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jslin09
/
gemma2-2b-ner

Text Generation
Transformers
Safetensors
Chinese
gemma2
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use jslin09/gemma2-2b-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use jslin09/gemma2-2b-ner with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="jslin09/gemma2-2b-ner")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("jslin09/gemma2-2b-ner")
    model = AutoModelForCausalLM.from_pretrained("jslin09/gemma2-2b-ner")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use jslin09/gemma2-2b-ner with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "jslin09/gemma2-2b-ner"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "jslin09/gemma2-2b-ner",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/jslin09/gemma2-2b-ner
  • SGLang

    How to use jslin09/gemma2-2b-ner 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 "jslin09/gemma2-2b-ner" \
        --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": "jslin09/gemma2-2b-ner",
    		"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 "jslin09/gemma2-2b-ner" \
            --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": "jslin09/gemma2-2b-ner",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use jslin09/gemma2-2b-ner with Docker Model Runner:

    docker model run hf.co/jslin09/gemma2-2b-ner
gemma2-2b-ner
10.5 GB
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  • 1 contributor
History: 15 commits
jslin09's picture
jslin09
Update README.md
cf4c8df verified over 1 year ago
  • .gitattributes
    1.57 kB
    First Release over 1 year ago
  • README.md
    10.8 kB
    Update README.md over 1 year ago
  • config.json
    858 Bytes
    Precision 訓練到 0.98,Recall訓練到0.75 over 1 year ago
  • generation_config.json
    168 Bytes
    Precision 訓練到 0.98,Recall訓練到0.75 over 1 year ago
  • model-00001-of-00003.safetensors
    4.99 GB
    xet
    Precision 訓練到 0.98,Recall訓練到0.75 over 1 year ago
  • model-00002-of-00003.safetensors
    4.98 GB
    xet
    Precision 訓練到 0.98,Recall訓練到0.75 over 1 year ago
  • model-00003-of-00003.safetensors
    481 MB
    xet
    Precision 訓練到 0.98,Recall訓練到0.75 over 1 year ago
  • model.safetensors.index.json
    24.2 kB
    First Release over 1 year ago
  • special_tokens_map.json
    636 Bytes
    First Release over 1 year ago
  • tokenizer.json
    34.4 MB
    xet
    First Release over 1 year ago
  • tokenizer.model
    4.24 MB
    xet
    Precision 訓練到 0.98,Recall訓練到0.75 over 1 year ago
  • tokenizer_config.json
    46.4 kB
    First Release over 1 year ago