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NebulaByte
/
hindi_gpt2

Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community
2

Instructions to use NebulaByte/hindi_gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use NebulaByte/hindi_gpt2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="NebulaByte/hindi_gpt2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("NebulaByte/hindi_gpt2")
    model = AutoModelForCausalLM.from_pretrained("NebulaByte/hindi_gpt2")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use NebulaByte/hindi_gpt2 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "NebulaByte/hindi_gpt2"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "NebulaByte/hindi_gpt2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/NebulaByte/hindi_gpt2
  • SGLang

    How to use NebulaByte/hindi_gpt2 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 "NebulaByte/hindi_gpt2" \
        --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": "NebulaByte/hindi_gpt2",
    		"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 "NebulaByte/hindi_gpt2" \
            --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": "NebulaByte/hindi_gpt2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use NebulaByte/hindi_gpt2 with Docker Model Runner:

    docker model run hf.co/NebulaByte/hindi_gpt2
hindi_gpt2 / runs
Ctrl+K
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  • 1 contributor
History: 6 commits
NebulaByte's picture
NebulaByte
Model save
d0e2be6 almost 3 years ago
  • Aug01_08-43-38_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_08-47-31_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_08-47-47_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_08-48-09_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_08-50-36_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_08-53-26_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_08-54-45_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_08-56-29_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_09-02-03_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_09-04-00_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_09-05-21_200aef42ed08
    Training in progress, step 400 almost 3 years ago
  • Aug01_09-16-27_200aef42ed08
    Model save almost 3 years ago