Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

spdenisov
/
kamll

Text Generation
Transformers
PyTorch
llama
text-generation-inference
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use spdenisov/kamll with Transformers:

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

    How to use spdenisov/kamll with vLLM:

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

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

    How to use spdenisov/kamll with Docker Model Runner:

    docker model run hf.co/spdenisov/kamll
kamll / checkpoint-5000
40.4 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 1 commit
spdenisov's picture
spdenisov
5k
d67b129 about 3 years ago
  • config.json
    558 Bytes
    xet
    5k about 3 years ago
  • generation_config.json
    137 Bytes
    xet
    5k about 3 years ago
  • optimizer.pt

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.BFloat16Storage"

    What is a pickle import?

    27 GB
    xet
    5k about 3 years ago
  • pytorch_model-00001-of-00002.bin

    Detected Pickle imports (4)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.BFloat16Storage",
    • "torch.FloatStorage"

    What is a pickle import?

    9.98 GB
    xet
    5k about 3 years ago
  • pytorch_model-00002-of-00002.bin
    3.5 GB
    xet
    5k about 3 years ago
  • pytorch_model.bin.index.json
    26.8 kB
    xet
    5k about 3 years ago
  • rng_state.pth
    14.5 kB
    xet
    5k about 3 years ago
  • scheduler.pt
    627 Bytes
    xet
    5k about 3 years ago
  • trainer_state.json
    602 kB
    xet
    5k about 3 years ago
  • training_args.bin
    3.52 kB
    xet
    5k about 3 years ago