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

Libraries

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
```

Harsh_GRPO / index.html

TheDeepDas

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<!doctype html>
<html>
	<head>
		<meta charset="utf-8" />
		<meta name="viewport" content="width=device-width" />
		<title>My static Space</title>
		<link rel="stylesheet" href="style.css" />
	</head>
	<body>
		<div class="card">
			<h1>Welcome to your static Space!</h1>
			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
			<p>
				Also don't forget to check the
				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
			</p>
		</div>
	</body>
</html>

	<!doctype html>
	<html>
	<head>
	<meta charset="utf-8" />
	<meta name="viewport" content="width=device-width" />
	<title>My static Space</title>
	<link rel="stylesheet" href="style.css" />
	</head>
	<body>
	<div class="card">
	<h1>Welcome to your static Space!</h1>
	<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
	<p>
	Also don't forget to check the
	<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
	</p>
	</div>
	</body>
	</html>