From 7739e17218ab5a177271aa99d871fbfc813007fc Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 22 Oct 2026 07:00:14 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: RedHatAI/Qwen2.5-VL-7B-Instruct-quantized.w4a16 Source: Original Platform --- .gitattributes | 36 ++ README.md | 574 +++++++++++++++++++++++++++++++ added_tokens.json | 24 ++ chat_template.json | 3 + config.json | 3 + configuration.json | 1 + generation_config.json | 14 + merges.txt | 3 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 3 + preprocessor_config.json | 29 ++ recipe.yaml | 12 + special_tokens_map.json | 31 ++ tokenizer.json | 3 + tokenizer_config.json | 3 + vocab.json | 3 + 17 files changed, 748 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 added_tokens.json create mode 100644 chat_template.json create mode 100644 config.json create mode 100644 configuration.json create mode 100644 generation_config.json create mode 100644 merges.txt create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 preprocessor_config.json create mode 100644 recipe.yaml create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 vocab.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..3c96229 --- /dev/null +++ b/README.md @@ -0,0 +1,574 @@ +--- +tags: +- vllm +- vision +- w4a16 +license: apache-2.0 +license_link: >- + https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md +language: +- en +base_model: Qwen/Qwen2.5-VL-7B-Instruct +library_name: transformers +--- + +# Qwen2.5-VL-7B-Instruct-quantized-w4a16 + +## Model Overview +- **Model Architecture:** Qwen/Qwen2.5-VL-7B-Instruct + - **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 [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). + +### Model Optimizations + +This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) to INT8 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/Qwen2.5-VL-7B-Instruct-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 base64 +from io import BytesIO +import torch +from datasets import load_dataset +from qwen_vl_utils import process_vision_info +from transformers import AutoProcessor +from llmcompressor.modifiers.quantization import GPTQModifier +from llmcompressor.transformers import oneshot +from llmcompressor.transformers.tracing import ( + TraceableQwen2_5_VLForConditionalGeneration, +) +from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy, ActivationOrdering, QuantizationScheme + +# Load model. +model_id = "Qwen/Qwen2.5-VL-7B-Instruct" + +model = TraceableQwen2_5_VLForConditionalGeneration.from_pretrained( + model_id, + device_map="auto", + torch_dtype="auto", +) +processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) + +# Oneshot arguments +DATASET_ID = "lmms-lab/flickr30k" +DATASET_SPLIT = {"calibration": "test[:512]"} +NUM_CALIBRATION_SAMPLES = 512 +MAX_SEQUENCE_LENGTH = 2048 + +# Load dataset and preprocess. +ds = load_dataset(DATASET_ID, split=DATASET_SPLIT) +ds = ds.shuffle(seed=42) +dampening_frac=0.01 + +# Apply chat template and tokenize inputs. +def preprocess_and_tokenize(example): + # preprocess + buffered = BytesIO() + example["image"].save(buffered, format="PNG") + encoded_image = base64.b64encode(buffered.getvalue()) + encoded_image_text = encoded_image.decode("utf-8") + base64_qwen = f"data:image;base64,{encoded_image_text}" + messages = [ + { + "role": "user", + "content": [ + {"type": "image", "image": base64_qwen}, + {"type": "text", "text": "What does the image show?"}, + ], + } + ] + text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + + # tokenize + return processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=False, + max_length=MAX_SEQUENCE_LENGTH, + truncation=True, + ) +ds = ds.map(preprocess_and_tokenize, remove_columns=ds["calibration"].column_names) + +# Define a oneshot data collator for multimodal inputs. +def data_collator(batch): + assert len(batch) == 1 + return {key: torch.tensor(value) for key, value in batch[0].items()} + +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=["Qwen2_5_VLDecoderLayer"], + ignore=["lm_head", "re:visual.*"], + 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=ds, + 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.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 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CategoryMetricQwen/Qwen2.5-VL-7B-InstructQwen2.5-VL-7B-Instruct-quantized.W4A16Recovery (%)
VisionMMMU (val, CoT)
explicit_prompt_relaxed_correctness		52.0051.1198.29%
VQAv2 (val)
vqa_match		75.5973.9097.76%
DocVQA (val)
anls		94.2794.1399.85%
ChartQA (test, CoT)
anywhere_in_answer_relaxed_correctness		86.4485.6499.07%
Mathvista (testmini, CoT)
explicit_prompt_relaxed_correctness		69.4767.1796.69%
Average Score75.9574.7998.47%
TextMGSM (CoT)56.3851.8992.04%
MMLU (5-shot)71.0968.6796.59%
+ +## Inference Performance + + +This model achieves up to 2.35x speedup in single-stream deployment and up to 2.02x 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/Qwen2.5-VL-7B-Instruct-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
HardwareModelAverage Cost ReductionLatency (s)Queries Per DollarLatency (s)th> + Queries Per DollarLatency (s)Queries Per Dollar
A6000x1Qwen/Qwen2.5-VL-7B-Instruct4.99123.213863.11431
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a81.503.612482.121632.02237
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a162.053.313511.432521.43321
A100x1Qwen/Qwen2.5-VL-7B-Instruct2.87071.711621.71198
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a81.242.48511.414541.31512
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a161.492.29121.117911.01950
H100x1Qwen/Qwen2.5-VL-7B-Instruct2.05571.29191.2941
neuralmagic/Qwen2.5-VL-7B-Instruct-FP8-Dynamic1.281.66980.911810.91219
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a161.281.66860.911910.91228
+ +**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
HardwareModelAverage Cost ReductionMaximum throughput (QPS)Queries Per DollarMaximum throughput (QPS)Queries Per DollarMaximum throughput (QPS)Queries Per Dollar
A6000x1Qwen/Qwen2.5-VL-7B-Instruct0.418371.568461.77638
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a81.410.522972.3101372.511472
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a161.600.418282.7122543.415477
A100x1Qwen/Qwen2.5-VL-7B-Instruct0.713472.652213.06122
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a81.270.816393.468513.97918
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a161.210.713143.059834.69206
H100x1Qwen/Qwen2.5-VL-7B-Instruct0.99693.133583.33615
neuralmagic/Qwen2.5-VL-7B-Instruct-FP8-Dynamic1.291.213313.841094.24598
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a161.281.212983.841904.24573
+ +**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). \ No newline at end of file diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..482ced4 --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,24 @@ +{ + "": 151658, + "": 151657, + "<|box_end|>": 151649, + "<|box_start|>": 151648, + "<|endoftext|>": 151643, + "<|file_sep|>": 151664, + "<|fim_middle|>": 151660, + "<|fim_pad|>": 151662, + "<|fim_prefix|>": 151659, + "<|fim_suffix|>": 151661, + "<|im_end|>": 151645, + "<|im_start|>": 151644, + "<|image_pad|>": 151655, + "<|object_ref_end|>": 151647, + "<|object_ref_start|>": 151646, + "<|quad_end|>": 151651, + "<|quad_start|>": 151650, + "<|repo_name|>": 151663, + "<|video_pad|>": 151656, + "<|vision_end|>": 151653, + "<|vision_pad|>": 151654, + "<|vision_start|>": 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