Instructions to use laion/SweSmith-8B-SFT-Rope-step62 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use laion/SweSmith-8B-SFT-Rope-step62 with Transformers:

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

pipe = pipeline("text-generation", model="laion/SweSmith-8B-SFT-Rope-step62")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("laion/SweSmith-8B-SFT-Rope-step62")
model = AutoModelForCausalLM.from_pretrained("laion/SweSmith-8B-SFT-Rope-step62")
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 Settings

vLLM

How to use laion/SweSmith-8B-SFT-Rope-step62 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "laion/SweSmith-8B-SFT-Rope-step62"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "laion/SweSmith-8B-SFT-Rope-step62",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/laion/SweSmith-8B-SFT-Rope-step62

SGLang

How to use laion/SweSmith-8B-SFT-Rope-step62 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 "laion/SweSmith-8B-SFT-Rope-step62" \
    --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": "laion/SweSmith-8B-SFT-Rope-step62",
		"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 "laion/SweSmith-8B-SFT-Rope-step62" \
        --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": "laion/SweSmith-8B-SFT-Rope-step62",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use laion/SweSmith-8B-SFT-Rope-step62 with Docker Model Runner:
```
docker model run hf.co/laion/SweSmith-8B-SFT-Rope-step62
```

marianna13 commited on Mar 16

Commit

5bddf43

verified ·

1 Parent(s): 0020fe4

Update README.md

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@@ -22,20 +22,9 @@ RL-trained Qwen3-8B on SWEsmith tasks (65k context with YaRN rope scaling factor
 ## Training Details
 - **Base model**: [laion/r2egym-nl2bash-stack-bugsseq-fixthink-again](https://huggingface.co/laion/r2egym-nl2bash-stack-bugsseq-fixthink-again) (Qwen3-8B SFT)
-- **Training method**: RLOO-N (Reinforcement Learning with Leave-One-Out baselines)
 - **Training data**: 2,500 SWEsmith tasks (oracle-verified, 120s timeout)
-- **Framework**: BenSkyRL + Harbor (Apptainer Bridge on JUWELS)
 - **Sandbox**: Apptainer containers with proxychains for internet access
-## Eval Results (dev_set_71_tasks, n=3)
-See results_20260316.md for full comparison tables.
-## Usage
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("laion/SweSmith-8B-SFT-Rope-step62")
-tokenizer = AutoTokenizer.from_pretrained("laion/SweSmith-8B-SFT-Rope-step62")
-```

 ## Training Details
 - **Base model**: [laion/r2egym-nl2bash-stack-bugsseq-fixthink-again](https://huggingface.co/laion/r2egym-nl2bash-stack-bugsseq-fixthink-again) (Qwen3-8B SFT)
+- **Training method**: RLOO-N
 - **Training data**: 2,500 SWEsmith tasks (oracle-verified, 120s timeout)
+- **Framework**: BenSkyRL + Harbor
 - **Sandbox**: Apptainer containers with proxychains for internet access