---
base_model: google/gemma-2-9b-it
library_name: peft
license: gemma
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: gemma-2-9b-it-lora-yt-titles
  results: []
datasets:
- AdamLucek/youtube-titles
pipeline_tag: text-generation
---

# LoRA Adapters for Gemma-2-9B-IT on YouTube Titles

These are LoRA adapters for [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) trained on [AdamLucek/youtube-titles](https://huggingface.co/datasets/AdamLucek/youtube-titles).

Intended task is to tune Gemma 2 9B to generate YouTube title's more similar to popular YouTubers, data was prepped in the instruction tuned token format.

## Intended uses & limitations

See original model page [google/gemma-2-9b-it intended usage](https://huggingface.co/google/gemma-2-9b-it#intended-usage) for details about Gemma 2 9B usage, limitations, and ethical considerations.

## Usage

The below code will show you how to load and interface with the LoRA model.

**Loading the Model & LoRA Adapters**
```python
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

# Load the Pre Trained Model
model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b-it", 
                                             quantization_config=BitsAndBytesConfig(load_in_8bit=True), 
                                             device_map="auto"
                                             ).eval()
# Load the Tokenizer
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")

# Attach LoRA Adapters to Pre Trained Model
model = PeftModel.from_pretrained(model, "AdamLucek/gemma-2-9b-it-lora-yt-titles", adapter_name="youtube_titles")
```
**Inference**

```python
topic = "huggingface AI models"
messages = [
    {"role": "user", "content": f"Create a YouTube title about {topic}"}
]

# Apply chat template and prepare inputs
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}

# Generate outputs
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    top_p=0.95,
    temperature=0.1,
    repetition_penalty=1.2,
    eos_token_id=tokenizer.eos_token_id
)
  
# Decode outputs
decoded = tokenizer.decode(outputs[0])

print(decoded)
```

## Training procedure

### Training hyperparameters

Trained on a single a6000 using the following script

```
python \
    examples/scripts/sft.py \
    --model_name_or_path="google/gemma-2-9b-it" \
    --dataset_name="AdamLucek/youtube-titles" \
    --dataset_text_field="gemma2_9b_it_format" \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --gradient_accumulation_steps=4 \
    --max_grad_norm=1.0 \
    --learning_rate=5e-5 \
    --weight_decay=0.01 \
    --lr_scheduler_type="cosine" \
    --warmup_ratio=0.1 \
    --report_to="wandb" \
    --bf16 \
    --max_seq_length=2048 \
    --lora_r=16 \
    --lora_alpha=32 \
    --lora_target_modules q_proj k_proj v_proj o_proj \
    --load_in_8bit \
    --use_peft \
    --attn_implementation="eager" \
    --logging_steps=1 \
    --eval_strategy="steps" \
    --eval_steps=200 \
    --save_strategy="steps" \
    --save_steps=250 \
    --output_dir="models/gemma2" \
    --hub_model_id="gemma-2-9b-it-lora-yt-titles" \
    --push_to_hub \
    --num_train_epochs=3
```

### Training results

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/adam-lucek/huggingface/runs/vhp5k2tx)

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.2556        | 0.7619 | 200  | 2.0945          |
| 2.1866        | 1.5238 | 400  | 2.0988          |
| 2.3421        | 2.2857 | 600  | 2.2142          |

It achieves the following results on the evaluation set:
- Loss: 2.2142

### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.0.1
- Datasets 2.20.0
- Tokenizers 0.19.1