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VikramR
/
text2cypher-grpo

Transformers
Safetensors
Generated from Trainer
grpo
unsloth
trl
Model card Files Files and versions
xet
Community

Instructions to use VikramR/text2cypher-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use VikramR/text2cypher-grpo with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("VikramR/text2cypher-grpo", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • Unsloth Studio

    How to use VikramR/text2cypher-grpo 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 VikramR/text2cypher-grpo 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 VikramR/text2cypher-grpo to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for VikramR/text2cypher-grpo to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="VikramR/text2cypher-grpo",
        max_seq_length=2048,
    )
text2cypher-grpo / checkpoint-150
384 MB
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  • 1 contributor
History: 1 commit
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VikramR
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  • README.md
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  • adapter_config.json
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  • adapter_model.safetensors
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  • chat_template.jinja
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  • optimizer.pt
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  • processor_config.json
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  • rng_state.pth
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  • scheduler.pt
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  • tokenizer.json
    32.2 MB
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  • tokenizer_config.json
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  • trainer_state.json
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  • training_args.bin
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