Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

Lomesh7777
/
salespath-grpo

Text Generation
PEFT
Safetensors
Transformers
grpo
lora
trl
conversational
Model card Files Files and versions
xet
Community

Instructions to use Lomesh7777/salespath-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use Lomesh7777/salespath-grpo with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct")
    model = PeftModel.from_pretrained(base_model, "Lomesh7777/salespath-grpo")
  • Transformers

    How to use Lomesh7777/salespath-grpo with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Lomesh7777/salespath-grpo")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Lomesh7777/salespath-grpo", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Lomesh7777/salespath-grpo with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Lomesh7777/salespath-grpo"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Lomesh7777/salespath-grpo",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Lomesh7777/salespath-grpo
  • SGLang

    How to use Lomesh7777/salespath-grpo 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 "Lomesh7777/salespath-grpo" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Lomesh7777/salespath-grpo",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    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 "Lomesh7777/salespath-grpo" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Lomesh7777/salespath-grpo",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use Lomesh7777/salespath-grpo with Docker Model Runner:

    docker model run hf.co/Lomesh7777/salespath-grpo
salespath-grpo / checkpoint-100
46.6 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Lomesh7777's picture
Lomesh7777
Upload folder using huggingface_hub
88d2321 verified 7 days ago
  • ref
    Upload folder using huggingface_hub 7 days ago
  • README.md
    5.23 kB
    Upload folder using huggingface_hub 8 days ago
  • adapter_config.json
    1.03 kB
    Upload folder using huggingface_hub 8 days ago
  • adapter_model.safetensors
    8.73 MB
    xet
    Upload folder using huggingface_hub 7 days ago
  • chat_template.jinja
    2.51 kB
    Upload folder using huggingface_hub 8 days ago
  • optimizer.pt
    17.5 MB
    xet
    Upload folder using huggingface_hub 7 days ago
  • rng_state.pth
    14.2 kB
    xet
    Upload folder using huggingface_hub 7 days ago
  • scaler.pt
    988 Bytes
    xet
    Upload folder using huggingface_hub 8 days ago
  • scheduler.pt
    1.06 kB
    xet
    Upload folder using huggingface_hub 7 days ago
  • tokenizer.json
    11.4 MB
    xet
    Upload folder using huggingface_hub 8 days ago
  • tokenizer_config.json
    399 Bytes
    Upload folder using huggingface_hub 8 days ago
  • trainer_state.json
    108 kB
    Upload folder using huggingface_hub 7 days ago
  • training_args.bin
    6.78 kB
    xet
    Upload folder using huggingface_hub 7 days ago