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 /final_hf_validation.json

LeeXugar

Add model card and reproducible SFT process artifacts

93fc4c9 verified 4 days ago

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592 Bytes

	{
	"status": "PASS",
	"architecture": ["Qwen3_5ForCausalLM"],
	"model_type": "qwen3_5_text",
	"tie_word_embeddings": false,
	"loaded_dtype": "torch.float16",
	"parameter_numel": 1006672704,
	"safetensors_numel": 1006672704,
	"safetensors_tensor_count": 321,
	"safetensors_dtype_counts": {"F32": 321},
	"model_safetensors_bytes": 4026732464,
	"model_safetensors_sha256": "d97009d9e41838a2eb8ce0e6275ac41cf80f1186915628f31662d3ba0b67e48e",
	"all_parameters_finite": true,
	"generation_new_tokens": 16,
	"torch": "2.8.0+cu128",
	"cuda": "12.8",
	"gpu": "Tesla V100-PCIE-32GB"
	}