Gemma-2-Fine-Tunning

Tools and method for fine-tuning the Gemma 2 model on custom datasets

Gemma 2

Gemma 2 builds on the foundation of its predecessors by implementing several key architectural improvements and training techniques. These include the use of interleaving local and global attention mechanisms, which enhance the model's ability to handle long sequences, and the Grouped-Query Attention (GQA) method, which optimizes inference speed without sacrificing performance.

The models are trained using a novel approach that replaces the standard next token prediction with knowledge distillation. This method uses a larger "teacher" model to generate a probability distribution of potential next tokens, which the smaller "student" models are trained to predict. This approach significantly boosts the performance of smaller models, enabling them to achieve results that are competitive with much larger models.

Gemma 2 is trained on a massive dataset comprising trillions of tokens from various sources, including web documents, code, and scientific articles. The training process is highly optimized, utilizing advanced infrastructure such as Google's TPUv4 and TPUv5 processors.

In terms of safety and responsibility, the Gemma 2 models are designed with extensive safeguards, including pre-training data filtering and post-training reinforcement learning from human feedback (RLHF) to minimize the risks of harmful outputs. The models are also subjected to rigorous evaluations on a range of benchmarks, demonstrating their superiority in tasks like question answering, commonsense reasoning, and coding.

Despite the advancements, the report also acknowledges the limitations and potential risks of deploying such powerful models. The Gemma 2 team stresses the importance of continued research to address issues like factuality, robustness, and alignment, and to ensure the models are used responsibly in real-world applications.

Low-Rank Adaptation (LoRA)

LoRA (Low-Rank Adaptation) is a technique proposed by Microsoft for adapting large language models like GPT-3 to specific tasks in a more efficient way. Traditional fine-tuning requires updating all the parameters of a model, which becomes impractical for very large models due to high computational and storage costs. LoRA addresses this by freezing the pre-trained model weights and introducing small, trainable low-rank matrices into the Transformer layers. This significantly reduces the number of trainable parameters and the memory required for fine-tuning, without compromising performance.

Key points include:

• Efficiency: LoRA reduces the number of trainable parameters by up to 10,000 times and the GPU memory requirement by 3 times compared to full fine-tuning.
• Performance: Despite using fewer parameters, LoRA matches or even exceeds the performance of fully fine-tuned models on various benchmarks.
• No Added Latency: Unlike other methods that introduce additional layers or complexity, LoRA does not increase inference latency, making it ideal for deployment in production.
• Scalability: LoRA is scalable and can be applied to extremely large models like GPT-3 (175 billion parameters) with significant computational and storage savings.

LoRA is particularly advantageous in scenarios where multiple task-specific models need to be deployed, as it allows quick switching between tasks by only updating the low-rank matrices, rather than the entire model

Fine-Tuning Gemma 2 Models

Gemma 2 is a cutting-edge language model designed to be versatile and efficient, with models available in different parameter sizes. Fine-tuning these models requires different hardware setups depending on the model size:

1. Gemma 2 2B (English) - GPU T4

   For the 2-billion parameter version of Gemma 2, fine-tuning can be performed efficiently using a NVIDIA T4 GPU. The T4 GPU is optimized for AI workloads, offering a balance between performance and cost. It features:

   • Memory: 16 GB GDDR6
   • Performance: 65 TFLOPs for mixed-precision AI tasks
   • Compute Capability: Supports FP32, FP16, and INT8 operations

   Fine-tuning on the T4 is suitable for moderate-scale tasks, where the 2B model's requirements align well with the T4's capabilities, offering decent throughput and training speed.

2. Gemma 2 9B (English) - TPU v3

   For the larger 9-billion parameter version of Gemma 2, fine-tuning is best performed on Google TPU v3. TPUs (Tensor Processing Units) are designed specifically for accelerating machine learning tasks, and the v3 version provides:

   • Memory: 16 GB HBM per core
   • Performance: 420 TFLOPs for mixed-precision AI tasks
   • Architecture: Custom ASIC designed to accelerate TensorFlow operations

   The TPU v3 is ideal for handling the more demanding 9B model, providing the necessary computational power and memory to perform fine-tuning efficiently on larger datasets with higher precision.