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If you are affiliated with a commercial entity, we may also send you communications about our models. For more information on your rights and data handling, please see our privacy policy. extra_gated_button_content: Submit library_name: vllm pipeline_tag: image-text-to-text base_model: - mistralai/Pixtral-Large-Instruct-2411 --- # Model Card for Pixtral-Large-Instruct-2411 Pixtral-Large-Instruct-2411 is a 124B multimodal model built on top of Mistral Large 2, i.e., [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407). Pixtral Large is the second model in our multimodal family and demonstrates frontier-level image understanding. Particularly, the model is able to understand documents, charts and natural images, while maintaining the leading text-only understanding of Mistral Large 2. For more details about this model please refer to the [Pixtral Large blog post](https://mistral.ai/news/pixtral-large/) and the [Pixtral 12B blog post](https://mistral.ai/news/pixtral-12b/). ## Key features - Frontier-class multimodal performance - State-of-the-art on MathVista, DocVQA, VQAv2 - Extends Mistral Large 2 without compromising text performance - 123B multimodal decoder, 1B parameter vision encoder - 128K context window: fits minimum of 30 high-resolution images ### System Prompt Handling We appreciate the feedback received from our community regarding our system prompt handling. In response, we have implemented stronger support for system prompts. To achieve optimal results, we recommend always including a system prompt that clearly outlines the bot's purpose, even if it is minimal. ### Basic Instruct Template (V7) ``` [SYSTEM_PROMPT] [/SYSTEM_PROMPT][INST] [/INST] [INST] [/INST] ``` **Be careful with subtle missing or trailing white spaces!** *Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth* ## Metrics | Model | MathVista (CoT) | MMMU (CoT) | ChartQA (CoT) | DocVQA (ANLS) | VQAv2 (VQA Match) | AI2D (BBox) | MM MT-Bench | |:----------------------------:|:---------------:|:----------:|:-------------:|:--------------:|:-----------------:|:-----------:|:-----------:| | **Pixtral Large (124B)** | **69.4** | 64.0 | 88.1 | **93.3**| **80.9** | 93.8 | **7.4**| | Gemini-1.5 Pro (measured) | 67.8 | 66.3 | 83.8 | 92.3 | 70.6 | **94.6**| 6.8 | | GPT-4o (measured) | 65.4 | **68.6**| 85.2 | 88.5 | 76.4 | 93.2 | 6.7 | | Claude-3.5 Sonnet (measured) | 67.1 | 68.4 | **89.1**| 88.6 | 69.5 | 76.9 | 7.3 | | Llama-3.2 90B (measured) | 49.1 | 53.7 | 70.8 | 85.7 | 67.0 | - | 5.5 | Specific model versions evaluated: Claude-3.5 Sonnet (new) [Oct 24], Gemini-1.5 Pro (002) [Sep 24], GPT-4o (2024-08-06) [Aug 24]. See [mistral-evals](https://github.com/mistralai/mistral-evals) for open-source MM MT-Bench evaluation scripts. ## Usage The model can be used with the following frameworks - [`vllm`](https://github.com/vllm-project/vllm): See [here](#vLLM) ### vLLM We recommend using Pixtral-Large-Instruct-2411 with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines with Pixtral-Large-Instruct-2411. **_Installation_** Make sure you install [`vLLM >= v0.6.4.post1`](https://github.com/vllm-project/vllm/releases/tag/v0.6.4.post1): ``` pip install --upgrade vllm ``` Also make sure you have [`mistral_common >= 1.5.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.0) installed: ``` pip install --upgrade mistral_common ``` 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/sha256-55a88146a4da0b6e193431b5b1d3492dfd7bebdc16919df4d031273e85a6157c?context=explore). #### Server (Image) We recommend to use Pixtral-Large-Instruct-2411 in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Pixtral-Large-Instruct-2411 --config-format mistral --load-format mistral --tokenizer_mode mistral --limit_mm_per_prompt 'image=10' --tensor-parallel-size 8 ``` 2. And ping the client: ```py import requests import json from huggingface_hub import hf_hub_download from datetime import datetime, timedelta url = "http://:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Pixtral-Large-Instruct-2411" 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) SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "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.", }, {"type": "image_url", "image_url": {"url": image_url}}, ], }, ] data = {"model": model, "messages": messages} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) # Determining which country has the "best" food can be subjective and depends on personal preferences. However, based on popular culinary reputations, here are some countries known for their cuisine: #1. **Italy** (Brown) - Known for its pasta, pizza, and diverse regional dishes. # - City: Milan #2. **France** (Dark Brown) - Renowned for its fine dining, pastries, and wine. # - City: Lyon #3. **Spain** (Yellow) - Famous for tapas, paella, and a variety of seafood dishes. # - City: Barcelona #4. **Greece** (Yellow) - Known for its Mediterranean cuisine, including moussaka, souvlaki, and fresh seafood. # - City: Thessaloniki #These rankings are based on general culinary reputations and can vary widely depending on individual tastes. ``` #### Server (Text-only) You can also ping the client with a text-only example. The following example shows how the system prompt can be used to make sure the model always knows the current date. ```py import requests import json from huggingface_hub import hf_hub_download from datetime import datetime, timedelta url = "http://:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Pixtral-Large-Instruct-2411" 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) SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": "Without browsing the web, how many days ago was Mistral founded?" }, ] data = {"model": model, "messages": messages} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) # Mistral AI was founded in April 2023. Since the current date is November 18, 2024, we can calculate the number of days between April 2023 and November 18, 2024. #First, calculate the days from April 2023 to the end of 2023: #- April: 27 days (30 - 3) #- May: 31 days #- June: 30 days #- July: 31 days #- August: 31 days #- September: 30 days #- October: 31 days #- November: 30 days #- December: 31 days #Total days from April 2023 to December 31, 2023: 27 + 31 + 30 + 31 + 31 + 30 + 31 + 30 + 31 = 272 days #Next, calculate the days from January 1, 2024, to November 18, 2024: #- January: 31 days #- February: 29 days (2024 is a leap year) #- March: 31 days #- April: 30 days #- May: 31 days #- June: 30 days #- July: 31 days #- August: 31 days #- September: 30 days #- October: 31 days #- November: 18 days #Total days from January 1, 2024, to November 18, 2024: 31 + 29 + 31 + 30 + 31 + 30 + 31 + 31 + 30 + 31 + 18 = 323 days #Adding the two periods together: #272 days (from April 2023 to December 2023) + 323 days (from January 2024 to November 18, 2024) = 595 days #Therefore, Mistral AI was founded 595 days ago from November 18, 2024. ``` #### Offline Example ```py from vllm import LLM from vllm.sampling_params import SamplingParams from huggingface_hub import hf_hub_download from datetime import datetime, timedelta model_name = "mistralai/Pixtral-Large-Instruct-2411" 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) SYSTEM_PROMPT = load_system_prompt(model_name, "SYSTEM_PROMPT.txt") image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "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.", }, {"type": "image_url", "image_url": {"url": image_url}}, ], }, ] sampling_params = SamplingParams(max_tokens=512) # note that running this model on GPU requires over 300 GB of GPU RAM llm = LLM(model=model_name, config_format="mistral", load_format="mistral", tokenizer_mode="mistral", tensor_parallel_size=8, limit_mm_per_prompt={"image": 4}) outputs = llm.chat(messages, sampling_params=sampling_params) print(outputs[0].outputs[0].text) ``` ## Evaluation The model was evaluated using [mistral-evals](https://github.com/neuralmagic/mistral-evals) for vision-related tasks and using [lm_evaluation_harness](https://github.com/neuralmagic/lm-evaluation-harness) for select text-based benchmarks. The evaluations were conducted using the following commands:
Evaluation Commands ### Vision Tasks - vqav2 - docvqa - mathvista - mmmu - chartqa ``` vllm serve neuralmagic/pixtral-12b-quantized.w8a8 --tensor_parallel_size 1 --max_model_len 25000 --trust_remote_code --max_num_seqs 8 --gpu_memory_utilization 0.9 --dtype float16 --limit_mm_per_prompt image=7 python -m eval.run eval_vllm \ --model_name neuralmagic/pixtral-12b-quantized.w8a8 \ --url http://0.0.0.0:8000 \ --output_dir ~/tmp \ --eval_name ``` ### Text-based Tasks #### MMLU ``` lm_eval \ --model vllm \ --model_args pretrained="",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \ --tasks mmlu \ --num_fewshot 5 \ --batch_size auto \ --output_path output_dir ``` #### MGSM ``` lm_eval \ --model vllm \ --model_args pretrained="",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=,gpu_memory_utilization=0.9 \ --tasks mgsm_cot_native \ --apply_chat_template \ --num_fewshot 0 \ --batch_size auto \ --output_path output_dir ```
### Accuracy
Category Metric neuralmagic/Pixtral-Large-Instruct-2411-hf neuralmagic/Pixtral-Large-Instruct-2411-hf-FP8-dynamic Recovery (%)
Vision MMMU (val, CoT)
explicit_prompt_relaxed_correctness		 63.56 63.44 99.81%
VQAv2 (val)
vqa_match		 79.03 79.06 100.04%
DocVQA (val)
anls		 89.55 89.63 100.09%
ChartQA (test, CoT)
anywhere_in_answer_relaxed_correctness		 82.24 82.80 100.68%
Mathvista (testmini, CoT)
explicit_prompt_relaxed_correctness		 67.3 66.50 98.81%
Average Score 76.34 76.29 99.93%
Text MGSM (CoT) 76.05 75.58 99.38%
MMLU (5-shot) 82.8 82.74 99.93%
## Inference Performance This model achieves up to 1.9x speedup in single-stream deployment and up to 1.78x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario. The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
Benchmarking Command ``` guidellm --model neuralmagic/Pixtral-Large-Instruct-2411-hf-FP8-dynamic --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=,generated_tokens=,images=,width=,height= --max seconds 120 --backend aiohttp_server ```
### Single-stream performance (measured with vLLM version 0.7.2)
Document Visual Question Answering
1680W x 2240H
64/128		 Visual Reasoning
640W x 480H
128/128		 Image Captioning
480W x 360H
0/128
Hardware Number of GPUs Model Average Cost Reduction Latency (s) Queries Per Dollar Latency (s) Queries Per Dollar Latency (s) Queries Per Dollar
A100 4 neuralmagic/Pixtral-Large-Instruct-2411-hf 7.5 67 6.5 77 6.4 79
2 neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 1.86 8.1 124 7.1 142 6.8 148
2 neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 2.52 6.9 147 5.1 199 4.5 221
H100 4 neuralmagic/Pixtral-Large-Instruct-2411-hf 4.4 67 3.9 74 3.7 79
2 neuralmagic/Pixtral-Large-Instruct-2411-hf-FP8-Dynamic 1.82 4.7 120 4.1 137 3.9 145
2 neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 1.87 4.7 120 3.9 144 3.8 149
**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens **QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025). ### Multi-stream asynchronous performance (measured with vLLM version 0.7.2) <
Document Visual Question Answering
1680W x 2240H
64/128		 Visual Reasoning
640W x 480H
128/128		 Image Captioning
480W x 360H
0/128
Hardware Model Average Cost Reduction Maximum throughput (QPS) Queries Per Dollar Maximum throughput (QPS) Queries Per Dollar Maximum throughput (QPS) Queries Per Dollar
A100x4 neuralmagic/Pixtral-Large-Instruct-2411-hf 0.4 222 0.7 341 0.8 399
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 1.70 0.8 383 1.1 571 1.3 674
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 1.48 0.5 276 1.0 505 1.4 680
H100x4 neuralmagic/Pixtral-Large-Instruct-2411-hf 1.0 284 1.6 465 1.8 511
neuralmagic/Pixtral-Large-Instruct-2411-hf-FP8-Dynamic 1.61 1.7 467 2.6 726 3.2 908
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 1.33 1.4 393 2.2 726 2.7 764
**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens **QPS: Queries per second. **QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025). ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Diogo Costa, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall### Multi-stream asynchronous performance (measured with vLLM version 0.7.2) <
Document Visual Question Answering
1680W x 2240H
64/128		 Visual Reasoning
640W x 480H
128/128		 Image Captioning
480W x 360H
0/128
Hardware Model Average Cost Reduction Maximum throughput (QPS) Queries Per Dollar Maximum throughput (QPS) Queries Per Dollar Maximum throughput (QPS) Queries Per Dollar
A100x4 neuralmagic/Pixtral-Large-Instruct-2411-hf 0.4 222 0.7 341 0.8 399
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 1.70 0.8 766 1.1 1142 1.3 1348
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 1.48 0.5 552 1.0 1010 1.4 1360
H100x4 neuralmagic/Pixtral-Large-Instruct-2411-hf 1.0 284 1.6 465 1.8 511
neuralmagic/Pixtral-Large-Instruct-2411-hf-FP8-Dynamic 1.61 1.7 905 2.6 1406 3.2 1759
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 1.33 1.4 761 2.2 1228 2.7 1480
**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens **QPS: Queries per second. **QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).