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
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Grogros
/
Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct

Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct")
    model = AutoModelForCausalLM.from_pretrained("Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct
  • SGLang

    How to use Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct 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 "Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct" \
        --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": "Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct",
    		"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 "Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct" \
            --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": "Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct with Docker Model Runner:

    docker model run hf.co/Grogros/Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct
Grogros-gpt2-kgw-k1-delta2.0-LearnabilityScratch-OpenMathInstruct
2.99 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 12 commits
Grogros's picture
Grogros
Model save
665fefe verified over 1 year ago
  • checkpoint-1000
    Training in progress, step 1000, checkpoint over 1 year ago
  • checkpoint-1500
    Training in progress, step 1500, checkpoint over 1 year ago
  • checkpoint-2000
    Training in progress, step 2000, checkpoint over 1 year ago
  • checkpoint-2500
    Training in progress, step 2500, checkpoint over 1 year ago
  • checkpoint-500
    Training in progress, step 500, checkpoint over 1 year ago
  • runs
    Training in progress, step 2500 over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    1.39 kB
    Model save over 1 year ago
  • config.json
    951 Bytes
    Training in progress, step 500 over 1 year ago
  • generation_config.json
    119 Bytes
    Model save over 1 year ago
  • model.safetensors
    498 MB
    xet
    Training in progress, step 2500 over 1 year ago
  • training_args.bin

    Detected Pickle imports (9)

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

    How to fix it?

    5.37 kB
    xet
    Training in progress, step 500 over 1 year ago