Instructions to use OpenHands/CodeScout-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenHands/CodeScout-14B with Transformers:

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

pipe = pipeline("text-generation", model="OpenHands/CodeScout-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenHands/CodeScout-14B")
model = AutoModelForCausalLM.from_pretrained("OpenHands/CodeScout-14B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenHands/CodeScout-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenHands/CodeScout-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenHands/CodeScout-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenHands/CodeScout-14B

SGLang

How to use OpenHands/CodeScout-14B 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 "OpenHands/CodeScout-14B" \
    --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": "OpenHands/CodeScout-14B",
		"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 "OpenHands/CodeScout-14B" \
        --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": "OpenHands/CodeScout-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenHands/CodeScout-14B with Docker Model Runner:
```
docker model run hf.co/OpenHands/CodeScout-14B
```

gneubig commited on Mar 18

Commit

cd27c11

verified ·

1 Parent(s): 72baeee

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -22,7 +22,7 @@ datasets:
 # CodeScout-14B
-[📄 Paper](https://arxiv.org/abs/2507.02875) • [💻 Code](https://github.com/OpenHands/codescout) • [🤗 Collection](https://huggingface.co/collections/OpenHands/codescout-69b9a6adcf21f348f4db937f)
 **Strongest CodeScout model — open-source SOTA on SWE-Bench code localization.**
@@ -99,7 +99,7 @@ CodeScout-14B is designed for **repository-level code localization**: given a Gi
 @article{sutawika2025codescout,
   title={CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents},
   author={Sutawika, Lintang and Soni, Aditya Bharat and R R, Bharath Sriraam and Gandhi, Apurva and Yassine, Taha and Vijayvargiya, Sanidhya and Li, Yuchen and Zhou, Xuhui and Zhang, Yilin and Maben, Leander Melroy and Neubig, Graham},
-  journal={arXiv preprint arXiv:2507.02875},
   year={2025}
 }
 ```

 # CodeScout-14B
+[📄 Paper](https://arxiv.org/abs/XXXX.XXXXX) • [💻 Code](https://github.com/OpenHands/codescout) • [🤗 Collection](https://huggingface.co/collections/OpenHands/codescout-69b9a6adcf21f348f4db937f)
 **Strongest CodeScout model — open-source SOTA on SWE-Bench code localization.**
 @article{sutawika2025codescout,
   title={CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents},
   author={Sutawika, Lintang and Soni, Aditya Bharat and R R, Bharath Sriraam and Gandhi, Apurva and Yassine, Taha and Vijayvargiya, Sanidhya and Li, Yuchen and Zhou, Xuhui and Zhang, Yilin and Maben, Leander Melroy and Neubig, Graham},
+  journal={arXiv preprint arXiv:XXXX.XXXXX},
   year={2025}
 }
 ```