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Puujeeeeeeeeeeee
/
qwen35-cpt-round5

Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
text-generation-inference
unsloth
Model card Files Files and versions
xet
Community

Instructions to use Puujeeeeeeeeeeee/qwen35-cpt-round5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Puujeeeeeeeeeeee/qwen35-cpt-round5 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="Puujeeeeeeeeeeee/qwen35-cpt-round5")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("Puujeeeeeeeeeeee/qwen35-cpt-round5")
    model = AutoModelForMultimodalLM.from_pretrained("Puujeeeeeeeeeeee/qwen35-cpt-round5", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Puujeeeeeeeeeeee/qwen35-cpt-round5 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Puujeeeeeeeeeeee/qwen35-cpt-round5"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Puujeeeeeeeeeeee/qwen35-cpt-round5",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Puujeeeeeeeeeeee/qwen35-cpt-round5
  • SGLang

    How to use Puujeeeeeeeeeeee/qwen35-cpt-round5 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 "Puujeeeeeeeeeeee/qwen35-cpt-round5" \
        --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": "Puujeeeeeeeeeeee/qwen35-cpt-round5",
    		"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 "Puujeeeeeeeeeeee/qwen35-cpt-round5" \
            --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": "Puujeeeeeeeeeeee/qwen35-cpt-round5",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Unsloth Desktop
  • Docker Model Runner

    How to use Puujeeeeeeeeeeee/qwen35-cpt-round5 with Docker Model Runner:

    docker model run hf.co/Puujeeeeeeeeeeee/qwen35-cpt-round5
qwen35-cpt-round5
18.8 GB
Ctrl+K
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  • 1 contributor
History: 8 commits
Puujeeeeeeeeeeee's picture
Puujeeeeeeeeeeee
(Trained with Unsloth)
0ef7d43 verified 22 days ago
  • .gitattributes
    1.57 kB
    (Trained with Unsloth) 22 days ago
  • README.md
    605 Bytes
    Unsloth Model Card 22 days ago
  • config.json
    3.49 kB
    (Trained with Unsloth) 22 days ago
  • generation_config.json
    163 Bytes
    (Trained with Unsloth) 22 days ago
  • model-00001-of-00004.safetensors
    4.94 GB
    xet
    (Trained with Unsloth) 22 days ago
  • model-00002-of-00004.safetensors
    4.99 GB
    xet
    (Trained with Unsloth) 22 days ago
  • model-00003-of-00004.safetensors
    4.95 GB
    xet
    (Trained with Unsloth) 22 days ago
  • model-00004-of-00004.safetensors
    3.93 GB
    xet
    (Trained with Unsloth) 22 days ago
  • model.safetensors.index.json
    69.3 kB
    (Trained with Unsloth) 22 days ago
  • processor_config.json
    1.3 kB
    (Trained with Unsloth) 22 days ago
  • tokenizer.json
    20 MB
    xet
    (Trained with Unsloth) 22 days ago
  • tokenizer_config.json
    7.16 kB
    (Trained with Unsloth) 22 days ago