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README.md

@@ -36,144 +36,81 @@ tags:
 - mistral-common
 ---
 
-# Model Card for Mistral-Small-3.1-24B-Instruct-2503
+# Mistral-Small-3.2-24B-Instruct-2506
 
-Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) **adds state-of-the-art vision understanding** and enhances **long context capabilities up to 128k tokens** without compromising text performance. 
-With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.  
-This model is an instruction-finetuned version of: [Mistral-Small-3.1-24B-Base-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
+Mistral-Small-3.2-24B-Instruct-2506 is a minor update of [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503).
 
-Mistral Small 3.1 can be deployed locally and is exceptionally "knowledge-dense," fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.  
+Small-3.2 improves in the following categories:
+- **Instruction following**: Small-3.2 is better at following precise instructions
+- **Repetition errors**: Small-3.2 produces less infinite generations or repetitive answers
+- **Function calling**: Small-3.2's function calling template is more robust (see [here](https://github.com/mistralai/mistral-common/blob/535b4d0a0fc94674ea17db6cf8dc2079b81cbcfa/src/mistral_common/tokens/tokenizers/instruct.py#L778) and [examples](#function-calling))
 
-It is ideal for:
-- Fast-response conversational agents.
-- Low-latency function calling.
-- Subject matter experts via fine-tuning.
-- Local inference for hobbyists and organizations handling sensitive data.
-- Programming and math reasoning.
-- Long document understanding.
-- Visual understanding.
-
-For enterprises requiring specialized capabilities (increased context, specific modalities, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
-
-Learn more about Mistral Small 3.1 in our [blog post](https://mistral.ai/news/mistral-small-3-1/).
+In all other categories Small-3.2 should match or slightly improve compared to [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503).
 
 ## Key Features
-- **Vision:** Vision capabilities enable the model to analyze images and provide insights based on visual content in addition to text.
-- **Multilingual:** Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi.
-- **Agent-Centric:** Offers best-in-class agentic capabilities with native function calling and JSON outputting.
-- **Advanced Reasoning:** State-of-the-art conversational and reasoning capabilities.
-- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
-- **Context Window:** A 128k context window.
-- **System Prompt:** Maintains strong adherence and support for system prompts.
-- **Tokenizer:** Utilizes a Tekken tokenizer with a 131k vocabulary size.
+- same as [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503#key-features)
 
 ## Benchmark Results
 
-When available, we report numbers previously published by other model providers, otherwise we re-evaluate them using our own evaluation harness.
-
-### Pretrain Evals
+We compare Mistral-Small-3.2-24B to [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503).
+For more comparison against other models of similar size, please check [Mistral-Small-3.1's Benchmarks'](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503#benchmark-results)
 
-| Model                          | MMLU (5-shot) | MMLU Pro (5-shot CoT) | TriviaQA   | GPQA Main (5-shot CoT)| MMMU      |
-|--------------------------------|---------------|-----------------------|------------|-----------------------|-----------|
-| **Small 3.1 24B Base**         | **81.01%**    | **56.03%**            | 80.50%     | **37.50%**            | **59.27%**|
-| Gemma 3 27B PT                 | 78.60%        | 52.20%                | **81.30%** | 24.30%                | 56.10%    |
+### Text 
 
-### Instruction Evals
+#### Instruction Following / Chat / Tone
 
-#### Text
+| Model | Wildbench v2 | Arena Hard v2 | IF (Internal; accuracy) |
+|-------|---------------|---------------|------------------------|
+| Small 3.1 24B Instruct | 55.6% | 19.56% | 82.75% |
+| **Small 3.2 24B Instruct** | **65.33%** | **43.1%** | **84.78%** |
 
-| Model                          | MMLU      | MMLU Pro (5-shot CoT) | MATH                   | GPQA Main (5-shot CoT) | GPQA Diamond (5-shot CoT )| MBPP      | HumanEval | SimpleQA (TotalAcc)|
-|--------------------------------|-----------|-----------------------|------------------------|------------------------|---------------------------|-----------|-----------|--------------------|
-| **Small 3.1 24B Instruct**     | 80.62%    | 66.76%                | 69.30%                 | **44.42%**             | **45.96%**                | 74.71%    | **88.41%**| **10.43%**         |
-| Gemma 3 27B IT                 | 76.90%    | **67.50%**            | **89.00%**             | 36.83%                 | 42.40%                    | 74.40%    | 87.80%    | 10.00%             |
-| GPT4o Mini                     | **82.00%**| 61.70%                | 70.20%                 | 40.20%                 | 39.39%                    | 84.82%    | 87.20%    | 9.50%              |
-| Claude 3.5 Haiku               | 77.60%    | 65.00%                | 69.20%                 | 37.05%                 | 41.60%                    | **85.60%**| 88.10%    | 8.02%              |
-| Cohere Aya-Vision 32B          | 72.14%    | 47.16%                | 41.98%                 | 34.38%                 | 33.84%                    | 70.43%    | 62.20%    | 7.65%              |
+#### Infinite Generations
 
-#### Vision
+Small 3.2 reduces infinite generations by 2x on challenging, long and repetitive prompts.
 
-| Model                          | MMMU       | MMMU PRO  | Mathvista | ChartQA   | DocVQA    | AI2D        | MM MT Bench |
-|--------------------------------|------------|-----------|-----------|-----------|-----------|-------------|-------------|
-| **Small 3.1 24B Instruct**     | 64.00%     | **49.25%**| **68.91%**| 86.24%    | **94.08%**| **93.72%**  | **7.3**     |
-| Gemma 3 27B IT                 | **64.90%** | 48.38%    | 67.60%    | 76.00%    | 86.60%    | 84.50%      | 7           |
-| GPT4o Mini                     | 59.40%     | 37.60%    | 56.70%    | 76.80%    | 86.70%    | 88.10%      | 6.6         |
-| Claude 3.5 Haiku               | 60.50%     | 45.03%    | 61.60%    | **87.20%**| 90.00%    | 92.10%      | 6.5         |
-| Cohere Aya-Vision 32B          | 48.20%     | 31.50%    | 50.10%    | 63.04%    | 72.40%    | 82.57%      | 4.1         |
+| Model | Infinite Generations (Internal; Lower is better) |
+|-------|-------|
+| Small 3.1 24B Instruct | 2.11% |
+| **Small 3.2 24B Instruct** | **1.29%** |
 
-### Multilingual Evals
+#### STEM
 
-| Model                          | Average    | European   | East Asian | Middle Eastern |
-|--------------------------------|------------|------------|------------|----------------|
-| **Small 3.1 24B Instruct**     | **71.18%** | **75.30%** | **69.17%** | 69.08%         |
-| Gemma 3 27B IT                 | 70.19%     | 74.14%     | 65.65%     | 70.76%         |
-| GPT4o Mini                     | 70.36%     | 74.21%     | 65.96%     | **70.90%**     |
-| Claude 3.5 Haiku               | 70.16%     | 73.45%     | 67.05%     | 70.00%         |
-| Cohere Aya-Vision 32B          | 62.15%     | 64.70%     | 57.61%     | 64.12%         |
+| Model                          | MMLU      | MMLU Pro (5-shot CoT) | MATH                   | GPQA Main (5-shot CoT) | GPQA Diamond (5-shot CoT )| MBPP Plus - Pass@5 | HumanEval Plus - Pass@5 | SimpleQA (TotalAcc)|
+|--------------------------------|-----------|-----------------------|------------------------|------------------------|---------------------------|--------------------|-------------------------|--------------------|
+| Small 3.1 24B Instruct         | 80.62%    | 66.76%                | 69.30%                 | 44.42%                 | 45.96%                    | 74.63%             | 88.99%                  | 10.43%             |
+| **Small 3.2 24B Instruct**     | 80.50%    | **69.06%**            | 69.42%                 | 44.22%                 | 46.13%                    | **78.33%**         | **92.90%**              | **12.10%**         |
 
-### Long Context Evals
+### Vision
 
-| Model                          | LongBench v2    | RULER 32K   | RULER 128K |
-|--------------------------------|-----------------|-------------|------------|
-| **Small 3.1 24B Instruct**     | **37.18%**      | **93.96%**  | 81.20%     |
-| Gemma 3 27B IT                 | 34.59%          | 91.10%      | 66.00%     |
-| GPT4o Mini                     | 29.30%          | 90.20%      | 65.8%      |
-| Claude 3.5 Haiku               | 35.19%          | 92.60%      | **91.90%** |
-
-## Basic Instruct Template (V7-Tekken)
-
-```
-<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]
-```
-*`<system_prompt>`, `<user message>` and `<assistant response>` are placeholders.*
+| Model                          | MMMU       | Mathvista | ChartQA   | DocVQA    | AI2D      |
+|--------------------------------|------------|-----------|-----------|-----------|-----------|
+| Small 3.1 24B Instruct         | **64.00%** | **68.91%**| 86.24%    | 94.08%    | 93.72%  | 
+| **Small 3.2 24B Instruct**     | 62.50%     | 67.09%    | **87.4%** | 94.86%    | 92.91%  | 
 
-***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
 
 ## Usage
 
 The model can be used with the following frameworks;
-- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm)
+- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
+- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
 
 **Note 1**: We recommend using a relatively low temperature, such as `temperature=0.15`.
 
-**Note 2**: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend the following 
-system prompt:
-
-```
-system_prompt = """You are Mistral Small 3.1, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
-You power an AI assistant called Le Chat.
-Your knowledge base was last updated on 2023-10-01.
-The current date is {today}.
-
-When you're not sure about some information, you say that you don't have the information and don't make up anything.
-If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. "What are some good restaurants around me?" => "Where are you?" or "When is the next flight to Tokyo" => "Where do you travel from?").
-You are always very attentive to dates, in particular you try to resolve dates (e.g. "yesterday" is {yesterday}) and when asked about information at specific dates, you discard information that is at another date.
-You follow these instructions in all languages, and always respond to the user in the language they use or request.
-Next sections describe the capabilities that you have.
-
-# WEB BROWSING INSTRUCTIONS
-
-You cannot perform any web search or access internet to open URLs, links etc. If it seems like the user is expecting you to do so, you clarify the situation and ask the user to copy paste the text directly in the chat.
-
-# MULTI-MODAL INSTRUCTIONS
-
-You have the ability to read images, but you cannot generate images.
-You cannot read nor transcribe audio files or videos."""
-```
+**Note 2**: Make sure to add a system prompt to the model to best tailor it to your needs. If you want to use the model as a general assistant, we recommend to use the one provided in the [SYSTEM_PROMPT.txt](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506/blob/main/SYSTEM_PROMPT.txt) file.
 
 ### vLLM (recommended)
 
-We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
-to implement production-ready inference pipelines.
+We recommend using this model with [vLLM](https://github.com/vllm-project/vllm).
 
-**_Installation_**
+#### Installation
 
-Make sure you install [`vLLM >= 0.8.1`](https://github.com/vllm-project/vllm/releases/tag/v0.8.1):
+Make sure to install [`vLLM >= 0.9.1`](https://github.com/vllm-project/vllm/releases/tag/v0.9.1):
 
 ```
 pip install vllm --upgrade
 ```
 
-Doing so should automatically install [`mistral_common >= 1.5.4`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.4).
+Doing so should automatically install [`mistral_common >= 1.6.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.6.2).
 
 To check:
 ```
@@ -182,31 +119,53 @@ 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/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
 
-#### Server
+#### Serve
 
-We recommand that you use Mistral-Small-3.1-24B-Instruct-2503 in a server/client setting. 
+We recommend that you use Mistral-Small-3.2-24B-Instruct-2506 in a server/client setting. 
 
 1. Spin up a server:
 
 ```
-vllm serve mistralai/Mistral-Small-3.1-24B-Instruct-2503 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 2
+vllm serve mistralai/Mistral-Small-3.2-24B-Instruct-2506 \
+  --tokenizer_mode mistral --config_format mistral \
+  --load_format mistral --tool-call-parser mistral \
+  --enable-auto-tool-choice --limit-mm-per-prompt '{"image":10}' \
+  --tensor-parallel-size 2
 ```
 
-**Note:** Running Mistral-Small-3.1-24B-Instruct-2503 on GPU requires ~55 GB of GPU RAM in bf16 or fp16. 
+**Note:** Running Mistral-Small-3.2-24B-Instruct-2506 on GPU requires ~55 GB of GPU RAM in bf16 or fp16. 
+
+
+2. To ping the client you can use a simple Python snippet. See the following examples.
+
 
+#### Vision reasoning
 
-2. To ping the client you can use a simple Python snippet.
+Leverage the vision capabilities of Mistral-Small-3.2-24B-Instruct-2506 to make the best choice given a scenario, go catch them all !
+
+<details>
+  <summary>Python snippet</summary>
 
 ```py
-import requests
-import json
-from huggingface_hub import hf_hub_download
 from datetime import datetime, timedelta
 
-url = "http://<your-server-url>:8000/v1/chat/completions"
-headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
+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"
 
-model = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+TEMP = 0.15
+MAX_TOK = 131072
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+models = client.models.list()
+model = models.data[0].id
 
 
 def load_system_prompt(repo_id: str, filename: str) -> str:
@@ -219,9 +178,9 @@ def load_system_prompt(repo_id: str, filename: str) -> str:
     return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
 
 
-SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
-
-image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"
+model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
+SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
+image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
 
 messages = [
     {"role": "system", "content": SYSTEM_PROMPT},
@@ -230,93 +189,102 @@ messages = [
         "content": [
             {
                 "type": "text",
-                "text": "Which of the depicted countries has the best food? Which the second and third and fourth? Name the country, its color on the map and one its city that is visible on the map, but is not the capital. Make absolutely sure to only name a city that can be seen on the map.",
+                "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}},
         ],
     },
 ]
 
-data = {"model": model, "messages": messages, "temperature": 0.15}
 
-response = requests.post(url, headers=headers, data=json.dumps(data))
-print(response.json()["choices"][0]["message"]["content"])
-# Determining the "best" food is highly subjective and depends on personal preferences. However, based on general popularity and recognition, here are some countries known for their cuisine:
+response = client.chat.completions.create(
+    model=model,
+    messages=messages,
+    temperature=TEMP,
+    max_tokens=MAX_TOK,
+)
 
-# 1. **Italy** - Color: Light Green - City: Milan
-#    - Italian cuisine is renowned worldwide for its pasta, pizza, and various regional specialties.
+print(response.choices[0].message.content)
+# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:
 
-# 2. **France** - Color: Brown - City: Lyon
-#    - French cuisine is celebrated for its sophistication, including dishes like coq au vin, bouillabaisse, and pastries like croissants and éclairs.
+# 1. **FIGHT**:
+#    - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
+#    - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.
 
-# 3. **Spain** - Color: Yellow - City: Bilbao
-#    - Spanish cuisine offers a variety of flavors, from paella and tapas to jamón ibérico and churros.
+# 2. **BAG**:
+#    - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture the Pidgey or heal your Pikachu if needed.
+#    - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat the Pidgey quickly.
 
-# 4. **Greece** - Not visible on the map
-#    - Greek cuisine is known for dishes like moussaka, souvlaki, and baklava. Unfortunately, Greece is not visible on the provided map, so I cannot name a city.
+# 3. **POKÉMON**:
+#    - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be a strategic move if you want to train a lower-level Pokémon.
+#    - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.
 
-# Since Greece is not visible on the map, I'll replace it with another country known for its good food:
+# 4. **RUN**:
+#    - **Pros**: Running away could save time and conserve your Pokémon's health and resources. If you are in a hurry or do not need the experience or items, running away is a safe option.
+#    - **Cons**: Running away means you miss out on the experience points and potential items or money that you could gain from defeating the Pidgey. It also means you do not get the chance to capture the Pidgey if you wanted to.
 
-# 4. **Turkey** - Color: Light Green (east part of the map) - City: Istanbul
-#    - Turkish cuisine is diverse and includes dishes like kebabs, meze, and baklava.
+# ### Recommendation:
+# Given the significant level advantage, the best action is likely to **FIGHT**. This will allow you to quickly defeat the Pidgey, gain experience points, and potentially earn items or money. If you are concerned about Pikachu's health, you could use an item from your **BAG** to heal it before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.
 ```
+</details>
 
-### Function calling
+#### Function calling
 
-Mistral-Small-3.1-24-Instruct-2503 is excellent at function / tool calling tasks via vLLM. *E.g.:*
+Mistral-Small-3.2-24B-Instruct-2506 is excellent at function / tool calling tasks via vLLM. *E.g.:*
 
 <details>
-  <summary>Example</summary>
+  <summary>Python snippet - easy</summary>
 
 ```py
-import requests
-import json
+from openai import OpenAI
 from huggingface_hub import hf_hub_download
-from datetime import datetime, timedelta
 
-url = "http://<your-url>:8000/v1/chat/completions"
-headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
+# 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 = 0.15
+MAX_TOK = 131072
 
-model = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
 
+models = client.models.list()
+model = models.data[0].id
 
 def load_system_prompt(repo_id: str, filename: str) -> str:
     file_path = hf_hub_download(repo_id=repo_id, filename=filename)
     with open(file_path, "r") as file:
         system_prompt = file.read()
-    today = datetime.today().strftime("%Y-%m-%d")
-    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
-    model_name = repo_id.split("/")[-1]
-    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
+    return system_prompt
 
+model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
+SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
 
-SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
-
+image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"
 
 tools = [
     {
         "type": "function",
         "function": {
-            "name": "get_current_weather",
-            "description": "Get the current weather in a given location",
+            "name": "get_current_population",
+            "description": "Get the up-to-date population of a given country.",
             "parameters": {
                 "type": "object",
                 "properties": {
-                    "city": {
-                        "type": "string",
-                        "description": "The city to find the weather for, e.g. 'San Francisco'",
-                    },
-                    "state": {
+                    "country": {
                         "type": "string",
-                        "description": "The state abbreviation, e.g. 'CA' for California",
+                        "description": "The country to find the population of.",
                     },
                     "unit": {
                         "type": "string",
-                        "description": "The unit for temperature",
-                        "enum": ["celsius", "fahrenheit"],
+                        "description": "The unit for the population.",
+                        "enum": ["millions", "thousands"],
                     },
                 },
-                "required": ["city", "state", "unit"],
+                "required": ["country", "unit"],
             },
         },
     },
@@ -370,65 +338,361 @@ messages = [
     },
     {
         "role": "user",
-        "content": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?",
+        "content": [
+            {
+                "type": "text",
+                "text": "Can you tell me what is the biggest country depicted on the map?",
+            },
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url,
+                },
+            },
+        ],
+    }
+]
+
+response = client.chat.completions.create(
+    model=model,
+    messages=messages,
+    temperature=TEMP,
+    max_tokens=MAX_TOK,
+    tools=tools,
+    tool_choice="auto",
+)
+
+assistant_message = response.choices[0].message.content
+print(assistant_message)
+# The biggest country depicted on the map is Russia.
+
+messages.extend([
+    {"role": "assistant", "content": assistant_message},
+    {"role": "user", "content": "What is the population of that country in millions?"},
+])
+
+response = client.chat.completions.create(
+    model=model,
+    messages=messages,
+    temperature=TEMP,
+    max_tokens=MAX_TOK,
+    tools=tools,
+    tool_choice="auto",
+)
+
+print(response.choices[0].message.tool_calls)
+# [ChatCompletionMessageToolCall(id='3e92V6Vfo', function=Function(arguments='{"country": "Russia", "unit": "millions"}', name='get_current_population'), type='function')]
+```
+
+</details>
+
+<details>
+  <summary>Python snippet - complex</summary>
+
+```python
+import json
+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 = 0.15
+MAX_TOK = 131072
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+models = client.models.list()
+model = models.data[0].id
+
+
+def load_system_prompt(repo_id: str, filename: str) -> str:
+    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    with open(file_path, "r") as file:
+        system_prompt = file.read()
+    return system_prompt
+
+
+model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
+SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
+
+image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"
+
+
+def my_calculator(expression: str) -> str:
+    return str(eval(expression))
+
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "my_calculator",
+            "description": "A calculator that can evaluate a mathematical expression.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "expression": {
+                        "type": "string",
+                        "description": "The mathematical expression to evaluate.",
+                    },
+                },
+                "required": ["expression"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "rewrite",
+            "description": "Rewrite a given text for improved clarity",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "text": {
+                        "type": "string",
+                        "description": "The input text to rewrite",
+                    }
+                },
+            },
+        },
     },
 ]
 
-data = {"model": model, "messages": messages, "tools": tools, "temperature": 0.15}
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "Can you calculate the results for all the equations displayed in the image? Only compute the ones that involve numbers.",
+            },
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url,
+                },
+            },
+        ],
+    },
+]
 
-response = requests.post(url, headers=headers, data=json.dumps(data))
-print(response.json()["choices"][0]["message"]["tool_calls"])
-# [{'id': '8PdihwL6d', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'}}]
+response = client.chat.completions.create(
+    model=model,
+    messages=messages,
+    temperature=TEMP,
+    max_tokens=MAX_TOK,
+    tools=tools,
+    tool_choice="auto",
+)
+
+tool_calls = response.choices[0].message.tool_calls
+print(tool_calls)
+# [ChatCompletionMessageToolCall(id='CyQBSAtGh', function=Function(arguments='{"expression": "6 + 2 * 3"}', name='my_calculator'), type='function'), ChatCompletionMessageToolCall(id='KQqRCqvzc', function=Function(arguments='{"expression": "19 - (8 + 2) + 1"}', name='my_calculator'), type='function')]
+
+results = []
+for tool_call in tool_calls:
+    function_name = tool_call.function.name
+    function_args = tool_call.function.arguments
+    if function_name == "my_calculator":
+        result = my_calculator(**json.loads(function_args))
+        results.append(result)
+
+messages.append({"role": "assistant", "tool_calls": tool_calls})
+for tool_call, result in zip(tool_calls, results):
+    messages.append(
+        {
+            "role": "tool",
+            "tool_call_id": tool_call.id,
+            "name": tool_call.function.name,
+            "content": result,
+        }
+    )
+
+
+response = client.chat.completions.create(
+    model=model,
+    messages=messages,
+    temperature=TEMP,
+    max_tokens=MAX_TOK,
+)
+
+print(response.choices[0].message.content)
+# Here are the results for the equations that involve numbers:
+
+# 1. \( 6 + 2 \times 3 = 12 \)
+# 3. \( 19 - (8 + 2) + 1 = 10 \)
+
+# For the other equations, you need to substitute the variables with specific values to compute the results.
 ```
 
 </details>
 
-#### Offline
+#### Instruction following
 
-```py
-from vllm import LLM
-from vllm.sampling_params import SamplingParams
-from datetime import datetime, timedelta
+Mistral-Small-3.2-24B-Instruct-2506 will follow your instructions down to the last letter ! 
 
-SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
+<details>
+  <summary>Python snippet</summary>
+
+```python
+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 = 0.15
+MAX_TOK = 131072
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
 
-user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
+models = client.models.list()
+model = models.data[0].id
+
+
+def load_system_prompt(repo_id: str, filename: str) -> str:
+    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    with open(file_path, "r") as file:
+        system_prompt = file.read()
+    return system_prompt
+
+
+model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
+SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
 
 messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
     {
-        "role": "system",
-        "content": SYSTEM_PROMPT
+        "role": "user",
+        "content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.",
     },
+]
+
+response = client.chat.completions.create(
+    model=model,
+    messages=messages,
+    temperature=TEMP,
+    max_tokens=MAX_TOK,
+)
+
+assistant_message = response.choices[0].message.content
+print(assistant_message)
+
+# Here's a sentence where each word starts with the next letter of the alphabet, starting from 'a' and ending with 'z':
+
+# "Always brave cats dance elegantly, fluffy giraffes happily ignore jungle kites, lovingly munching nuts, observing playful quails racing swiftly, tiny unicorns vaulting while xylophones yodel zealously."
+
+# This sentence follows the sequence from A to Z without skipping any letters.
+```
+</details>
+
+### Transformers
+
+You can also use Mistral-Small-3.2-24B-Instruct-2506 with `Transformers` !
+
+To make the best use of our model with `Transformers` make sure to have [installed](https://github.com/mistralai/mistral-common) `mistral-common >= 1.6.2` 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
+from datetime import datetime, timedelta
+import torch
+
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from huggingface_hub import hf_hub_download
+from transformers import Mistral3ForConditionalGeneration
+
+
+def load_system_prompt(repo_id: str, filename: str) -> str:
+    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    with open(file_path, "r") as file:
+        system_prompt = file.read()
+    today = datetime.today().strftime("%Y-%m-%d")
+    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
+    model_name = repo_id.split("/")[-1]
+    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
+
+
+model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
+SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
+
+tokenizer = MistralTokenizer.from_hf_hub(model_id)
+
+model = Mistral3ForConditionalGeneration.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16
+)
+
+image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
+
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
     {
         "role": "user",
-        "content": user_prompt
+        "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}},
+        ],
     },
 ]
-model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
-# note that running this model on GPU requires over 60 GB of GPU RAM
-llm = LLM(model=model_name, tokenizer_mode="mistral")
-
-sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
-outputs = llm.chat(messages, sampling_params=sampling_params)
-
-print(outputs[0].outputs[0].text)
-# Here are five non-formal ways to say "See you later" in French:
-
-# 1. **À plus tard** - Until later
-# 2. **À toute** - See you soon (informal)
-# 3. **Salut** - Bye (can also mean hi)
-# 4. **À plus** - See you later (informal)
-# 5. **Ciao** - Bye (informal, borrowed from Italian)
-
-# ```
-#  /\_/\
-# ( o.o )
-#  > ^ <
-# ```
-```
 
-### Transformers (untested)
+tokenized = tokenizer.encode_chat_completion(ChatCompletionRequest(messages=messages))
+
+input_ids = torch.tensor([tokenized.tokens])
+attention_mask = torch.ones_like(input_ids)
+pixel_values = torch.tensor(tokenized.images[0], dtype=torch.bfloat16).unsqueeze(0)
+image_sizes = torch.tensor([pixel_values.shape[-2:]])
+
+output = model.generate(
+    input_ids=input_ids,
+    attention_mask=attention_mask,
+    pixel_values=pixel_values,
+    image_sizes=image_sizes,
+    max_new_tokens=1000,
+)[0]
+
+decoded_output = tokenizer.decode(output[len(tokenized.tokens) :])
+print(decoded_output)
+# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:
+
+# 1. **FIGHT**:
+#    - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
+#    - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.
+
+# 2. **BAG**:
+#    - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture Pidgey or heal Pikachu if needed.
+#    - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat Pidgey quickly.
+
+# 3. **POKÉMON**:
+#    - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be strategic if you want to train a lower-level Pokémon.
+#    - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.
+
+# 4. **RUN**:
+#    - **Pros**: Running away could be a quick way to avoid the battle altogether. This might be useful if you are trying to conserve resources or if you are in a hurry to get to another location.
+#    - **Cons**: Running away means you miss out on the experience points, items, or money that you could gain from defeating Pidgey. It also might not be the most efficient use of your time if you are trying to train your Pokémon.
+
+# ### Recommendation:
+# Given the significant level advantage, the best action to take is likely **FIGHT**. This will allow you to quickly defeat Pidgey and gain experience points for Pikachu. If you are concerned about Pikachu's health, you could use the **BAG** to heal Pikachu before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.
+```
 
-Transformers-compatible model weights are also uploaded (thanks a lot @cyrilvallez). 
-However the transformers implementation was **not throughly tested**, but only on "vibe-checks".
-Hence, we can only ensure 100% correct behavior when using the original weight format with vllm (see above).
+</details>