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

Prahaladha
/
math-checkpoints

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

Instructions to use Prahaladha/math-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Prahaladha/math-checkpoints with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Prahaladha/math-checkpoints", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • Unsloth Studio

    How to use Prahaladha/math-checkpoints 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 Prahaladha/math-checkpoints 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 Prahaladha/math-checkpoints to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for Prahaladha/math-checkpoints to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="Prahaladha/math-checkpoints",
        max_seq_length=2048,
    )
math-checkpoints
5.05 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 91 commits
Prahaladha's picture
Prahaladha
Upload phaseC_hard/final/chat_template.jinja with huggingface_hub
b38e494 verified 12 months ago
  • checkpoint-200
    Upload checkpoint-200/adapter_model.safetensors with huggingface_hub 12 months ago
  • checkpoint-366
    Upload checkpoint-366/adapter_model.safetensors with huggingface_hub 12 months ago
  • final
    Upload final/adapter_model.safetensors with huggingface_hub 12 months ago
  • phaseB_medium
    Upload phaseB_medium/final/training_args.bin with huggingface_hub 12 months ago
  • phaseC_hard
    Upload phaseC_hard/final/chat_template.jinja with huggingface_hub 12 months ago
  • .gitattributes
    2.08 kB
    Upload phaseC_hard/final/tokenizer.json with huggingface_hub 12 months ago
  • README.md
    1.47 kB
    Upload README.md with huggingface_hub 12 months ago