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igorktech
/
RuBit-LLama-63M

Text Generation
Transformers
Safetensors
Russian
English
llama
Generated from Trainer
bitnet
rulm
darulm
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use igorktech/RuBit-LLama-63M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use igorktech/RuBit-LLama-63M with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="igorktech/RuBit-LLama-63M")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("igorktech/RuBit-LLama-63M")
    model = AutoModelForCausalLM.from_pretrained("igorktech/RuBit-LLama-63M")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use igorktech/RuBit-LLama-63M with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "igorktech/RuBit-LLama-63M"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "igorktech/RuBit-LLama-63M",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/igorktech/RuBit-LLama-63M
  • SGLang

    How to use igorktech/RuBit-LLama-63M 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 "igorktech/RuBit-LLama-63M" \
        --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": "igorktech/RuBit-LLama-63M",
    		"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 "igorktech/RuBit-LLama-63M" \
            --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": "igorktech/RuBit-LLama-63M",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use igorktech/RuBit-LLama-63M with Docker Model Runner:

    docker model run hf.co/igorktech/RuBit-LLama-63M
RuBit-LLama-63M
513 MB
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  • 1 contributor
History: 10 commits
igorktech's picture
igorktech
Update README.md
9eb6b38 verified over 1 year ago
  • final_model
    Model save about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    3.23 kB
    Update README.md over 1 year ago
  • config.json
    701 Bytes
    Training in progress, step 16219 about 2 years ago
  • generation_config.json
    132 Bytes
    Model save about 2 years ago
  • model.safetensors
    253 MB
    xet
    Training in progress, step 64876 about 2 years ago
  • special_tokens_map.json
    510 Bytes
    Training in progress, step 16219 about 2 years ago
  • tokenizer.json
    2.27 MB
    Training in progress, step 16219 about 2 years ago
  • tokenizer.model
    990 kB
    xet
    Training in progress, step 16219 about 2 years ago
  • tokenizer_config.json
    1.01 kB
    Training in progress, step 16219 about 2 years ago
  • training_args.bin

    Detected Pickle imports (9)

    • "transformers.trainer_utils.IntervalStrategy",
    • "accelerate.utils.dataclasses.DistributedType",
    • "transformers.trainer_pt_utils.AcceleratorConfig",
    • "transformers.trainer_utils.SchedulerType",
    • "transformers.training_args.OptimizerNames",
    • "accelerate.state.PartialState",
    • "torch.device",
    • "transformers.training_args.TrainingArguments",
    • "transformers.trainer_utils.HubStrategy"

    How to fix it?

    4.98 kB
    xet
    Training in progress, step 16219 about 2 years ago