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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeeChanRX/LeeChanRX-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("LeeChanRX/LeeChanRX-3B-Instruct")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeeChanRX/LeeChanRX-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeeChanRX/LeeChanRX-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/LeeChanRX-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use LeeChanRX/LeeChanRX-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/LeeChanRX/LeeChanRX-3B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧠 LeeChan-3B-Instruct

LeeChan-3B-Instruct is a conversational AI assistant model created and fine-tuned by LeeChanRX.

Built on top of Qwen2.5-3B-Instruct, this model is designed to provide natural conversations, helpful responses, coding assistance, and instruction-following behavior with a friendly and stable personality.

The model has been customized to act as “LeeChan”, an intelligent and conversational AI assistant focused on clarity, reliability, and user-friendly interaction.

✨ Features

Conversational AI assistant
Instruction-following optimized
Coding and programming support
Friendly and natural responses
Stable chat behavior
Fine-tuned personality alignment
Lightweight 3B parameter architecture
Transformers compatible
Standalone merged model

🏗️ Base Model

This model is fine-tuned from:

Qwen/Qwen2.5-3B-Instruct

Credits and appreciation go to the original Qwen team for providing the open-source foundation model.

🚀 Usage

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

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

tokenizer = AutoTokenizer.from_pretrained(model_name)

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

messages = [
    {
        "role": "system",
        "content": "You are LeeChan, a helpful AI assistant."
    },
    {
        "role": "user",
        "content": "Hello"
    }
]

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=128,
    temperature=0.7,
    repetition_penalty=1.1
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for LeeChanRX/LeeChanRX-3B-Instruct

Adapters

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Quantizations

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