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: 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:

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 👩‍💻

I recommend install the following version of torch:

pip install "torch>=2.1.1" -U

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

model_id = "MAISAAI/gemma-2b-coder"

tokenizer = AutoTokenizer.from_pretrained(model_id)

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

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

bibtex

@misc {maisa_ai_2024,
	author       = { {MAISA AI} },
	title        = { gemma-2b-coder (Revision e5e4e5b) },
	year         = 2024,
	url          = { https://huggingface.co/MAISAAI/gemma-2b-coder },
	doi          = { 10.57967/hf/2208 },
	publisher    = { Hugging Face }
}