Instructions to use dipta007/decomposeRL-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dipta007/decomposeRL-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dipta007/decomposeRL-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dipta007/decomposeRL-7b")
model = AutoModelForCausalLM.from_pretrained("dipta007/decomposeRL-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dipta007/decomposeRL-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dipta007/decomposeRL-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dipta007/decomposeRL-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dipta007/decomposeRL-7b

SGLang

How to use dipta007/decomposeRL-7b 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 "dipta007/decomposeRL-7b" \
    --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": "dipta007/decomposeRL-7b",
		"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 "dipta007/decomposeRL-7b" \
        --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": "dipta007/decomposeRL-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dipta007/decomposeRL-7b with Docker Model Runner:
```
docker model run hf.co/dipta007/decomposeRL-7b
```

dipta007 commited on 9 days ago

Commit

70ca118

verified ·

1 Parent(s): 54b21c8

Update

Browse files

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README.md +6 -0

README.md CHANGED Viewed

@@ -85,6 +85,12 @@ GRPO is supervised with a sum of seven rewards, grouped into three families:
 ## Quickstart
 DecomposeRL expects a specific verification prompt around your `claim` + `evidence_doc`. The `build_prompt` helper below wraps them for you so you don't have to construct the full instruction block every time.
 ```python

 ## Quickstart
+A complete runnable script is included in the repo as [`example.py`](./example.py) (download it [here](https://huggingface.co/dipta007/decomposeRL-7b/resolve/main/example.py)):
+```bash
+python example.py
+```
 DecomposeRL expects a specific verification prompt around your `claim` + `evidence_doc`. The `build_prompt` helper below wraps them for you so you don't have to construct the full instruction block every time.
 ```python