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mingyi456/
Qwen3-VL-8B-Instruct-DF11

Name: Qwen3-VL-8B-Instruct-DF11
Availability: InStock

transformers

safetensors

image-text-to-text

Qwen/Qwen3-VL-8B-Instruct

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For more information (including how to compress models yourself), check out https://huggingface.co/DFloat11 and https://github.com/LeanModels/DFloat11

Feel free to request for other models for compression as well (for either the diffusers library, ComfyUI, or any other model), although models that use architectures which are unfamiliar to me might be more difficult.

How to Use

`transformers`

python

import torch
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from dfloat11 import DFloat11Model

# default: Load the model on the available device(s)
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct", dtype=torch.bfloat16, device_map="cpu"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen3VLForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen3-VL-8B-Instruct",
#     dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

DFloat11Model.from_pretrained("mingyi456/Qwen3-VL-8B-Instruct-DF11", device = "cpu", bfloat16_model = model)
model.to("cuda")

processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-8B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Compression Details

This is the pattern_dict for compression:

python

pattern_dict={
	r"model\.visual\.blocks\.\d+": [
		"attn.qkv",
		"attn.proj",
		"mlp.linear_fc1",
		"mlp.linear_fc2"
	],
	r"model\.visual\.merger": [
		"linear_fc1",
		"linear_fc2",
	],
	r"model\.visual\.deepstack_merger_list\.\d+": [
		"linear_fc1",
		"linear_fc2",
	],
	
	r"model\.language_model\.embed_tokens": [],
	r"model\.language_model\.layers\.\d+":[
		"self_attn.q_proj",
		"self_attn.k_proj",
		"self_attn.v_proj",
		"self_attn.o_proj",
		"mlp.gate_proj",
		"mlp.up_proj",
		"mlp.down_proj"
	],
	
	r"lm_head": []
}