Instructions to use omar81939/Ouro-1.4B-Thinking-depth-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omar81939/Ouro-1.4B-Thinking-depth-GRPO with Transformers:

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

pipe = pipeline("text-generation", model="omar81939/Ouro-1.4B-Thinking-depth-GRPO", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("omar81939/Ouro-1.4B-Thinking-depth-GRPO", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use omar81939/Ouro-1.4B-Thinking-depth-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omar81939/Ouro-1.4B-Thinking-depth-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": "omar81939/Ouro-1.4B-Thinking-depth-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/omar81939/Ouro-1.4B-Thinking-depth-GRPO

SGLang

How to use omar81939/Ouro-1.4B-Thinking-depth-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 "omar81939/Ouro-1.4B-Thinking-depth-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": "omar81939/Ouro-1.4B-Thinking-depth-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 "omar81939/Ouro-1.4B-Thinking-depth-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": "omar81939/Ouro-1.4B-Thinking-depth-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use omar81939/Ouro-1.4B-Thinking-depth-GRPO with Docker Model Runner:
```
docker model run hf.co/omar81939/Ouro-1.4B-Thinking-depth-GRPO
```

Ouro-1.4B-Thinking-depth-GRPO / chat_template.jinja

omar81939

Depth-as-action GRPO, step 400 (single seed, MATH-500)

7e7e20d verified about 2 months ago

Raw History Blame Contribute Delete

599 Bytes

{%- if messages[0]['role'] == 'system' -%}{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}{%- else -%}{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{%- endif -%}{%- for message in messages -%}{%- if message.role == 'system' and loop.first -%}{# Skip #}{%- else -%}{{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<|im_start|>assistant\n' }}{%- if enable_thinking is defined and enable_thinking is true -%}{{- '<think>\n' }}{%- endif -%}{%- endif -%}