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casyssong
/
RefReward-SR

Image-Text-to-Text
Transformers
Safetensors
reward-model
super-resolution
qwen3-vl
grpo
academic-research
Model card Files Files and versions
xet
Community

Instructions to use casyssong/RefReward-SR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use casyssong/RefReward-SR with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="casyssong/RefReward-SR")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("casyssong/RefReward-SR", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use casyssong/RefReward-SR with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "casyssong/RefReward-SR"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "casyssong/RefReward-SR",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/casyssong/RefReward-SR
  • SGLang

    How to use casyssong/RefReward-SR 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 "casyssong/RefReward-SR" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "casyssong/RefReward-SR",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    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 "casyssong/RefReward-SR" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "casyssong/RefReward-SR",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use casyssong/RefReward-SR with Docker Model Runner:

    docker model run hf.co/casyssong/RefReward-SR
RefReward-SR / checkpoint-1200
17.6 GB
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  • 1 contributor
History: 1 commit
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casyssong
Upload checkpoint-1200
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  • added_tokens.json
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  • chat_template.jinja
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  • checkpoint-1200_stats.csv
    39.1 kB
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  • config.json
    1.59 kB
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  • generation_config.json
    199 Bytes
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  • merges.txt
    1.67 MB
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  • model-00001-of-00004.safetensors
    5 GB
    xet
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  • model-00002-of-00004.safetensors
    4.92 GB
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  • model-00003-of-00004.safetensors
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  • model-00004-of-00004.safetensors
    2.7 GB
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  • model.safetensors.index.json
    67.8 kB
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  • preprocessor_config.json
    782 Bytes
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  • special_tokens_map.json
    613 Bytes
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  • tokenizer.json
    11.4 MB
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  • tokenizer_config.json
    5.45 kB
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  • trainer_state.json
    528 kB
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  • training_args.bin
    8.79 kB
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  • video_preprocessor_config.json
    817 Bytes
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  • vocab.json
    2.78 MB
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