Instructions to use LeeChanRX/LeeChan-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeeChanRX/LeeChan-3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="LeeChanRX/LeeChan-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeeChanRX/LeeChan-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("LeeChanRX/LeeChan-3B-Instruct", 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 LeeChanRX/LeeChan-3B-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LeeChanRX/LeeChan-3B-Instruct

SGLang

How to use LeeChanRX/LeeChan-3B-Instruct 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 "LeeChanRX/LeeChan-3B-Instruct" \
    --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": "LeeChanRX/LeeChan-3B-Instruct",
		"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 "LeeChanRX/LeeChan-3B-Instruct" \
        --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": "LeeChanRX/LeeChan-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LeeChanRX/LeeChan-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/LeeChanRX/LeeChan-3B-Instruct
```

LeeChan-3B-Instruct

Developed by LeeChanRX Studio

LeeChan-3B-Instruct is a customized conversational AI model developed by LeeChanRX Studio. It is designed for chat, coding, reasoning, writing, mathematics, translation, and general-purpose AI assistance.

✨ Features

🤖 Intelligent AI Assistant
💻 Code Generation & Debugging
🧠 Advanced Reasoning
📚 Question Answering
✍️ Content Writing
🌍 Multilingual Support
📄 JSON & Structured Output
⚡ GGUF Optimized
📝 Long Context Conversations

📊 Model Information

Property	Value
Model Name	LeeChan-3B-Instruct
Developer	LeeChanRX Studio
Parameters	3.09 Billion
Architecture	Transformer
Context Length	32,768 Tokens
Max Generation	8,192 Tokens
Format	Hugging Face Transformers

🚀 Usage

Python (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "LeeChanRX/LeeChan-3B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto"
)

messages = [
    {
        "role": "system",
        "content": "You are LeeChan-3B-Instruct, developed by LeeChanRX Studio."
    },
    {
        "role": "user",
        "content": "Hello! Introduce yourself."
    }
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9
)

response = tokenizer.decode(
    outputs[0][inputs.input_ids.shape[-1]:],
    skip_special_tokens=True
)

print(response)

⚙️ Recommended Settings

Parameter	Value
Temperature	0.7
Top-p	0.9
Top-k	40
Repeat Penalty	1.1
Max Tokens	2048–8192

📦 Installation

pip install -U transformers accelerate torch sentencepiece

🧪 Example

Prompt

Write a Python function to calculate factorial.

Response

def factorial(n):
    if n <= 1:
        return 1
    return n * factorial(n - 1)

📜 License

This project is distributed under the original license applicable to the base model.

For complete license information, see:

https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE

👨‍💻 Developer

LeeChanRX Studio

Building lightweight, efficient, and open AI assistants.

📌 Version

LeeChan-3B-Instruct v1.0.0

Downloads last month: 98

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for LeeChanRX/LeeChan-3B-Instruct

Base model

Qwen/Qwen2.5-3B

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Qwen/Qwen2.5-3B-Instruct

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