GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
LeeChanRX
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
LeeChanRX
Model Tree
Qwen/Qwen2.5-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| 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 |
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport 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)| Parameter | Value |
|---|---|
| Temperature | 0.7 |
| Top-p | 0.9 |
| Top-k | 40 |
| Repeat Penalty | 1.1 |
| Max Tokens | 2048–8192 |
bash
pip install -U transformers accelerate torch sentencepiecePrompt
text
Write a Python function to calculate factorial.Response
python
def factorial(n): if n <= 1: return 1 return n * factorial(n - 1)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
LeeChanRX Studio
Building lightweight, efficient, and open AI assistants.
LeeChan-3B-Instruct v1.0.0
© 2026 LeeChanRX Studio.
from transformers import AutoTokenizer, AutoModelForCausalLMimport 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)pip install -U transformers accelerate torch sentencepiecedef factorial(n): if n <= 1: return 1 return n * factorial(n - 1)