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---
license: bigcode-openrail-m
---
# CodeGen-Alpaca-1B
This is a fine-tuned version of `StarCoderBase-1B` using the **CodeAlpaca (2k subset)** dataset.
It has been trained with **QLoRA** on Google Colab for lightweight, memory-efficient code generation.
---
## Model Capabilities
This model understands **instruction-style prompts** for generating code in multiple programming languages, especially Python.
---
## Requirements (Colab Setup)
If you are running this model on **Google Colab**, you’ll need to:
1. Go to the left sidebar and click the **🔑 (Secrets)** tab.
2. Add a new secret with the name:`HF_TOKEN` and set the value to your **Hugging Face token** (from https://huggingface.co/settings/tokens).
3. Enable **Notebook access** for your token.
4. Restart the Colab session.
Then log in inside the notebook:
```python
from huggingface_hub import login
import os
login(token=os.environ["HF_TOKEN"])
```
---
## How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import re
model_id = "key-life/codegen-alpaca-1b"
# Load model & tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
# Example prompt
prompt = "### Instruction:\nWrite a Python function to check if a number is prime.\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
# Generate code
outputs = model.generate(
**inputs,
max_new_tokens=128,
temperature=0.2, # more deterministic
top_p=0.9, # avoids rambling
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id
)
# Decode
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only code block
code_block = re.findall(r"```(?:python)?(.*?)```", decoded, re.DOTALL)
if code_block:
response = code_block[0].strip()
else:
response = decoded.split("### Response:")[-1].strip()
print(response)
``` |