README.md

---
tags:
- generated_from_trainer
- code
- coding
- llama
model-index:
- name: gemma-2b-coder
  results: []
license: apache-2.0
language:
- code
thumbnail: https://huggingface.co/mrm8488/gemma-2b-coder/resolve/main/logo.png
datasets:
- HuggingFaceH4/CodeAlpaca_20K
pipeline_tag: text-generation
---

<div style="text-align:center;width:250px;height:250px;">
    <img src="https://huggingface.co/mrm8488/gemma-2b-coder/resolve/main/logo.png" alt="gemma coder logo"">
</div>


# Gemma Coder 👩‍💻
**Gemma 2B** fine-tuned on the **CodeAlpaca 20k instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library.

## Model description 🧠

[Gemma-2b](https://huggingface.co/google/gemma-2b)

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.


## Training and evaluation data 📚

[CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K): contains 20K instruction-following data used for fine-tuning the Code Alpaca model.


### Training hyperparameters ⚙

Training took 1h 40 min on Free Colab T4 GPU (16GB VRAM) with the following params:

```py
num_train_epochs=2,
per_device_train_batch_size=2,
per_device_eval_batch_size=1,
gradient_accumulation_steps=32
learning_rate=2.5e-5,
optim="paged_adamw_8bit",
logging_steps=5,
seed=66,
load_best_model_at_end=True,
save_strategy="steps",
save_steps=50,                
evaluation_strategy="steps",
eval_steps=50,
save_total_limit=2,
remove_unused_columns=True,
fp16=True,
bf16=False
```

### Training results 🗒️

| Step | Training Loss | Validation Loss |
|------|---------------|-----------------|
| 50   | 1.467800      | 1.450770        |
| 100  | 1.060000      | 1.064840        |
| 150  | 0.900200      | 0.922290        |
| 200  | 0.848400      | 0.879911        |
| 250  | 0.838100      | 0.867354        |



### Eval results 📊

WIP


### Example of usage 👩‍💻
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

model_id = "mrm8488/llama-2-coder-7b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")

def create_prompt(instruction):
  system = "You are a coding assistant that will help the user to resolve the following instruction:"
  instruction = "### Instruction: " + instruction
  return system + "\n" + instruction + "\n\n" + "### Solution:" + "\n"

def generate(
        instruction,
        max_new_tokens=256,
        temperature=0.1,
        top_p=0.75,
        top_k=40,
        num_beams=2,
        **kwargs,
):
    system = f"<bos><|system|>\nYou are a helpful coding assistant.<eos>\n"
    prompt = f"{system}<|user|>\n{instruction}<eos>\n<|assistant|>\n"
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].to("cuda")
    attention_mask = inputs["attention_mask"].to("cuda")
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        **kwargs,
    )
    with torch.no_grad():
        generation_output = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            generation_config=generation_config,
            return_dict_in_generate=True,
            max_new_tokens=max_new_tokens,
            early_stopping=True
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s, skip_special_tokens=True)
    return output.split("<|assistant|>")[1]

instruction = """
Edit the following XML code to add a navigation bar to the top of a web page
<html>
<head>
  <title>Maisa</title>
</head>
"""
print(generate(instruction))
```

### Citation

WIP