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SujiKim
/
learnweak-evocua-8b-lora-r32-vscode

Text Generation
PEFT
Safetensors
Transformers
llama-factory
lora
conversational
Model card Files Files and versions
xet
Community
1

Instructions to use SujiKim/learnweak-evocua-8b-lora-r32-vscode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use SujiKim/learnweak-evocua-8b-lora-r32-vscode with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("meituan/EvoCUA-8B-20260105")
    model = PeftModel.from_pretrained(base_model, "SujiKim/learnweak-evocua-8b-lora-r32-vscode")
  • Transformers

    How to use SujiKim/learnweak-evocua-8b-lora-r32-vscode with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="SujiKim/learnweak-evocua-8b-lora-r32-vscode")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("SujiKim/learnweak-evocua-8b-lora-r32-vscode", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use SujiKim/learnweak-evocua-8b-lora-r32-vscode with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "SujiKim/learnweak-evocua-8b-lora-r32-vscode"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "SujiKim/learnweak-evocua-8b-lora-r32-vscode",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/SujiKim/learnweak-evocua-8b-lora-r32-vscode
  • SGLang

    How to use SujiKim/learnweak-evocua-8b-lora-r32-vscode 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 "SujiKim/learnweak-evocua-8b-lora-r32-vscode" \
        --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": "SujiKim/learnweak-evocua-8b-lora-r32-vscode",
    		"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 "SujiKim/learnweak-evocua-8b-lora-r32-vscode" \
            --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": "SujiKim/learnweak-evocua-8b-lora-r32-vscode",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use SujiKim/learnweak-evocua-8b-lora-r32-vscode with Docker Model Runner:

    docker model run hf.co/SujiKim/learnweak-evocua-8b-lora-r32-vscode
learnweak-evocua-8b-lora-r32-vscode
365 MB
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  • 1 contributor
History: 13 commits
SujiKim's picture
SujiKim
Upload vocab.json with huggingface_hub
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  • .gitattributes
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  • README.md
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  • adapter_config.json
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  • adapter_model.safetensors
    349 MB
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  • added_tokens.json
    707 Bytes
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  • chat_template.jinja
    5.2 kB
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  • merges.txt
    1.67 MB
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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
    5.47 kB
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  • video_preprocessor_config.json
    817 Bytes
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  • vocab.json
    2.78 MB
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