File size: 2,069 Bytes
cf47703 67915ce 9c1b53f 67915ce 9c1b53f 67915ce 9c1b53f 67915ce cf47703 67915ce cf47703 9c1b53f cf47703 9c1b53f 67915ce 9c1b53f 67915ce 9c1b53f cf47703 67915ce cf47703 9c1b53f cf47703 67915ce cf47703 9c1b53f cf47703 9c1b53f 67915ce cf47703 67915ce cf47703 67915ce cf47703 9c1b53f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | ---
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) |