Instructions to use TheDeepDas/Harsh_GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TheDeepDas/Harsh_GRPO with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "TheDeepDas/Harsh_GRPO") - Transformers
How to use TheDeepDas/Harsh_GRPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheDeepDas/Harsh_GRPO") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheDeepDas/Harsh_GRPO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheDeepDas/Harsh_GRPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheDeepDas/Harsh_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": "TheDeepDas/Harsh_GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/TheDeepDas/Harsh_GRPO
- SGLang
How to use TheDeepDas/Harsh_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 "TheDeepDas/Harsh_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": "TheDeepDas/Harsh_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 "TheDeepDas/Harsh_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": "TheDeepDas/Harsh_GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use TheDeepDas/Harsh_GRPO with Docker Model Runner:
docker model run hf.co/TheDeepDas/Harsh_GRPO
Download trainer_state.json from TheDeepDas/Harsh_GRPO: direct link, hf CLI and curl.
- Browser
- Download file 35.4 kB
-
https://huggingface.co/TheDeepDas/Harsh_GRPO/resolve/main/trainer_state.json
- Command line
-
hf download hf://TheDeepDas/Harsh_GRPO/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/TheDeepDas/Harsh_GRPO/resolve/main/trainer_state.json
35.4 kB
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