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Model: RedHatAI/Qwen2.5-VL-3B-Instruct-quantized.w8a8 Source: Original Platform
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README.md
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---
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tags:
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- vllm
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- vision
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- w8a8
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license: apache-2.0
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license_link: >-
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https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
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language:
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- en
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base_model: Qwen/Qwen2.5-VL-3B-Instruct
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library_name: transformers
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---
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# Qwen2.5-VL-3B-Instruct-quantized-w8a8
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## Model Overview
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- **Model Architecture:** Qwen/Qwen2.5-VL-3B-Instruct
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- **Input:** Vision-Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Weight quantization:** INT8
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- **Activation quantization:** INT8
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- **Release Date:** 2/24/2025
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- **Version:** 1.0
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- **Model Developers:** Neural Magic
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Quantized version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
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### Model Optimizations
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This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) to INT8 data type, ready for inference with vLLM >= 0.5.2.
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## Deployment
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### Use with vLLM
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm.assets.image import ImageAsset
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from vllm import LLM, SamplingParams
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# prepare model
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llm = LLM(
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model="neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8",
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trust_remote_code=True,
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max_model_len=4096,
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max_num_seqs=2,
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)
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# prepare inputs
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question = "What is the content of this image?"
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inputs = {
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"prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
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"multi_modal_data": {
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"image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
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},
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}
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# generate response
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print("========== SAMPLE GENERATION ==============")
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outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
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print(f"PROMPT : {outputs[0].prompt}")
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print(f"RESPONSE: {outputs[0].outputs[0].text}")
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print("==========================================")
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```
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vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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## Creation
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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.
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<details>
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<summary>Model Creation Code</summary>
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```python
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import base64
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from io import BytesIO
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import torch
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from datasets import load_dataset
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from qwen_vl_utils import process_vision_info
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from transformers import AutoProcessor
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from llmcompressor.modifiers.quantization import GPTQModifier
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from llmcompressor.transformers import oneshot
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from llmcompressor.transformers.tracing import (
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TraceableQwen2_5_VLForConditionalGeneration,
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)
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# Load model.
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model_id = args["model_id"]
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model = TraceableQwen2_5_VLForConditionalGeneration.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype="auto",
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)
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processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
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# Oneshot arguments
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DATASET_ID = "lmms-lab/flickr30k"
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DATASET_SPLIT = {"calibration": "test[:512]"}
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NUM_CALIBRATION_SAMPLES = 512
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MAX_SEQUENCE_LENGTH = 2048
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# Load dataset and preprocess.
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ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
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ds = ds.shuffle(seed=42)
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dampening_frac=args["dampening_frac"]
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save_name = f"{model_id.split('/')[1]}-W8A8-samples{NUM_CALIBRATION_SAMPLES}-df{dampening_frac}"
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save_path = os.path.join(args["save_dir"], save_name)
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print("Save Path will be:", save_path)
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# Apply chat template and tokenize inputs.
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def preprocess_and_tokenize(example):
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# preprocess
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buffered = BytesIO()
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example["image"].save(buffered, format="PNG")
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encoded_image = base64.b64encode(buffered.getvalue())
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encoded_image_text = encoded_image.decode("utf-8")
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base64_qwen = f"data:image;base64,{encoded_image_text}"
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": base64_qwen},
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{"type": "text", "text": "What does the image show?"},
|
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],
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}
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]
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text = processor.apply_chat_template(
|
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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# tokenize
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return processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=False,
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max_length=MAX_SEQUENCE_LENGTH,
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truncation=True,
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)
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ds = ds.map(preprocess_and_tokenize, remove_columns=ds["calibration"].column_names)
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# Define a oneshot data collator for multimodal inputs.
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def data_collator(batch):
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assert len(batch) == 1
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return {key: torch.tensor(value) for key, value in batch[0].items()}
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# Recipe
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recipe = [
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GPTQModifier(
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targets="Linear",
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scheme="W8A8",
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sequential_targets=["Qwen2_5_VLDecoderLayer"],
|
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ignore=["lm_head", "re:visual.*"],
|
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),
|
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]
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SAVE_DIR==f"{model_id.split('/')[1]}-quantized.w8a8"
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# Perform oneshot
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oneshot(
|
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model=model,
|
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tokenizer=model_id,
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dataset=ds,
|
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recipe=recipe,
|
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max_seq_length=MAX_SEQUENCE_LENGTH,
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num_calibration_samples=NUM_CALIBRATION_SAMPLES,
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trust_remote_code_model=True,
|
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data_collator=data_collator,
|
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output_dir=SAVE_DIR
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)
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```
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</details>
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## Evaluation
|
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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:
|
||||
|
||||
<details>
|
||||
<summary>Evaluation Commands</summary>
|
||||
|
||||
### Vision Tasks
|
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- vqav2
|
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- 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
|
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|
||||
python -m eval.run eval_vllm \
|
||||
--model_name neuralmagic/pixtral-12b-quantized.w8a8 \
|
||||
--url http://0.0.0.0:8000 \
|
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--output_dir ~/tmp \
|
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--eval_name <vision_task_name>
|
||||
```
|
||||
|
||||
### Text-based Tasks
|
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#### MMLU
|
||||
|
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```
|
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lm_eval \
|
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--model vllm \
|
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--model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
|
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--tasks mmlu \
|
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--num_fewshot 5 \
|
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--batch_size auto \
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--output_path output_dir
|
||||
|
||||
```
|
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|
||||
#### MGSM
|
||||
|
||||
```
|
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lm_eval \
|
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--model vllm \
|
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--model_args pretrained="<model_name>",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=<n>,gpu_memory_utilization=0.9 \
|
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--tasks mgsm_cot_native \
|
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--apply_chat_template \
|
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--num_fewshot 0 \
|
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--batch_size auto \
|
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--output_path output_dir
|
||||
|
||||
```
|
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</details>
|
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### Accuracy
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<table>
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<thead>
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<tr>
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<th>Category</th>
|
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<th>Metric</th>
|
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<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
|
||||
<th>Recovery (%)</th>
|
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</tr>
|
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</thead>
|
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<tbody>
|
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<tr>
|
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<td rowspan="6"><b>Vision</b></td>
|
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<td>MMMU (val, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
|
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<td>44.56</td>
|
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<td>45.67</td>
|
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<td>102.49%</td>
|
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</tr>
|
||||
<tr>
|
||||
<td>VQAv2 (val)<br><i>vqa_match</i></td>
|
||||
<td>75.94</td>
|
||||
<td>75.55</td>
|
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<td>99.49%</td>
|
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</tr>
|
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<tr>
|
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<td>DocVQA (val)<br><i>anls</i></td>
|
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<td>92.53</td>
|
||||
<td>92.32</td>
|
||||
<td>99.77%</td>
|
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</tr>
|
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<tr>
|
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<td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td>
|
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<td>81.20</td>
|
||||
<td>78.80</td>
|
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<td>97.04%</td>
|
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</tr>
|
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<tr>
|
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<td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
|
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<td>54.15</td>
|
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<td>53.85</td>
|
||||
<td>99.45%</td>
|
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</tr>
|
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<tr>
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<td><b>Average Score</b></td>
|
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<td><b>69.28</b></td>
|
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<td><b>69.24</b></td>
|
||||
<td><b>99.94%</b></td>
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</tr>
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||||
<tr>
|
||||
<td rowspan="2"><b>Text</b></td>
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||||
<td>MGSM (CoT)</td>
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<td>43.69</td>
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||||
<td>41.98</td>
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||||
<td>96.09%</td>
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</tr>
|
||||
<tr>
|
||||
<td>MMLU (5-shot)</td>
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||||
<td>65.32</td>
|
||||
<td>64.83</td>
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<td>99.25%</td>
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</tr>
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||||
</tbody>
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</table>
|
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|
||||
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## Inference Performance
|
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|
||||
|
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This model achieves up to 1.33x speedup in single-stream deployment and up to 1.37x 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).
|
||||
|
||||
<details>
|
||||
<summary>Benchmarking Command</summary>
|
||||
```
|
||||
guidellm --model neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8 --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>,images=<num_images>,width=<image_width>,height=<image_height> --max seconds 120 --backend aiohttp_server
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### Single-stream performance (measured with vLLM version 0.7.2)
|
||||
|
||||
<table border="1" class="dataframe">
|
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<thead>
|
||||
<tr>
|
||||
<th></th>
|
||||
<th></th>
|
||||
<th></th>
|
||||
<th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
|
||||
<th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
|
||||
<th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>Hardware</th>
|
||||
<th>Model</th>
|
||||
<th>Average Cost Reduction</th>
|
||||
<th>Latency (s)</th>
|
||||
<th>Queries Per Dollar</th>
|
||||
<th>Latency (s)th>
|
||||
<th>Queries Per Dollar</th>
|
||||
<th>Latency (s)</th>
|
||||
<th>Queries Per Dollar</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody style="text-align: center">
|
||||
<tr>
|
||||
<th rowspan="3" valign="top">A6000x1</th>
|
||||
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
|
||||
<td></td>
|
||||
<td>3.1</td>
|
||||
<td>1454</td>
|
||||
<td>1.8</td>
|
||||
<td>2546</td>
|
||||
<td>1.7</td>
|
||||
<td>2610</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
|
||||
<td>1.27</td>
|
||||
<td>2.6</td>
|
||||
<td>1708</td>
|
||||
<td>1.3</td>
|
||||
<td>3340</td>
|
||||
<td>1.3</td>
|
||||
<td>3459</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
|
||||
<td>1.57</td>
|
||||
<td>2.4</td>
|
||||
<td>1886</td>
|
||||
<td>1.0</td>
|
||||
<td>4409</td>
|
||||
<td>1.0</td>
|
||||
<td>4409</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th rowspan="3" valign="top">A100x1</th>
|
||||
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
|
||||
<td></td>
|
||||
<td>2.2</td>
|
||||
<td>920</td>
|
||||
<td>1.3</td>
|
||||
<td>1603</td>
|
||||
<td>1.2</td>
|
||||
<td>1636</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
|
||||
<td>1.09</td>
|
||||
<td>2.1</td>
|
||||
<td>975</td>
|
||||
<td>1.2</td>
|
||||
<td>1743</td>
|
||||
<td>1.1</td>
|
||||
<td>1814</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
|
||||
<td>1.20</td>
|
||||
<td>2.0</td>
|
||||
<td>1011</td>
|
||||
<td>1.0</td>
|
||||
<td>2015</td>
|
||||
<td>1.0</td>
|
||||
<td>2012</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th rowspan="3" valign="top">H100x1</th>
|
||||
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
|
||||
<td>1.5</td>
|
||||
<td>740</td>
|
||||
<td>0.9</td>
|
||||
<td>1221</td>
|
||||
<td>0.9</td>
|
||||
<td>1276</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
|
||||
<td>1.06</td>
|
||||
<td>1.4</td>
|
||||
<td>768</td>
|
||||
<td>0.9</td>
|
||||
<td>1276</td>
|
||||
<td>0.8</td>
|
||||
<td>1399</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
|
||||
<td>1.24</td>
|
||||
<td>0.9</td>
|
||||
<td>1219</td>
|
||||
<td>0.9</td>
|
||||
<td>1270</td>
|
||||
<td>0.8</td>
|
||||
<td>1304</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
**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)
|
||||
|
||||
<table border="1" class="dataframe">
|
||||
<thead>
|
||||
<tr>
|
||||
<th></th>
|
||||
<th></th>
|
||||
<th></th>
|
||||
<th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
|
||||
<th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
|
||||
<th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>Hardware</th>
|
||||
<th>Model</th>
|
||||
<th>Average Cost Reduction</th>
|
||||
<th>Maximum throughput (QPS)</th>
|
||||
<th>Queries Per Dollar</th>
|
||||
<th>Maximum throughput (QPS)</th>
|
||||
<th>Queries Per Dollar</th>
|
||||
<th>Maximum throughput (QPS)</th>
|
||||
<th>Queries Per Dollar</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody style="text-align: center">
|
||||
<tr>
|
||||
<th rowspan="3" valign="top">A6000x1</th>
|
||||
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
|
||||
<td></td>
|
||||
<td>0.5</td>
|
||||
<td>2405</td>
|
||||
<td>2.6</td>
|
||||
<td>11889</td>
|
||||
<td>2.9</td>
|
||||
<td>12909</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
|
||||
<td>1.26</td>
|
||||
<td>0.6</td>
|
||||
<td>2725</td>
|
||||
<td>3.4</td>
|
||||
<td>15162</td>
|
||||
<td>3.9</td>
|
||||
<td>17673</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
|
||||
<td>1.39</td>
|
||||
<td>0.6</td>
|
||||
<td>2548</td>
|
||||
<td>3.9</td>
|
||||
<td>17437</td>
|
||||
<td>4.7</td>
|
||||
<td>21223</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th rowspan="3" valign="top">A100x1</th>
|
||||
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
|
||||
<td></td>
|
||||
<td>0.8</td>
|
||||
<td>1663</td>
|
||||
<td>3.9</td>
|
||||
<td>7899</td>
|
||||
<td>4.4</td>
|
||||
<td>8924</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
|
||||
<td>1.06</td>
|
||||
<td>0.9</td>
|
||||
<td>1734</td>
|
||||
<td>4.2</td>
|
||||
<td>8488</td>
|
||||
<td>4.7</td>
|
||||
<td>9548</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
|
||||
<td>1.10</td>
|
||||
<td>0.9</td>
|
||||
<td>1775</td>
|
||||
<td>4.2</td>
|
||||
<td>8540</td>
|
||||
<td>5.1</td>
|
||||
<td>10318</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th rowspan="3" valign="top">H100x1</th>
|
||||
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
|
||||
<td></td>
|
||||
<td>1.1</td>
|
||||
<td>1188</td>
|
||||
<td>4.3</td>
|
||||
<td>4656</td>
|
||||
<td>4.3</td>
|
||||
<td>4676</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
|
||||
<td>1.15</td>
|
||||
<td>1.4</td>
|
||||
<td>1570</td>
|
||||
<td>4.3</td>
|
||||
<td>4676</td>
|
||||
<td>4.8</td>
|
||||
<td>5220</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
|
||||
<td>1.96</td>
|
||||
<td>4.2</td>
|
||||
<td>4598</td>
|
||||
<td>4.1</td>
|
||||
<td>4505</td>
|
||||
<td>4.4</td>
|
||||
<td>4838</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
**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).
|
||||
3
chat_template.json
Normal file
3
chat_template.json
Normal file
@@ -0,0 +1,3 @@
|
||||
{
|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
|
||||
}
|
||||
265
config.json
Normal file
265
config.json
Normal file
@@ -0,0 +1,265 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"vision_start_token_id": 151652,
|
||||
"vision_end_token_id": 151653,
|
||||
"vision_token_id": 151654,
|
||||
"image_token_id": 151655,
|
||||
"video_token_id": 151656,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 32768,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.41.2",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"quantization_config": {
|
||||
"config_groups": {
|
||||
"group_0": {
|
||||
"input_activations": {
|
||||
"actorder": null,
|
||||
"block_structure": null,
|
||||
"dynamic": true,
|
||||
"group_size": null,
|
||||
"num_bits": 8,
|
||||
"observer": null,
|
||||
"observer_kwargs": {},
|
||||
"strategy": "token",
|
||||
"symmetric": true,
|
||||
"type": "int"
|
||||
},
|
||||
"output_activations": null,
|
||||
"targets": [
|
||||
"Linear"
|
||||
],
|
||||
"weights": {
|
||||
"actorder": null,
|
||||
"block_structure": null,
|
||||
"dynamic": false,
|
||||
"group_size": null,
|
||||
"num_bits": 8,
|
||||
"observer": "minmax",
|
||||
"observer_kwargs": {},
|
||||
"strategy": "channel",
|
||||
"symmetric": true,
|
||||
"type": "int"
|
||||
}
|
||||
}
|
||||
},
|
||||
"format": "int-quantized",
|
||||
"global_compression_ratio": 1.2350377879852423,
|
||||
"ignore": [
|
||||
"visual.blocks.0.attn.qkv",
|
||||
"visual.blocks.0.attn.proj",
|
||||
"visual.blocks.0.mlp.gate_proj",
|
||||
"visual.blocks.0.mlp.up_proj",
|
||||
"visual.blocks.0.mlp.down_proj",
|
||||
"visual.blocks.1.attn.qkv",
|
||||
"visual.blocks.1.attn.proj",
|
||||
"visual.blocks.1.mlp.gate_proj",
|
||||
"visual.blocks.1.mlp.up_proj",
|
||||
"visual.blocks.1.mlp.down_proj",
|
||||
"visual.blocks.2.attn.qkv",
|
||||
"visual.blocks.2.attn.proj",
|
||||
"visual.blocks.2.mlp.gate_proj",
|
||||
"visual.blocks.2.mlp.up_proj",
|
||||
"visual.blocks.2.mlp.down_proj",
|
||||
"visual.blocks.3.attn.qkv",
|
||||
"visual.blocks.3.attn.proj",
|
||||
"visual.blocks.3.mlp.gate_proj",
|
||||
"visual.blocks.3.mlp.up_proj",
|
||||
"visual.blocks.3.mlp.down_proj",
|
||||
"visual.blocks.4.attn.qkv",
|
||||
"visual.blocks.4.attn.proj",
|
||||
"visual.blocks.4.mlp.gate_proj",
|
||||
"visual.blocks.4.mlp.up_proj",
|
||||
"visual.blocks.4.mlp.down_proj",
|
||||
"visual.blocks.5.attn.qkv",
|
||||
"visual.blocks.5.attn.proj",
|
||||
"visual.blocks.5.mlp.gate_proj",
|
||||
"visual.blocks.5.mlp.up_proj",
|
||||
"visual.blocks.5.mlp.down_proj",
|
||||
"visual.blocks.6.attn.qkv",
|
||||
"visual.blocks.6.attn.proj",
|
||||
"visual.blocks.6.mlp.gate_proj",
|
||||
"visual.blocks.6.mlp.up_proj",
|
||||
"visual.blocks.6.mlp.down_proj",
|
||||
"visual.blocks.7.attn.qkv",
|
||||
"visual.blocks.7.attn.proj",
|
||||
"visual.blocks.7.mlp.gate_proj",
|
||||
"visual.blocks.7.mlp.up_proj",
|
||||
"visual.blocks.7.mlp.down_proj",
|
||||
"visual.blocks.8.attn.qkv",
|
||||
"visual.blocks.8.attn.proj",
|
||||
"visual.blocks.8.mlp.gate_proj",
|
||||
"visual.blocks.8.mlp.up_proj",
|
||||
"visual.blocks.8.mlp.down_proj",
|
||||
"visual.blocks.9.attn.qkv",
|
||||
"visual.blocks.9.attn.proj",
|
||||
"visual.blocks.9.mlp.gate_proj",
|
||||
"visual.blocks.9.mlp.up_proj",
|
||||
"visual.blocks.9.mlp.down_proj",
|
||||
"visual.blocks.10.attn.qkv",
|
||||
"visual.blocks.10.attn.proj",
|
||||
"visual.blocks.10.mlp.gate_proj",
|
||||
"visual.blocks.10.mlp.up_proj",
|
||||
"visual.blocks.10.mlp.down_proj",
|
||||
"visual.blocks.11.attn.qkv",
|
||||
"visual.blocks.11.attn.proj",
|
||||
"visual.blocks.11.mlp.gate_proj",
|
||||
"visual.blocks.11.mlp.up_proj",
|
||||
"visual.blocks.11.mlp.down_proj",
|
||||
"visual.blocks.12.attn.qkv",
|
||||
"visual.blocks.12.attn.proj",
|
||||
"visual.blocks.12.mlp.gate_proj",
|
||||
"visual.blocks.12.mlp.up_proj",
|
||||
"visual.blocks.12.mlp.down_proj",
|
||||
"visual.blocks.13.attn.qkv",
|
||||
"visual.blocks.13.attn.proj",
|
||||
"visual.blocks.13.mlp.gate_proj",
|
||||
"visual.blocks.13.mlp.up_proj",
|
||||
"visual.blocks.13.mlp.down_proj",
|
||||
"visual.blocks.14.attn.qkv",
|
||||
"visual.blocks.14.attn.proj",
|
||||
"visual.blocks.14.mlp.gate_proj",
|
||||
"visual.blocks.14.mlp.up_proj",
|
||||
"visual.blocks.14.mlp.down_proj",
|
||||
"visual.blocks.15.attn.qkv",
|
||||
"visual.blocks.15.attn.proj",
|
||||
"visual.blocks.15.mlp.gate_proj",
|
||||
"visual.blocks.15.mlp.up_proj",
|
||||
"visual.blocks.15.mlp.down_proj",
|
||||
"visual.blocks.16.attn.qkv",
|
||||
"visual.blocks.16.attn.proj",
|
||||
"visual.blocks.16.mlp.gate_proj",
|
||||
"visual.blocks.16.mlp.up_proj",
|
||||
"visual.blocks.16.mlp.down_proj",
|
||||
"visual.blocks.17.attn.qkv",
|
||||
"visual.blocks.17.attn.proj",
|
||||
"visual.blocks.17.mlp.gate_proj",
|
||||
"visual.blocks.17.mlp.up_proj",
|
||||
"visual.blocks.17.mlp.down_proj",
|
||||
"visual.blocks.18.attn.qkv",
|
||||
"visual.blocks.18.attn.proj",
|
||||
"visual.blocks.18.mlp.gate_proj",
|
||||
"visual.blocks.18.mlp.up_proj",
|
||||
"visual.blocks.18.mlp.down_proj",
|
||||
"visual.blocks.19.attn.qkv",
|
||||
"visual.blocks.19.attn.proj",
|
||||
"visual.blocks.19.mlp.gate_proj",
|
||||
"visual.blocks.19.mlp.up_proj",
|
||||
"visual.blocks.19.mlp.down_proj",
|
||||
"visual.blocks.20.attn.qkv",
|
||||
"visual.blocks.20.attn.proj",
|
||||
"visual.blocks.20.mlp.gate_proj",
|
||||
"visual.blocks.20.mlp.up_proj",
|
||||
"visual.blocks.20.mlp.down_proj",
|
||||
"visual.blocks.21.attn.qkv",
|
||||
"visual.blocks.21.attn.proj",
|
||||
"visual.blocks.21.mlp.gate_proj",
|
||||
"visual.blocks.21.mlp.up_proj",
|
||||
"visual.blocks.21.mlp.down_proj",
|
||||
"visual.blocks.22.attn.qkv",
|
||||
"visual.blocks.22.attn.proj",
|
||||
"visual.blocks.22.mlp.gate_proj",
|
||||
"visual.blocks.22.mlp.up_proj",
|
||||
"visual.blocks.22.mlp.down_proj",
|
||||
"visual.blocks.23.attn.qkv",
|
||||
"visual.blocks.23.attn.proj",
|
||||
"visual.blocks.23.mlp.gate_proj",
|
||||
"visual.blocks.23.mlp.up_proj",
|
||||
"visual.blocks.23.mlp.down_proj",
|
||||
"visual.blocks.24.attn.qkv",
|
||||
"visual.blocks.24.attn.proj",
|
||||
"visual.blocks.24.mlp.gate_proj",
|
||||
"visual.blocks.24.mlp.up_proj",
|
||||
"visual.blocks.24.mlp.down_proj",
|
||||
"visual.blocks.25.attn.qkv",
|
||||
"visual.blocks.25.attn.proj",
|
||||
"visual.blocks.25.mlp.gate_proj",
|
||||
"visual.blocks.25.mlp.up_proj",
|
||||
"visual.blocks.25.mlp.down_proj",
|
||||
"visual.blocks.26.attn.qkv",
|
||||
"visual.blocks.26.attn.proj",
|
||||
"visual.blocks.26.mlp.gate_proj",
|
||||
"visual.blocks.26.mlp.up_proj",
|
||||
"visual.blocks.26.mlp.down_proj",
|
||||
"visual.blocks.27.attn.qkv",
|
||||
"visual.blocks.27.attn.proj",
|
||||
"visual.blocks.27.mlp.gate_proj",
|
||||
"visual.blocks.27.mlp.up_proj",
|
||||
"visual.blocks.27.mlp.down_proj",
|
||||
"visual.blocks.28.attn.qkv",
|
||||
"visual.blocks.28.attn.proj",
|
||||
"visual.blocks.28.mlp.gate_proj",
|
||||
"visual.blocks.28.mlp.up_proj",
|
||||
"visual.blocks.28.mlp.down_proj",
|
||||
"visual.blocks.29.attn.qkv",
|
||||
"visual.blocks.29.attn.proj",
|
||||
"visual.blocks.29.mlp.gate_proj",
|
||||
"visual.blocks.29.mlp.up_proj",
|
||||
"visual.blocks.29.mlp.down_proj",
|
||||
"visual.blocks.30.attn.qkv",
|
||||
"visual.blocks.30.attn.proj",
|
||||
"visual.blocks.30.mlp.gate_proj",
|
||||
"visual.blocks.30.mlp.up_proj",
|
||||
"visual.blocks.30.mlp.down_proj",
|
||||
"visual.blocks.31.attn.qkv",
|
||||
"visual.blocks.31.attn.proj",
|
||||
"visual.blocks.31.mlp.gate_proj",
|
||||
"visual.blocks.31.mlp.up_proj",
|
||||
"visual.blocks.31.mlp.down_proj",
|
||||
"visual.merger.mlp.0",
|
||||
"visual.merger.mlp.2",
|
||||
"lm_head"
|
||||
],
|
||||
"kv_cache_scheme": null,
|
||||
"quant_method": "compressed-tensors",
|
||||
"quantization_status": "compressed"
|
||||
},
|
||||
"vision_config": {
|
||||
"depth": 32,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1280,
|
||||
"intermediate_size": 3420,
|
||||
"num_heads": 16,
|
||||
"in_chans": 3,
|
||||
"out_hidden_size": 2048,
|
||||
"patch_size": 14,
|
||||
"spatial_merge_size": 2,
|
||||
"spatial_patch_size": 14,
|
||||
"window_size": 112,
|
||||
"fullatt_block_indexes": [
|
||||
7,
|
||||
15,
|
||||
23,
|
||||
31
|
||||
],
|
||||
"tokens_per_second": 2,
|
||||
"temporal_patch_size": 2
|
||||
},
|
||||
"rope_scaling": {
|
||||
"type": "mrope",
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
]
|
||||
},
|
||||
"vocab_size": 151936
|
||||
}
|
||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"pad_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"repetition_penalty": 1.05,
|
||||
"temperature": 0.000001,
|
||||
"transformers_version": "4.49.0"
|
||||
}
|
||||
151387
merges.txt
Normal file
151387
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:91e80387362144aac1e4afcb61601c9401aad655b8bf11b528eb94815eba3c3b
|
||||
size 4736898848
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f939d272ee1b8b2b48b26ee2f8aca7b320f91d442323b5548c53efe900ec0d18
|
||||
size 622329984
|
||||
1084
model.safetensors.index.json
Normal file
1084
model.safetensors.index.json
Normal file
File diff suppressed because it is too large
Load Diff
19
preprocessor_config.json
Normal file
19
preprocessor_config.json
Normal file
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"min_pixels": 3136,
|
||||
"max_pixels": 12845056,
|
||||
"patch_size": 14,
|
||||
"temporal_patch_size": 2,
|
||||
"merge_size": 2,
|
||||
"image_mean": [
|
||||
0.48145466,
|
||||
0.4578275,
|
||||
0.40821073
|
||||
],
|
||||
"image_std": [
|
||||
0.26862954,
|
||||
0.26130258,
|
||||
0.27577711
|
||||
],
|
||||
"image_processor_type": "Qwen2VLImageProcessor",
|
||||
"processor_class": "Qwen2_5_VLProcessor"
|
||||
}
|
||||
7
recipe.yaml
Normal file
7
recipe.yaml
Normal file
@@ -0,0 +1,7 @@
|
||||
DEFAULT_stage:
|
||||
DEFAULT_modifiers:
|
||||
GPTQModifier:
|
||||
sequential_targets: [Qwen2_5_VLDecoderLayer]
|
||||
scheme: W8A8
|
||||
targets: Linear
|
||||
ignore: [lm_head, 're:visual.*']
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"add_bos_token": false
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user