--- tags: - w4a16 - int4 - vllm - vision license: apache-2.0 license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md language: - en base_model: mgoin/pixtral-12b library_name: transformers --- # pixtral-12b-quantized.w4a16 ## Model Overview - **Model Architecture:** mgoin/pixtral-12b - **Input:** Vision-Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Activation quantization:** FP16 - **Release Date:** 2/24/2025 - **Version:** 1.0 - **Model Developers:** Neural Magic Quantized version of [mgoin/pixtral-12b](https://huggingface.co/mgoin/pixtral-12b). ### Model Optimizations This model was obtained by quantizing the weights of [mgoin/pixtral-12b](https://huggingface.co/mgoin/pixtral-12b) to INT4 data type, ready for inference with vLLM >= 0.5.2. ## Deployment ### Use with vLLM This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. ```python from vllm.assets.image import ImageAsset from vllm import LLM, SamplingParams # prepare model llm = LLM( model="neuralmagic/pixtral-12b-quantized.w4a16", trust_remote_code=True, max_model_len=4096, max_num_seqs=2, ) # prepare inputs question = "What is the content of this image?" inputs = { "prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n", "multi_modal_data": { "image": ImageAsset("cherry_blossom").pil_image.convert("RGB") }, } # generate response print("========== SAMPLE GENERATION ==============") outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64)) print(f"PROMPT : {outputs[0].prompt}") print(f"RESPONSE: {outputs[0].outputs[0].text}") print("==========================================") ``` vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. ## Creation This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below as part a multimodal announcement blog.
Model Creation Code ```python import requests import torch from PIL import Image from transformers import AutoProcessor from llmcompressor.modifiers.quantization import GPTQModifier from llmcompressor.transformers import oneshot from llmcompressor.transformers.tracing import TraceableLlavaForConditionalGeneration from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy, ActivationOrdering, QuantizationScheme # Load model. model_id = "mgoin/pixtral-12b" model = TraceableLlavaForConditionalGeneration.from_pretrained( model_id, device_map="auto", torch_dtype="auto" ) processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) # Oneshot arguments DATASET_ID = "flickr30k" DATASET_SPLIT = {"calibration": "test[:512]"} NUM_CALIBRATION_SAMPLES = 512 MAX_SEQUENCE_LENGTH = 2048 dampening_frac=0.01 # Define a oneshot data collator for multimodal inputs. def data_collator(batch): assert len(batch) == 1 return { "input_ids": torch.LongTensor(batch[0]["input_ids"]), "attention_mask": torch.tensor(batch[0]["attention_mask"]), "pixel_values": torch.tensor(batch[0]["pixel_values"]), } recipe = GPTQModifier( targets="Linear", config_groups={ "config_group": QuantizationScheme( targets=["Linear"], weights=QuantizationArgs( num_bits=4, type=QuantizationType.INT, strategy=QuantizationStrategy.GROUP, group_size=128, symmetric=True, dynamic=False, actorder=ActivationOrdering.WEIGHT, ), ), }, sequential_targets=["MistralDecoderLayer"], ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"], update_size=NUM_CALIBRATION_SAMPLES, dampening_frac=dampening_frac, ) SAVE_DIR = f"{model_id.split('/')[1]}-quantized.w4a16" # Perform oneshot oneshot( model=model, tokenizer=model_id, dataset=DATASET_ID, splits=DATASET_SPLIT, recipe=recipe, max_seq_length=MAX_SEQUENCE_LENGTH, num_calibration_samples=NUM_CALIBRATION_SAMPLES, trust_remote_code_model=True, data_collator=data_collator, output_dir=SAVE_DIR ) ```
## 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.w4a16 \ --url http://0.0.0.0:8000 \ --output_dir ~/tmp --eval_name ``` ### Text-based Tasks #### MMLU ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/pixtral-12b-quantized.w4a16 ",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 \ ``` #### HumanEval ##### Generation ``` python3 codegen/generate.py \ --model neuralmagic/pixtral-12b-quantized.w4a16 \ --bs 16 \ --temperature 0.2 \ --n_samples 50 \ --root "." \ --dataset humaneval ``` ##### Sanitization ``` python3 evalplus/sanitize.py \ humaneval/neuralmagic/pixtral-12b-quantized.w4a16_vllm_temp_0.2 ``` ##### Evaluation ``` evalplus.evaluate \ --dataset humaneval \ --samples humaneval/neuralmagic/pixtral-12b-quantized.w4a16_vllm_temp_0.2-sanitized ```
### Accuracy
Category Metric mgoin/pixtral-12b neuralmagic/pixtral-12b-quantized.w4a16 Recovery (%)
Vision MMMU (val, CoT)
explicit_prompt_relaxed_correctness
 48.00 44.67 93.06%
VQAv2 (val)
vqa_match		 78.71 77.04 97.88%
DocVQA (val)
anls		 89.47 89.02 99.50%
ChartQA (test, CoT)
anywhere_in_answer_relaxed_correctness		 81.68 82.12 100.54%
Mathvista (testmini, CoT)
explicit_prompt_relaxed_correctness		 56.50 54.40 96.28%
Average Score 70.07 69.05 98.54%
Text HumanEval
pass@1		 71.40 63.80 89.37%
MMLU (5-shot) 68.40 65.56 95.86%
## Inference Performance This model achieves up to 2.47x speedup in single-stream deployment and up to 1.74x 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-12b-quantized.w4a16 --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 Model Average Cost Reduction Latency (s) Queries Per Dollar Latency (s) Queries Per Dollar Latency (s) Queries Per Dollar
A6000x1 mgoin/pixtral-12b 5.7 796 4.8 929 4.7 964
neuralmagic/pixtral-12b-quantized.w8a8 1.55 3.7 1220 3.1 1437 3.0 1511
neuralmagic/pixtral-12b-quantized.w4a16 2.16 3.2 1417 2.1 2093 1.9 2371
A100x1 mgoin/pixtral-12b 3.0 676 2.4 825 2.3 859
neuralmagic/pixtral-12b-quantized.w8a8 1.38 2.2 904 1.7 1159 1.7 1201
neuralmagic/pixtral-12b-quantized.w4a16 1.83 1.8 1096 1.3 1557 1.2 1702
H100x1 mgoin/pixtral-12b 1.8 595 1.5 732 1.4 764
neuralmagic/pixtral-12b-FP8-Dynamic 1.35 1.4 767 1.1 1008 1.0 1056
neuralmagic/pixtral-12b-quantized.w4a16 1.37 1.4 787 1.1 1018 1.0 1065
**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
A6000x1 mgoin/pixtral-12b 0.6 2632 0.9 4108 1.1 4774
neuralmagic/pixtral-12b-quantized.w8a8 1.50 0.9 3901 1.4 6160 1.6 7292
neuralmagic/pixtral-12b-quantized.w4a16 1.41 0.6 2890 1.3 5758 1.8 8312
A100x1 mgoin/pixtral-12b 1.1 2291 1.8 3670 2.1 4284
neuralmagic/pixtral-12b-quantized.w8a8 1.38 1.5 3096 2.5 5076 3.0 5965
neuralmagic/pixtral-12b-quantized.w4a16 1.40 1.4 2728 2.6 5133 3.5 6943
H100x1 BF16 2.6 2877 4.0 4372 4.7 5095
neuralmagic/pixtral-12b-FP8-Dynamic 1.33 3.4 3753 5.4 5862 6.3 6917
neuralmagic/pixtral-12b-quantized.w4a16 1.22 2.8 3115 5.0 5511 6.2 6777
**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).