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
- PEFT
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
KD checkpoint step=27500, ep=0, loss=0.1513
Browse files- README.md +12 -4
- adapter_config.json +4 -4
- adapter_model.safetensors +1 -1
- processor_config.json +2 -0
- training_state.json +1 -0
README.md
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---
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# Model Card for Model ID
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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base_model: Qwen/Qwen3-VL-8B-Instruct
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library_name: peft
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pipeline_tag: text-generation
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tags:
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- base_model:adapter:Qwen/Qwen3-VL-8B-Instruct
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- lora
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- transformers
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---
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# Model Card for Model ID
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.18.1
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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adapter_model.safetensors
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processor_config.json
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"merge_size": 2,
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"patch_size": 16,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"max_pixels": 401408,
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"merge_size": 2,
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"min_pixels": 3136,
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"patch_size": 16,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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training_state.json
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{"step": 27500, "ep": 0, "loss": 0.15126445889472961}
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