yzy-python-0.5b 🐍

Lightweight Python-focused language model (0.5B parameters) fine-tuned for code generation and instruction-following.

Optimized for:

• Python code generation
• scripting help
• small coding copilots
• local inference
• experimentation
• hackathons

Base model: Qwen2-0.5B-Instruct
Fine-tuning method: QLoRA (4-bit)
Dataset style: Alpaca-format Python instructions

---

Demo

Transformers usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "SamirXR/yzy-python-0.5b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

prompt = "Write a Python function to reverse a string"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=200
)

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

---

4-bit inference (recommended)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "SamirXR/yzy-python-0.5b"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

prompt = "Write a Python function for fibonacci numbers"

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

outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9
)

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

---

Gradio Chatbot Demo

import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

MODEL_NAME = "SamirXR/yzy-python-0.5b"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokenizer.pad_token = tokenizer.eos_token

def generate_code(instruction, history):
    prompt = f"### Instruction:\n{instruction}\n\n### Response:\n"

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

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
            repetition_penalty=1.1,
            pad_token_id=tokenizer.eos_token_id
        )

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    response = response.split("### Response:\n")[-1].strip()

    return response

demo = gr.ChatInterface(
    fn=generate_code,
    title="yzy-python-0.5b Chatbot",
    description="Python coding assistant (QLoRA fine-tuned Qwen2-0.5B)",
    examples=[
        "Write a function to calculate fibonacci numbers",
        "Create a Python class for a linked list",
        "Reverse a string in Python"
    ],
)

demo.launch(share=True)

---

Training Details

Base model: Qwen/Qwen2-0.5B-Instruct

Dataset: iamtarun/python_code_instructions_18k_alpaca

Format used during training:

### Instruction:
<task>

### Response:
<answer>

Training method: QLoRA (4-bit NF4 quantization)

Key parameters:

• LoRA rank: 8
• alpha: 16
• dropout: 0.05
• epochs: 2
• learning rate: 2e-4
• context length: 512
• optimizer: paged_adamw_8bit

---

Citation

If you use this model, please cite:

Base model: Qwen2 Technical Report (Qwen Team, 2024)

Dataset: python_code_instructions_18k_alpaca (iamtarun)

Model: yzy-python-0.5b (SamirXR)

---

Notes

This is a small model intended for experimentation and lightweight coding assistance. Performance will not match large models but allows fast local inference with minimal resources.