Instructions to use LeeXugar/CodePin-SFT-Qwen3.5-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeeXugar/CodePin-SFT-Qwen3.5-0.8B with Transformers:

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

pipe = pipeline("text-generation", model="LeeXugar/CodePin-SFT-Qwen3.5-0.8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeeXugar/CodePin-SFT-Qwen3.5-0.8B")
model = AutoModelForCausalLM.from_pretrained("LeeXugar/CodePin-SFT-Qwen3.5-0.8B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LeeXugar/CodePin-SFT-Qwen3.5-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeeXugar/CodePin-SFT-Qwen3.5-0.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeeXugar/CodePin-SFT-Qwen3.5-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeeXugar/CodePin-SFT-Qwen3.5-0.8B

SGLang

How to use LeeXugar/CodePin-SFT-Qwen3.5-0.8B 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 "LeeXugar/CodePin-SFT-Qwen3.5-0.8B" \
    --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": "LeeXugar/CodePin-SFT-Qwen3.5-0.8B",
		"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 "LeeXugar/CodePin-SFT-Qwen3.5-0.8B" \
        --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": "LeeXugar/CodePin-SFT-Qwen3.5-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LeeXugar/CodePin-SFT-Qwen3.5-0.8B with Docker Model Runner:
```
docker model run hf.co/LeeXugar/CodePin-SFT-Qwen3.5-0.8B
```

CodePin-SFT-Qwen3.5-0.8B / training /data_filter_summary.json

LeeXugar

Add model card and reproducible SFT process artifacts

93fc4c9 verified 4 days ago

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	{
	"tokenizer": "Qwen/Qwen3.5-0.8B",
	"max_length": 8192,
	"policy": "retain only complete trajectories whose tokenized length is at most max_length",
	"train": {
	"input_rows": 5700,
	"kept_rows": 5676,
	"dropped_rows": 24,
	"kept_sha256": "4525fc354089ba06e18a85d80a03c767239d4a2eaecfa58df4a67f3a3ee4ecb9"
	},
	"validation": {
	"input_rows": 300,
	"kept_rows": 294,
	"dropped_rows": 6,
	"kept_sha256": "c41d95d99d567b2734b382ba26aa986104b37ca3b506de32a32799a471c9d17d"
	}
	}