Instructions to use quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA")

Transformers

How to use quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA

SGLang

How to use quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA with Docker Model Runner:
```
docker model run hf.co/quin210/Qwen3-VL-8B-KD-MMMU-Pro-LoRA
```

Qwen3-VL-8B-KD-MMMU-Pro-LoRA / adapter_model.safetensors

quin210

KD checkpoint step=27500, ep=0, loss=0.1513

3a91cdb verified 6 months ago

Download

History Blame Contribute Delete

175 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: b68c3c7b7d43628bd99c2d355d0f2cde2b36afaebcf433f9da97664f94fd1c6d
Size of remote file:: 175 MB
SHA256:: 7d71e544d7e62dd04a5b42be5d6025e809dd418be518be8e64d9062d099fb131

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.