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vladocar
/
model

Text Generation
Transformers
PyTorch
English
gemma2
text-generation-inference
unsloth
trl
sft
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use vladocar/model with Transformers:

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

    How to use vladocar/model with vLLM:

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

    How to use vladocar/model 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 "vladocar/model" \
        --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": "vladocar/model",
    		"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 "vladocar/model" \
            --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": "vladocar/model",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Unsloth Studio new

    How to use vladocar/model with Unsloth Studio:

    Install Unsloth Studio (macOS, Linux, WSL)
    curl -fsSL https://unsloth.ai/install.sh | sh
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for vladocar/model to start chatting
    Install Unsloth Studio (Windows)
    irm https://unsloth.ai/install.ps1 | iex
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for vladocar/model to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for vladocar/model to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="vladocar/model",
        max_seq_length=2048,
    )
  • Docker Model Runner

    How to use vladocar/model with Docker Model Runner:

    docker model run hf.co/vladocar/model
model
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  • 1 contributor
History: 4 commits
vladocar's picture
vladocar
Trained with Unsloth
9f8329f verified almost 2 years ago
  • .gitattributes
    1.57 kB
    Upload tokenizer almost 2 years ago
  • README.md
    583 Bytes
    Trained with Unsloth almost 2 years ago
  • config.json
    923 Bytes
    Trained with Unsloth almost 2 years ago
  • generation_config.json
    168 Bytes
    Trained with Unsloth almost 2 years ago
  • pytorch_model-00001-of-00002.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    4.99 GB
    xet
    Trained with Unsloth almost 2 years ago
  • pytorch_model-00002-of-00002.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    241 MB
    xet
    Trained with Unsloth almost 2 years ago
  • pytorch_model.bin.index.json
    24.2 kB
    Trained with Unsloth almost 2 years ago
  • special_tokens_map.json
    555 Bytes
    Upload tokenizer almost 2 years ago
  • tokenizer.json
    17.5 MB
    xet
    Upload tokenizer almost 2 years ago
  • tokenizer.model
    4.24 MB
    xet
    Upload tokenizer almost 2 years ago
  • tokenizer_config.json
    46.3 kB
    Upload tokenizer almost 2 years ago