Instructions to use jgalego/reqlint-smollm3-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jgalego/reqlint-smollm3-3b with Transformers:

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

pipe = pipeline("text-generation", model="jgalego/reqlint-smollm3-3b")
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("jgalego/reqlint-smollm3-3b")
model = AutoModelForCausalLM.from_pretrained("jgalego/reqlint-smollm3-3b", 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 jgalego/reqlint-smollm3-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jgalego/reqlint-smollm3-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jgalego/reqlint-smollm3-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jgalego/reqlint-smollm3-3b

SGLang

How to use jgalego/reqlint-smollm3-3b 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 "jgalego/reqlint-smollm3-3b" \
    --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": "jgalego/reqlint-smollm3-3b",
		"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 "jgalego/reqlint-smollm3-3b" \
        --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": "jgalego/reqlint-smollm3-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jgalego/reqlint-smollm3-3b with Docker Model Runner:
```
docker model run hf.co/jgalego/reqlint-smollm3-3b
```

jgalego commited on 12 days ago

Commit

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@@ -92,7 +92,7 @@ Expected output:
 | | |
 |---|---|
 | Base | [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) |
-| Data | 20,000 synthetic requirements, generated by `reqlint.py` |
 | Method | LoRA, r=16, alpha=32, all linear layers, merged |
 | Steps | 1250 (1.0 epochs), batch size 16 |
 | Learning rate | 0.0002, cosine |

 | | |
 |---|---|
 | Base | [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) |
+| Data | 20,000 synthetic requirements |
 | Method | LoRA, r=16, alpha=32, all linear layers, merged |
 | Steps | 1250 (1.0 epochs), batch size 16 |
 | Learning rate | 0.0002, cosine |