Instructions to use OpenHands/CodeScout-1.7B-RFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenHands/CodeScout-1.7B-RFT with Transformers:

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

pipe = pipeline("text-generation", model="OpenHands/CodeScout-1.7B-RFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenHands/CodeScout-1.7B-RFT")
model = AutoModelForCausalLM.from_pretrained("OpenHands/CodeScout-1.7B-RFT")
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-1.7B-RFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenHands/CodeScout-1.7B-RFT"
# 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-1.7B-RFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenHands/CodeScout-1.7B-RFT

SGLang

How to use OpenHands/CodeScout-1.7B-RFT 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-1.7B-RFT" \
    --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-1.7B-RFT",
		"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-1.7B-RFT" \
        --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-1.7B-RFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenHands/CodeScout-1.7B-RFT with Docker Model Runner:
```
docker model run hf.co/OpenHands/CodeScout-1.7B-RFT
```

gneubig commited on Mar 17

Commit

524c99e

verified ·

1 Parent(s): 9c960aa

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@@ -26,6 +26,10 @@ datasets:
 **Pre-RL checkpoint — rejection fine-tuned on expert trajectories from CodeScout-14B.**
 CodeScout-1.7B-RFT is part of the **CodeScout** family of open-source RL-trained code search agents.
 CodeScout models achieve state-of-the-art repository-level code localization using *nothing more than a standard Unix terminal* — no static analysis, no repository graphs, no language-specific tooling.
@@ -51,6 +55,13 @@ Performance on SWE-Bench code localization (instance-averaged F1 scores):
 | **SWE-Bench Lite** — Func F1 | 27.07 | 39.87 | **44.43** |
 ## Training
 CodeScout-1.7B-RFT is the intermediate checkpoint produced by rejection fine-tuning (RFT) `Qwen3-1.7B` on expert trajectories from CodeScout-14B, before the final RL stage.

 **Pre-RL checkpoint — rejection fine-tuned on expert trajectories from CodeScout-14B.**
+<p align="center">
+  <img src="codescout_overview.png" alt="CodeScout Overview" width="100%">
+</p>
 CodeScout-1.7B-RFT is part of the **CodeScout** family of open-source RL-trained code search agents.
 CodeScout models achieve state-of-the-art repository-level code localization using *nothing more than a standard Unix terminal* — no static analysis, no repository graphs, no language-specific tooling.
 | **SWE-Bench Lite** — Func F1 | 27.07 | 39.87 | **44.43** |
+<p align="center">
+  <img src="f1_vs_params_file.png" alt="File-level F1 vs Model Size" width="48%">
+  <img src="f1_vs_params_function.png" alt="Function-level F1 vs Model Size" width="48%">
+</p>
+<p align="center"><em>Code localization performance on SWE-Bench Verified. CodeScout (⭐) achieves superior or competitive results over larger open-source LLMs and narrows the gap with closed-source frontier models.</em></p>
 ## Training
 CodeScout-1.7B-RFT is the intermediate checkpoint produced by rejection fine-tuning (RFT) `Qwen3-1.7B` on expert trajectories from CodeScout-14B, before the final RL stage.