Instructions to use Octen/Octen-WebGR-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Octen/Octen-WebGR-4B with Transformers:

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

pipe = pipeline("text-generation", model="Octen/Octen-WebGR-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Octen/Octen-WebGR-4B")
model = AutoModelForCausalLM.from_pretrained("Octen/Octen-WebGR-4B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Octen/Octen-WebGR-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Octen/Octen-WebGR-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Octen/Octen-WebGR-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Octen/Octen-WebGR-4B

SGLang

How to use Octen/Octen-WebGR-4B 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 "Octen/Octen-WebGR-4B" \
    --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": "Octen/Octen-WebGR-4B",
		"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 "Octen/Octen-WebGR-4B" \
        --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": "Octen/Octen-WebGR-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Octen/Octen-WebGR-4B with Docker Model Runner:
```
docker model run hf.co/Octen/Octen-WebGR-4B
```

Octen-WebGR-4B / requirements.txt

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# Versions the release was validated with. infer_cpu.py relies on DynamicCache.batch_repeat_interleave (transformers 4.x).
torch==2.8.0
transformers==4.57.1
pyarrow>=14
numpy>=1.26
huggingface_hub>=0.26
# Optional, GPU only: flash-attn==2.8.3 (infer_gpu.py falls back to SDPA without it)

	# Versions the release was validated with. infer_cpu.py relies on DynamicCache.batch_repeat_interleave (transformers 4.x).
	torch==2.8.0
	transformers==4.57.1
	pyarrow>=14
	numpy>=1.26
	huggingface_hub>=0.26
	# Optional, GPU only: flash-attn==2.8.3 (infer_gpu.py falls back to SDPA without it)