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neelsurya
/
results

Text Generation
Transformers
Safetensors
llama
trl
sft
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use neelsurya/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use neelsurya/results with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="neelsurya/results")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("neelsurya/results")
    model = AutoModelForCausalLM.from_pretrained("neelsurya/results", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use neelsurya/results with vLLM:

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

    How to use neelsurya/results 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 "neelsurya/results" \
        --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": "neelsurya/results",
    		"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 "neelsurya/results" \
            --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": "neelsurya/results",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use neelsurya/results with Docker Model Runner:

    docker model run hf.co/neelsurya/results
results
32.1 GB
Ctrl+K
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  • 1 contributor
History: 2 commits

This model has 1 file scanned as unsafe.

neelsurya's picture
neelsurya
surya-llama3-8b-SFT
5702efb verified about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    1.41 kB
    surya-llama3-8b-SFT about 2 years ago
  • config.json
    723 Bytes
    surya-llama3-8b-SFT about 2 years ago
  • generation_config.json
    177 Bytes
    surya-llama3-8b-SFT about 2 years ago
  • model-00001-of-00007.safetensors
    4.89 GB
    xet
    surya-llama3-8b-SFT about 2 years ago
  • model-00002-of-00007.safetensors
    4.83 GB
    xet
    surya-llama3-8b-SFT about 2 years ago
  • model-00003-of-00007.safetensors
    5 GB
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    surya-llama3-8b-SFT about 2 years ago
  • model-00004-of-00007.safetensors
    5 GB
    xet
    surya-llama3-8b-SFT about 2 years ago
  • model-00005-of-00007.safetensors
    4.83 GB
    xet
    surya-llama3-8b-SFT about 2 years ago
  • model-00006-of-00007.safetensors
    5 GB
    xet
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  • model-00007-of-00007.safetensors
    2.57 GB
    xet
    surya-llama3-8b-SFT about 2 years ago
  • model.safetensors.index.json
    24 kB
    surya-llama3-8b-SFT about 2 years ago
  • special_tokens_map.json
    335 Bytes
    surya-llama3-8b-SFT about 2 years ago
  • tokenizer.json
    9.09 MB
    surya-llama3-8b-SFT about 2 years ago
  • tokenizer_config.json
    50.7 kB
    surya-llama3-8b-SFT about 2 years ago
  • training_args.bin
    5.43 kB
    xet
    surya-llama3-8b-SFT about 2 years ago