-VL-8B-Instruct      | <u>0.528</u>| 66.3       | <u>0.946</u>| 8.00             |
+|                           |             |            |             |                  |
+| **Ministral 3 3B**        | 0.305       | <u>56.8</u>| 0.830       | 7.83             |
+| Qwen3-VL-4B-Instruct      | <u>0.438</u>| <u>56.8</u>| <u>0.900</u>| <u>8.01</u>      |
+| Qwen3-VL-2B-Instruct      | 0.163       | 42.2       | 0.786       | 6.36             |
+| Gemma3-4B-Instruct        | 0.318       | 49.1       | 0.759       | 5.23             |
+### Base
+| 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             | <u>0.676</u>    | 0.648          | 0.820             | 0.794       | 0.749           |
+| Qwen3 14B Base      | <u>0.754</u>      | 0.620           | <u>0.661</u>   | <u>0.837</u>      | <u>0.804</u>| 0.703           |
+| Gemma 3 12B Base    | 0.690             | 0.487           | 0.587          | 0.766             | 0.745       | <u>0.788</u>    |
+|                     |                   |                 |                |                   |             |                 |
+| **Ministral 3 8B**  | <u>0.706</u>      | <u>0.626</u>    | 0.591          | 0.793             | <u>0.761</u>| <u>0.681</u>    |
+| Qwen 3 8B Base      | 0.700             | 0.576           | <u>0.596</u>   | <u>0.794</u>      | 0.760       | 0.639           |
+|                     |                   |                 |                |                   |             |                 |
+| **Ministral 3 3B**  | 0.652             | <u>0.601</u>    | 0.511          | 0.735             | 0.707       | 0.592           |
+| Qwen 3 4B Base      | <u>0.677</u>      | 0.405           | <u>0.570</u>   | <u>0.759</u>      | <u>0.713</u>| 0.530           |
+| Gemma 3 4B Base     | 0.516             | 0.294           | 0.430          | 0.626             | 0.589       | <u>0.640</u>    |
+## Usage
+The model can be used with the following frameworks;
+- [`vllm`](https://github.com/vllm-project/vllm): See [here](#vllm)
+- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
+### vLLM
+We recommend using this model with [vLLM](https://github.com/vllm-project/vllm).
+#### Installation
+Make sure to install **vllm >= 0.12.0**:
+```
+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
+To fully exploit the `Ministral-3-14B-Reasoning-2512` we recommed using 2xH200 GPUs for deployment due to its large context. However if you don't need a large context, you can fall back to a single GPU.
+A simple launch command is:
+```bash
+vllm serve mistralai/Ministral-3-14B-Reasoning-2512 \
+  --tensor-parallel-size 2 \
+  --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.
+#### Usage of the model
+Here we assume that the model `mistralai/Ministral-3-8B-Reasoning-2512` is served and you can ping it to the domain `localhost` with the port `8000` which is the default for vLLM.
+<details>
+  <summary>Vision Reasoning</summary>
+Let's see if the Ministral 3 model knows when to pick a fight !
+```python
+from typing import Any
+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 = 0.7
+TOP_P = 0.95
+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) -> 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 = False
+answer = []
+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."
+    )
+```
+Now we'll make it compute some maths !
+```python
+from typing import Any
+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 = 0.7
+TOP_P = 0.95
+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) -> 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://i.ytimg.com/vi/5Y3xLHeyKZU/hqdefault.jpg"
+messages = [
+    SYSTEM_PROMPT,
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "Solve the equations. If they contain only numbers, use your calculator, else only think. Answer in the language of the image.",
+            },
+            {"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 = False
+answer = []
+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)
+    if 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."
+    )
+```
+</details>
+<details>
+  <summary>Text-Only Request</summary>
+Let's do more maths and leave it up to the model to figure out how to achieve a result.
+```python
+from typing import Any
+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 = 0.7
+TOP_P = 0.95
+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) -> 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")
+query = "Use each number in 2,5,6,3 exactly once, along with any combination of +, -, ×, ÷ (and parentheses for grouping), to make the number 24."
+messages = [
+    SYSTEM_PROMPT,
+    {"role": "user", "content": query}
+]
+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 = False
+answer = []
+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)
+    if 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.")
+```
+</details>
+### 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":
+```
+pip install transformers==5.0.0rc0
+```
+To make the best use of our model with `Transformers` make sure to have [installed](https://github.com/mistralai/mistral-common) `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:
+<details>
+  <summary>Python snippet</summary>
+```python
+import torch
+from transformers import Mistral3ForConditionalGeneration, MistralCommonBackend
+model_id = "mistralai/Ministral-3-14B-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)
+```
+</details>
+## 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.*