---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
- code
- coding
- qwen
- qwen2
- slm
- trl
- fine-tuned
license: apache-2.0
language:
- en
- code
---

# light-coder

`light-coder` is an ultra-lightweight, standalone instruction-tuned coding model created by fine-tuning [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on ~122k programming instruction-response pairs and merging the LoRA weights directly into the base checkpoint.

At under 1 GB in size, it requires minimal VRAM, executes quickly on consumer GPUs and CPUs, and integrates out-of-the-box with tools like vLLM, Ollama, and standard Hugging Face pipelines.

## Model Details

- **Developed by:** Milad Asghari
- **Model Name:** light-coder
- **Base Model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
- **Model Type:** Causal Language Model (Full Merged Weights)
- **Primary Domain:** Code generation, refactoring, and programming instruction-following
- **Language(s):** English, Multiple Programming Languages
- **License:** Apache-2.0
- **Size:** 988 MB (`safetensors`)

## How to Get Started

Because the weights are merged, you do not need the `peft` library for inference:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Miladasghari/light-coder"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Write a Python function to check if a string is a palindrome."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    top_p=0.9,
    repetition_penalty=1.05
)

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