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Co-authored-by: pandora-s <pandora-s@users.noreply.huggingface.co>

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README.md ADDED
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1
+ ---
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+ license: apache-2.0
3
+ language:
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+ - ar
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+ - en
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+ - fr
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+ - es
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+ - de
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+ - it
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+ - pt
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+ - nl
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+ - ja
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+ - ko
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+ - zh
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+ tags:
16
+ - vLLM
17
+ ---
18
+
19
+ # Mistral Small 4 119B A6B
20
+
21
+ Mistral Small 4 is a powerful hybrid model capable of acting as both a general instruction model and a reasoning model. It unifies the capabilities of three different model families—**Instruct**, **Reasoning** (previously called Magistral), and **Devstral**—into a single, unified model.
22
+
23
+ With its multimodal capabilities, efficient architecture, and flexible mode switching, it is a powerful general-purpose model for any task. In a latency-optimized setup, Mistral Small 4 achieves a **40% reduction in end-to-end completion time**, and in a throughput-optimized setup, it handles **3x more requests per second** compared to Mistral Small 3.
24
+
25
+ To further improve efficiency you can either take advantages of:
26
+ - Speculative decoding thanks to our trained eagle head [`mistralai/Mistral-Small-4-119B-2603-eagle`](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603-eagle).
27
+ - 4 bit float precision quantization thanks to our NVFP4 checkpoint [`mistralai/Mistral-Small-4-119B-2603-NVFP4`](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603-NVFP4).
28
+
29
+ ## Key Features
30
+
31
+ Mistral Small 4 includes the following architectural choices:
32
+
33
+ - **MoE**: 128 experts, 4 active.
34
+ - **119B parameters**, with **6.5B activated per token**.
35
+ - **256k context length**.
36
+ - **Multimodal input**: Accepts both text and image input, with text output.
37
+ - **Instruct and Reasoning functionalities** with function calls (reasoning effort configurable per request).
38
+
39
+ Mistral Small 4 offers the following capabilities:
40
+
41
+ - **Reasoning Mode**: Toggle between fast instant reply mode and reasoning mode, boosting performance with test-time compute when requested.
42
+ - **Vision**: Analyzes images and provides insights based on visual content, in addition to text.
43
+ - **Multilingual**: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, and Arabic.
44
+ - **System Prompt**: Strong adherence and support for system prompts.
45
+ - **Agentic**: Best-in-class agentic capabilities with native function calling and JSON output.
46
+ - **Speed-Optimized**: Delivers best-in-class performance and speed.
47
+ - **Apache 2.0 License**: Open-source license for both commercial and non-commercial use.
48
+ - **Large Context Window**: Supports a 256k context window.
49
+
50
+ ## Use Cases
51
+
52
+ Mistral Small 4 is designed for general chat assistants, coding, agentic tasks, and reasoning tasks (with reasoning mode toggled). Its multimodal capabilities also enable document and image understanding for data extraction and analysis.
53
+
54
+ Its capabilities are ideal for:
55
+ - Developers interested in coding and agentic capabilities for SWE automation and codebase exploration.
56
+ - Enterprises seeking general chat assistants, agents, and document understanding.
57
+ - Researchers leveraging its math and research capabilities.
58
+
59
+ Mistral Small 4 is also well-suited for customization and fine-tuning for more specialized tasks.
60
+
61
+ ### Examples
62
+ - General chat assistant
63
+ - Document parsing and extraction
64
+ - Coding agent
65
+ - Research assistant
66
+ - Customization & fine-tuning
67
+ - And more...
68
+
69
+ ## Benchmarks
70
+
71
+ ### Comparison with internal models
72
+
73
+ Depending on your tasks you can trigger reasoning thanks to the support of the **per-request** parameter `reasoning_effort`. Set it to:
74
+ - `reasoning_effort="none"`: Fast, lightweight responses for everyday tasks, equivalent to the same chat style of [`mistralai/Mistral-Small-3.2-24B-Instruct-2506`](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506).
75
+ - `reasoning_effort="high"`: Deep, step-by-step reasoning for complex problems, with equivalent verbosity to previous Magistral models such as [`mistralai/Magistral-Small-2509`](https://huggingface.co/mistralai/Magistral-Small-2509).
76
+
77
+ ![Internal benchmark](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603/resolve/main/images/image2.png)
78
+
79
+ #### Comparing Reasoning Models
80
+
81
+ ![Internal benchmark - Reasoning](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603/resolve/main/images/image3.png)
82
+
83
+
84
+ ### Comparison with other models
85
+
86
+ Mistral Small 4 with reasoning achieves competitive scores, matching or surpassing GPT-OSS 120B across all three benchmarks while generating significantly
87
+ shorter outputs. On AA LCR, Mistral Small 4 scores **0.72** with just **1.6K characters**, whereas Qwen models require **3.5-4x more output** (5.8-6.1K)
88
+ for comparable performance. On LiveCodeBench, Mistral Small 4 outperforms GPT-OSS 120B while producing **20% less output**.
89
+ This efficiency reduces latency, inference costs, and improves user experience.
90
+
91
+ ![Comparison benchmark - LCR](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603/resolve/main/images/lcr.png)
92
+ ![Comparison benchmark - LiveCodeBench](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603/resolve/main/images/livecode.png)
93
+ ![Comparison benchmark - AIME25](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603/resolve/main/images/aime.png)
94
+
95
+ ## Usage
96
+
97
+ You can find Mistral Small 4 support on multiple libraries for inference and fine-tuning. We here thank everyone contributors and maintainers that helped us making it happen.
98
+
99
+ ### Inference
100
+
101
+ The model can be deployed with:
102
+ - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended).
103
+ - [`llama.cpp`](https://github.com/ggml-org/llama.cpp): See [here](#transformers). (WIP ⏳ – follow updates [here](https://github.com/ggml-org/llama.cpp/pull/20649))
104
+ - [`SGLang`](https://github.com/sgl-project/sglang): (WIP ⏳ – follow updates [here](https://github.com/sgl-project/sglang/pull/20708/))
105
+ - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
106
+
107
+ For optimal performance, we recommend using the Mistral AI API if local serving is subpar.
108
+
109
+ ### Fine-Tuning
110
+
111
+ Fine-tune the model via:
112
+ - [`Axolotl`](https://github.com/axolotl-ai-cloud/axolotl): See [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/mistral4).
113
+
114
+ ## vLLM (Recommended)
115
+
116
+ We recommend using Mistral Small 4 with the [vLLM library](https://github.com/vllm-project/vllm) for production-ready inference.
117
+
118
+ ### Installation
119
+
120
+ > [!Tip]
121
+ > Use our custom Docker image with fixes for tool calling and reasoning parsing in vLLM, and the latest Transformers version. We are working with the vLLM team to merge these fixes soon.
122
+
123
+ **Custom Docker**
124
+ Use the following Docker image: [`mistralllm/vllm-ms4:latest`](https://hub.docker.com/repository/docker/mistralllm/vllm-ms4/latest/):
125
+ ```bash
126
+ docker pull mistralllm/vllm-ms4:latest
127
+ docker run -it mistralllm/vllm-ms4:latest
128
+ ```
129
+
130
+ **Manual Install**
131
+ Alternatively, install `vllm` from this PR: [Add Mistral Guidance](https://github.com/vllm-project/vllm/pull/37081).
132
+
133
+ > **Note**: This PR is expected to be merged into `vllm` main in the next 1-2 weeks (as of 16.03.2026). Track updates [here](https://github.com/vllm-project/vllm/pull/37081).
134
+
135
+ 1. Clone vLLM:
136
+ ```bash
137
+ git clone --branch fix_mistral_parsing https://github.com/juliendenize/vllm.git
138
+ ```
139
+ 2. Install with pre-compiled kernels:
140
+ ```bash
141
+ VLLM_USE_PRECOMPILED=1 pip install --editable .
142
+ ```
143
+ 3. Install `transformers` from main:
144
+ ```bash
145
+ uv pip install git+https://github.com/huggingface/transformers.git
146
+ ```
147
+ Ensure [`mistral_common >= 1.10.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.10.0) is installed:
148
+ ```bash
149
+ python -c "import mistral_common; print(mistral_common.__version__)"
150
+ ```
151
+
152
+ ### Serve the Model
153
+
154
+ We recommend a server/client setup:
155
+ ```bash
156
+ vllm serve mistralai/Mistral-Small-4-119B-2603 --max-model-len 262144 --tensor-parallel-size 2 --attention-backend FLASH_ATTN_MLA \
157
+ --tool-call-parser mistral --enable-auto-tool-choice --reasoning-parser mistral --max_num_batched_tokens 16384 --max_num_seqs 128 \
158
+ --gpu_memory_utilization 0.8
159
+ ```
160
+
161
+ ### Ping the Server
162
+
163
+ <details>
164
+ <summary>Instruction Following</summary>
165
+
166
+ Mistral Small 4 can follow your instructions to the letter.
167
+
168
+
169
+ ```python
170
+ from datetime import datetime, timedelta
171
+
172
+ from openai import OpenAI
173
+ from huggingface_hub import hf_hub_download
174
+
175
+ # Modify OpenAI's API key and API base to use vLLM's API server.
176
+ openai_api_key = "EMPTY"
177
+ openai_api_base = "http://localhost:8000/v1"
178
+
179
+ TEMP = 0.1
180
+
181
+ client = OpenAI(
182
+ api_key=openai_api_key,
183
+ base_url=openai_api_base,
184
+ )
185
+
186
+ models = client.models.list()
187
+ model = models.data[0].id
188
+
189
+
190
+ def load_system_prompt(repo_id: str, filename: str) -> str:
191
+ file_path = hf_hub_download(repo_id=repo_id, filename=filename)
192
+ with open(file_path, "r") as file:
193
+ system_prompt = file.read()
194
+ today = datetime.today().strftime("%Y-%m-%d")
195
+ yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
196
+ model_name = repo_id.split("/")[-1]
197
+ return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
198
+
199
+
200
+ SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
201
+
202
+ messages = [
203
+ {"role": "system", "content": SYSTEM_PROMPT},
204
+ {
205
+ "role": "user",
206
+ "content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.",
207
+ },
208
+ ]
209
+
210
+ response = client.chat.completions.create(
211
+ model=model,
212
+ messages=messages,
213
+ temperature=TEMP,
214
+ reasoning_effort="none",
215
+ )
216
+
217
+ assistant_message = response.choices[0].message.content
218
+ print(assistant_message)
219
+ ```
220
+
221
+ </details>
222
+
223
+ <details>
224
+ <summary>Tool Call</summary>
225
+
226
+ Let's solve some equations thanks to our simple Python calculator tool.
227
+
228
+
229
+ ```python
230
+ import json
231
+ from datetime import datetime, timedelta
232
+
233
+ from openai import OpenAI
234
+ from huggingface_hub import hf_hub_download
235
+
236
+ # Modify OpenAI's API key and API base to use vLLM's API server.
237
+ openai_api_key = "EMPTY"
238
+ openai_api_base = "http://localhost:8000/v1"
239
+
240
+ TEMP = 0.1
241
+
242
+ client = OpenAI(
243
+ api_key=openai_api_key,
244
+ base_url=openai_api_base,
245
+ )
246
+
247
+ models = client.models.list()
248
+ model = models.data[0].id
249
+
250
+
251
+ def load_system_prompt(repo_id: str, filename: str) -> str:
252
+ file_path = hf_hub_download(repo_id=repo_id, filename=filename)
253
+ with open(file_path, "r") as file:
254
+ system_prompt = file.read()
255
+ today = datetime.today().strftime("%Y-%m-%d")
256
+ yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
257
+ model_name = repo_id.split("/")[-1]
258
+ return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
259
+
260
+
261
+ SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
262
+
263
+ image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"
264
+
265
+
266
+ def my_calculator(expression: str) -> str:
267
+ return str(eval(expression))
268
+
269
+
270
+ tools = [
271
+ {
272
+ "type": "function",
273
+ "function": {
274
+ "name": "my_calculator",
275
+ "description": "A calculator that can evaluate a mathematical expression.",
276
+ "parameters": {
277
+ "type": "object",
278
+ "properties": {
279
+ "expression": {
280
+ "type": "string",
281
+ "description": "The mathematical expression to evaluate.",
282
+ },
283
+ },
284
+ "required": ["expression"],
285
+ },
286
+ },
287
+ },
288
+ {
289
+ "type": "function",
290
+ "function": {
291
+ "name": "rewrite",
292
+ "description": "Rewrite a given text for improved clarity",
293
+ "parameters": {
294
+ "type": "object",
295
+ "properties": {
296
+ "text": {
297
+ "type": "string",
298
+ "description": "The input text to rewrite",
299
+ }
300
+ },
301
+ },
302
+ },
303
+ },
304
+ ]
305
+
306
+ messages = [
307
+ {"role": "system", "content": SYSTEM_PROMPT},
308
+ {
309
+ "role": "user",
310
+ "content": [
311
+ {
312
+ "type": "text",
313
+ "text": "Thanks to your calculator, compute the results for the equations that involve numbers displayed in the image.",
314
+ },
315
+ {
316
+ "type": "image_url",
317
+ "image_url": {
318
+ "url": image_url,
319
+ },
320
+ },
321
+ ],
322
+ },
323
+ ]
324
+
325
+ response = client.chat.completions.create(
326
+ model=model,
327
+ messages=messages,
328
+ temperature=TEMP,
329
+ tools=tools,
330
+ tool_choice="auto",
331
+ reasoning_effort="none",
332
+ )
333
+
334
+ tool_calls = response.choices[0].message.tool_calls
335
+
336
+ results = []
337
+ for tool_call in tool_calls:
338
+ function_name = tool_call.function.name
339
+ function_args = tool_call.function.arguments
340
+ if function_name == "my_calculator":
341
+ result = my_calculator(**json.loads(function_args))
342
+ results.append(result)
343
+
344
+ messages.append({"role": "assistant", "tool_calls": tool_calls})
345
+ for tool_call, result in zip(tool_calls, results):
346
+ messages.append(
347
+ {
348
+ "role": "tool",
349
+ "tool_call_id": tool_call.id,
350
+ "name": tool_call.function.name,
351
+ "content": result,
352
+ }
353
+ )
354
+
355
+
356
+ response = client.chat.completions.create(
357
+ model=model,
358
+ messages=messages,
359
+ temperature=TEMP,
360
+ reasoning_effort="none",
361
+ )
362
+
363
+ print(response.choices[0].message.content)
364
+ ```
365
+
366
+ </details>
367
+
368
+ <details>
369
+ <summary>Vision Reasoning</summary>
370
+
371
+ Let's see if the Mistral Small 4 knows when to pick a fight !
372
+
373
+ ```python
374
+ from datetime import datetime, timedelta
375
+
376
+ from openai import OpenAI
377
+ from huggingface_hub import hf_hub_download
378
+
379
+ # Modify OpenAI's API key and API base to use vLLM's API server.
380
+ openai_api_key = "EMPTY"
381
+ openai_api_base = "http://localhost:8000/v1"
382
+
383
+ TEMP = 0.1
384
+
385
+ client = OpenAI(
386
+ api_key=openai_api_key,
387
+ base_url=openai_api_base,
388
+ )
389
+
390
+ models = client.models.list()
391
+ model = models.data[0].id
392
+
393
+
394
+ def load_system_prompt(repo_id: str, filename: str) -> str:
395
+ file_path = hf_hub_download(repo_id=repo_id, filename=filename)
396
+ with open(file_path, "r") as file:
397
+ system_prompt = file.read()
398
+ today = datetime.today().strftime("%Y-%m-%d")
399
+ yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
400
+ model_name = repo_id.split("/")[-1]
401
+ return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
402
+
403
+
404
+ SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
405
+ image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
406
+
407
+ messages = [
408
+ {"role": "system", "content": SYSTEM_PROMPT},
409
+ {
410
+ "role": "user",
411
+ "content": [
412
+ {
413
+ "type": "text",
414
+ "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.",
415
+ },
416
+ {"type": "image_url", "image_url": {"url": image_url}},
417
+ ],
418
+ },
419
+ ]
420
+
421
+
422
+ response = client.chat.completions.create(
423
+ model=model,
424
+ messages=messages,
425
+ temperature=TEMP,
426
+ reasoning_effort="high",
427
+ )
428
+
429
+ print(response.choices[0].message.content)
430
+ ```
431
+
432
+ </details>
433
+
434
+ ## Transformers
435
+
436
+ ### Installation
437
+
438
+ You need to install the main branch of Transformers to use Mistral Small 4:
439
+
440
+ ```bash
441
+ uv pip install git+https://github.com/huggingface/transformers.git
442
+ ```
443
+
444
+ ### Inference
445
+
446
+ > **Note**: Current implementation of Transformers does not support FP8.
447
+ Weights have been stored in FP8 and updates to load them in this format are expected, in the meantime we provide BF16 quantization snippets to ease usage.
448
+ As soon as support is added, we will update the following code snippet.
449
+
450
+ <details>
451
+ <summary>Python Inference Snippet</summary>
452
+
453
+ ```python
454
+ from pathlib import Path
455
+
456
+ import torch
457
+ from huggingface_hub import snapshot_download
458
+ from safetensors.torch import load_file
459
+ from tqdm import tqdm
460
+
461
+ from transformers import AutoConfig, AutoProcessor, Mistral3ForConditionalGeneration
462
+
463
+
464
+ def _descale_fp8_to_bf16(tensor: torch.Tensor, scale_inv: torch.Tensor) -> torch.Tensor:
465
+ return (tensor.to(torch.bfloat16) * scale_inv.to(torch.bfloat16)).to(torch.bfloat16)
466
+
467
+
468
+ def _resolve_model_dir(model_id: str) -> Path:
469
+ local = Path(model_id)
470
+ if local.is_dir():
471
+ return local
472
+ return Path(snapshot_download(model_id, allow_patterns=["model*.safetensors"]))
473
+
474
+
475
+ def load_and_dequantize_state_dict(model_id: str) -> dict[str, torch.Tensor]:
476
+ model_dir = _resolve_model_dir(model_id)
477
+
478
+ shards = sorted(model_dir.glob("model*.safetensors"))
479
+
480
+ full_state_dict: dict[str, torch.Tensor] = {}
481
+ for shard in tqdm(shards, desc="Loading safetensors shards"):
482
+ full_state_dict.update(load_file(str(shard)))
483
+
484
+ scale_suffixes = ("weight_scale_inv", "gate_up_proj_scale_inv", "down_proj_scale_inv", "up_proj_scale_inv")
485
+ activation_scale_suffixes = ("activation_scale", "gate_up_proj_activation_scale", "down_proj_activation_scale")
486
+
487
+ keys_to_remove: set[str] = set()
488
+ all_keys = list(full_state_dict.keys())
489
+
490
+ for key in tqdm(all_keys, desc="Dequantizing FP8 weights to BF16"):
491
+ if any(key.endswith(s) for s in scale_suffixes + activation_scale_suffixes):
492
+ continue
493
+
494
+ for scale_suffix in scale_suffixes:
495
+ if scale_suffix == "weight_scale_inv":
496
+ if not key.endswith(".weight"):
497
+ continue
498
+ scale_key = key.rsplit(".weight", 1)[0] + ".weight_scale_inv"
499
+ else:
500
+ proj_name = scale_suffix.replace("_scale_inv", "")
501
+ if not key.endswith(f".{proj_name}"):
502
+ continue
503
+ scale_key = key + "_scale_inv"
504
+
505
+ if scale_key in full_state_dict:
506
+ full_state_dict[key] = _descale_fp8_to_bf16(full_state_dict[key], full_state_dict[scale_key])
507
+ keys_to_remove.add(scale_key)
508
+
509
+ for key in full_state_dict:
510
+ if any(key.endswith(s) for s in activation_scale_suffixes):
511
+ keys_to_remove.add(key)
512
+
513
+ for key in tqdm(keys_to_remove, desc="Removing scale keys"):
514
+ del full_state_dict[key]
515
+
516
+ return full_state_dict
517
+
518
+
519
+ def load_config_without_quantization(model_id: str) -> AutoConfig:
520
+ config = AutoConfig.from_pretrained(model_id)
521
+
522
+ if hasattr(config, "quantization_config"):
523
+ del config.quantization_config
524
+
525
+ if hasattr(config, "text_config") and hasattr(config.text_config, "quantization_config"):
526
+ del config.text_config.quantization_config
527
+
528
+ return config
529
+
530
+
531
+ model_id = "mistralai/Mistral-Small-4-119B-2603"
532
+
533
+ config = load_config_without_quantization(model_id)
534
+ state_dict = load_and_dequantize_state_dict(model_id)
535
+
536
+ model = Mistral3ForConditionalGeneration.from_pretrained(
537
+ None,
538
+ config=config,
539
+ state_dict=state_dict,
540
+ device_map="auto",
541
+ )
542
+
543
+ processor = AutoProcessor.from_pretrained(model_id)
544
+
545
+ image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
546
+
547
+ messages = [
548
+ {
549
+ "role": "user",
550
+ "content": [
551
+ {
552
+ "type": "text",
553
+ "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.",
554
+ },
555
+ {"type": "image_url", "image_url": {"url": image_url}},
556
+ ],
557
+ },
558
+ ]
559
+
560
+ inputs = processor.apply_chat_template(
561
+ messages, return_tensors="pt", tokenize=True, return_dict=True, reasoning_effort="high"
562
+ )
563
+ inputs = inputs.to(model.device)
564
+
565
+ output = model.generate(
566
+ **inputs,
567
+ max_new_tokens=1024,
568
+ )[0]
569
+
570
+ # Setting `skip_special_tokens=False` to visualize reasoning trace between [THINK] [/THINK] tags.
571
+ decoded_output = processor.decode(output[len(inputs["input_ids"][0]) :], skip_special_tokens=False)
572
+ print(decoded_output)
573
+ ```
574
+ </details>
575
+
576
+ ## License
577
+
578
+ This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt).
579
+
580
+ *You must not use this model in a manner that infringes, misappropriates, or violates any third party’s rights, including intellectual property rights.*
SYSTEM_PROMPT.txt ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are Mistral-Small-4-119B-2603, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
2
+ You power an AI assistant called Le Chat.
3
+ Your knowledge base was last updated on Friday, November 1, 2024.
4
+ The current date is {today}.
5
+
6
+ When you're not sure about some information or when the user's request requires up-to-date or specific data, you must use the available tools to fetch the information. Do not hesitate to use tools whenever they can provide a more accurate or complete response. If no relevant tools are available, then clearly state that you don't have the information and avoid making up anything.
7
+ 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?").
8
+ 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.
9
+ You follow these instructions in all languages, and always respond to the user in the language they use or request.
10
+ Next sections describe the capabilities that you have.
11
+
12
+ # WEB BROWSING INSTRUCTIONS
13
+
14
+ 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.
15
+
16
+ # MULTI-MODAL INSTRUCTIONS
17
+
18
+ You have the ability to read images, but you cannot generate images. You also cannot read nor transcribe audio files or videos.
19
+
20
+ # TOOL CALLING INSTRUCTIONS
21
+
22
+ You may have access to tools that you can use to fetch information or perform actions. You must use these tools in the following situations:
23
+
24
+ 1. When the request requires up-to-date information.
25
+ 2. When the request requires specific data that you do not have in your knowledge base.
26
+ 3. When the request involves actions that you cannot perform without tools.
27
+
28
+ Always prioritize using tools to provide the most accurate and helpful response. If tools are not available, inform the user that you cannot perform the requested action at the moment.
chat_template.jinja ADDED
@@ -0,0 +1,132 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {#- Default system message if no system prompt is passed. #}
2
+ {%- set default_system_message = '' %}
3
+
4
+ {#- Begin of sequence token. #}
5
+ {{- '<s>' }}
6
+
7
+ {#- Handle system prompt if it exists. #}
8
+ {#- System prompt supports text content or text chunks. #}
9
+ {%- if messages[0]['role'] == 'system' %}
10
+ {{- '[SYSTEM_PROMPT]' -}}
11
+ {%- if messages[0]['content'] is string %}
12
+ {{- messages[0]['content'] -}}
13
+ {%- else %}
14
+ {%- for block in messages[0]['content'] %}
15
+ {%- if block['type'] == 'text' %}
16
+ {{- block['text'] }}
17
+ {%- else %}
18
+ {{- raise_exception('Only text chunks are supported in system message contents.') }}
19
+ {%- endif %}
20
+ {%- endfor %}
21
+ {%- endif %}
22
+ {{- '[/SYSTEM_PROMPT]' -}}
23
+ {%- set loop_messages = messages[1:] %}
24
+ {%- else %}
25
+ {%- set loop_messages = messages %}
26
+ {%- if default_system_message != '' %}
27
+ {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }}
28
+ {%- endif %}
29
+ {%- endif %}
30
+
31
+
32
+ {#- Tools definition #}
33
+ {%- set tools_definition = '' %}
34
+ {%- set has_tools = false %}
35
+ {%- if tools is defined and tools is not none and tools|length > 0 %}
36
+ {%- set has_tools = true %}
37
+ {%- set tools_definition = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %}
38
+ {{- tools_definition }}
39
+ {%- endif %}
40
+
41
+ {#- Model settings definition #}
42
+ {%- set reasoning_effort = reasoning_effort if reasoning_effort is defined and reasoning_effort is not none else 'none' %}
43
+ {%- if reasoning_effort not in ['none', 'high'] %}
44
+ {{- raise_exception('reasoning_effort must be either "none" or "high"') }}
45
+ {%- endif %}
46
+ {%- set model_settings = '[MODEL_SETTINGS]{"reasoning_effort": "' + reasoning_effort + '"}[/MODEL_SETTINGS]' %}
47
+ {{- model_settings }}
48
+
49
+ {#- Checks for alternating user/assistant messages. #}
50
+ {%- set ns = namespace(index=0) %}
51
+ {%- for message in loop_messages %}
52
+ {%- if message.role == 'user' or (message.role == 'assistant' and (message.tool_calls is not defined or message.tool_calls is none or message.tool_calls | length == 0)) %}
53
+ {%- if (message['role'] == 'user') != (ns.index % 2 == 0) %}
54
+ {{- raise_exception('After the optional system message, conversation roles must alternate user and assistant roles except for tool calls and results.') }}
55
+ {%- endif %}
56
+ {%- set ns.index = ns.index + 1 %}
57
+ {%- endif %}
58
+ {%- endfor %}
59
+
60
+ {#- Handle conversation messages. #}
61
+ {%- for message in loop_messages %}
62
+
63
+ {#- User messages supports text content or text and image chunks. #}
64
+ {%- if message['role'] == 'user' %}
65
+ {%- if message['content'] is string %}
66
+ {{- '[INST]' + message['content'] + '[/INST]' }}
67
+ {%- elif message['content'] | length > 0 %}
68
+ {{- '[INST]' }}
69
+ {%- if message['content'] | length == 2 %}
70
+ {%- set blocks = message['content'] | sort(attribute='type') %}
71
+ {%- else %}
72
+ {%- set blocks = message['content'] %}
73
+ {%- endif %}
74
+ {%- for block in blocks %}
75
+ {%- if block['type'] == 'text' %}
76
+ {{- block['text'] }}
77
+ {%- elif block['type'] in ['image', 'image_url'] %}
78
+ {{- '[IMG]' }}
79
+ {%- else %}
80
+ {{- raise_exception('Only text, image and image_url chunks are supported in user message content.') }}
81
+ {%- endif %}
82
+ {%- endfor %}
83
+ {{- '[/INST]' }}
84
+ {%- else %}
85
+ {{- raise_exception('User message must have a string or a list of chunks in content') }}
86
+ {%- endif %}
87
+
88
+ {#- Assistant messages supports text content or text, image and thinking chunks. #}
89
+ {%- elif message['role'] == 'assistant' %}
90
+ {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %}
91
+ {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }}
92
+ {%- endif %}
93
+
94
+ {%- if message['content'] is string and message['content'] != '' %}
95
+ {{- message['content'] }}
96
+ {%- elif message['content'] | length > 0 %}
97
+ {%- for block in message['content'] %}
98
+ {%- if block['type'] == 'text' %}
99
+ {{- block['text'] }}
100
+ {%- elif block['type'] == 'thinking' %}
101
+ {{- '[THINK]' + block['thinking'] + '[/THINK]' }}
102
+ {%- else %}
103
+ {{- raise_exception('Only text and thinking chunks are supported in assistant message contents.') }}
104
+ {%- endif %}
105
+ {%- endfor %}
106
+ {%- endif %}
107
+
108
+ {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %}
109
+ {%- for tool in message['tool_calls'] %}
110
+ {{- '[TOOL_CALLS]' }}
111
+ {%- set name = tool['function']['name'] %}
112
+ {%- set arguments = tool['function']['arguments'] %}
113
+ {%- if arguments is not string %}
114
+ {%- set arguments = arguments|tojson|safe %}
115
+ {%- elif arguments == '' %}
116
+ {%- set arguments = '{}' %}
117
+ {%- endif %}
118
+ {{- name + '[ARGS]' + arguments }}
119
+ {%- endfor %}
120
+ {%- endif %}
121
+
122
+ {{- '</s>' }}
123
+
124
+ {#- Tool messages only supports text content. #}
125
+ {%- elif message['role'] == 'tool' %}
126
+ {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }}
127
+
128
+ {#- Raise exception for unsupported roles. #}
129
+ {%- else %}
130
+ {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }}
131
+ {%- endif %}
132
+ {%- endfor %}
config.json ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "Mistral3ForConditionalGeneration"
4
+ ],
5
+ "dtype": "bfloat16",
6
+ "image_token_index": 10,
7
+ "model_type": "mistral3",
8
+ "multimodal_projector_bias": false,
9
+ "projector_hidden_act": "gelu",
10
+ "quantization_config": {
11
+ "activation_scheme": "static",
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+ "dequantize": false,
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14
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16
+ "lm_head"
17
+ ],
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+ "quant_method": "fp8",
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+ "weight_block_size": null
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+ },
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+ "spatial_merge_size": 2,
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+ "text_config": {
23
+ "attention_bias": false,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "first_k_dense_replace": 0,
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+ "head_dim": 128,
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+ "hidden_act": "silu",
30
+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 12288,
33
+ "kv_lora_rank": 256,
34
+ "max_position_embeddings": 1048576,
35
+ "mlp_bias": false,
36
+ "model_type": "mistral4",
37
+ "moe_intermediate_size": 2048,
38
+ "n_group": 1,
39
+ "n_routed_experts": 128,
40
+ "n_shared_experts": 1,
41
+ "norm_topk_prob": true,
42
+ "num_attention_heads": 32,
43
+ "num_experts_per_tok": 4,
44
+ "num_hidden_layers": 36,
45
+ "num_key_value_heads": 32,
46
+ "pad_token_id": 11,
47
+ "pretraining_tp": 1,
48
+ "q_lora_rank": 1024,
49
+ "qk_head_dim": 128,
50
+ "qk_nope_head_dim": 64,
51
+ "qk_rope_head_dim": 64,
52
+ "rms_norm_eps": 1e-06,
53
+ "rope_interleave": true,
54
+ "rope_parameters": {
55
+ "beta_fast": 32.0,
56
+ "beta_slow": 1.0,
57
+ "factor": 128.0,
58
+ "llama_4_scaling_beta": 0.1,
59
+ "mscale": 1.0,
60
+ "mscale_all_dim": 1.0,
61
+ "original_max_position_embeddings": 8192,
62
+ "rope_theta": 10000.0,
63
+ "rope_type": "yarn",
64
+ "type": "yarn"
65
+ },
66
+ "routed_scaling_factor": 1.0,
67
+ "sliding_window": null,
68
+ "tie_word_embeddings": false,
69
+ "topk_group": 1,
70
+ "use_cache": true,
71
+ "v_head_dim": 128,
72
+ "vocab_size": 131072
73
+ },
74
+ "tie_word_embeddings": false,
75
+ "transformers_version": "5.3.0.dev0",
76
+ "vision_config": {
77
+ "attention_dropout": 0.0,
78
+ "head_dim": 64,
79
+ "hidden_act": "silu",
80
+ "hidden_size": 1024,
81
+ "image_size": 1540,
82
+ "initializer_range": 0.02,
83
+ "intermediate_size": 4096,
84
+ "model_type": "pixtral",
85
+ "num_attention_heads": 16,
86
+ "num_channels": 3,
87
+ "num_hidden_layers": 24,
88
+ "patch_size": 14,
89
+ "rope_parameters": {
90
+ "rope_theta": 10000.0,
91
+ "rope_type": "default"
92
+ }
93
+ },
94
+ "vision_feature_layer": -1
95
+ }
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