By: Zeeshan Ali
This blog highlights the utilization of fine-tuning techniques on the LLAMA-2 model to facilitate the generation of Python code. By refining the model through targeted adjustments, we demonstrate its capacity to produce high-quality Python code snippets. This approach signifies a significant advancement in code generation, emphasizing the role of advanced language models in enhancing programming tasks. The abstract encapsulates the process, outcomes, and implications of employing fine-tuned LLAMA-2 for Python code generation, offering a glimpse into the potential of AI-driven coding assistance.
Preparing a Dataset
A training dataset that includes columns such as “instruction,” “input,” “output,” and “prompt” . Most of the data is generated through ChatGpt 3.5 .Dataset is particularly valuable for LLM Model. Here’s a detailed description of each column:
The “instruction” column contains contextual information or directives that guide the model’s behavior. It provides high-level guidance for generating the desired output based on the provided input and prompt. Instructions can be explicit or implicit, and they help the model understand the task it needs to perform.
Example: “Create a function in Python that takes two parameters and prints out the larger of them.”
The “input” column contains the initial text or context that the model uses to generate the desired output. This input can vary based on the task. It might include a paragraph of text, a question, a partial sentence, or any relevant textual content that sets the context for the model to work with.
Example : “parameter_1 = 7 parameter_2 = 9”
The “output” column contains the expected or desired output that the model should generate in response to the given input and instruction. For supervised learning tasks, this column serves as the target output that the model aims to replicate. In tasks like text completion, translation, or summarization, the output represents the intended result.
Example: “def printMax(parameter_1, parameter_2): if parameter_1 > parameter_2: print(parameter_1) else: print(parameter_2)”
The “prompt” column provides additional cues or context that guide the model’s generation process. A prompt can be a question, a statement, or any text that helps the model understand the specific context or tone required for generating the output accurately.
Example : “Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Create a function in Python that takes two parameters and prints out the larger of them. ### Input: parameter_1 = 7 parameter_2 = 9 ### Output: def printMax(parameter_1, parameter_2): if parameter_1 > parameter_2: print(parameter_1) else: print(parameter_2)”
Choosing a Foundation Model and a Fine Tuning Method
The Foundation model which is selected for this use case is Meta Llama-2-7b.
Different Fine Tuning Method Tried:
1. Low-Rank Adaptation of Large Language Models (LoRa):
Latency and Inference Phase: In the context of large language models, latency during the inference phase can be a significant concern, particularly for real-time applications. LoRa seeks to mitigate this issue without introducing additional layers, which could further exacerbate the latency problem.
Parameter Adjustment: Instead of adding new layers, LoRa works by making adjustments to the existing model parameters. This avoids the potential increase in computation associated with introducing new layers to the model architecture.
Change Tracking: LoRa’s approach involves training and storing the changes made to the model’s weights. This means that rather than retraining the entire model, only the necessary modifications are learned and tracked.
Freezing Pre-trained Weights: During LoRa’s adaptation process, the weights of the pre-trained model are frozen. This means that the knowledge learned by the model during the pre-training phase is preserved, and only specific updates are applied to achieve the desired improvements.
New Weights Matrix: LoRa creates a new weights matrix that captures the changes required for adaptation. This matrix is designed to reflect the necessary modifications without disrupting the existing pre-trained weights.
Low-Rank Decomposition: The new weights matrix obtained from LoRa is decomposed into two low-rank matrices. Low-rank matrices can efficiently capture relationships and patterns in the data while reducing the overall computational burden
Quantization: Quantization involves reducing the precision of numerical values in order to save memory and computational resources. In QLoRa, quantization is applied to the LoRa technique. Specifically, it employs a 4-bit normal quantization (nf4) method. This approach reduces the bit width of weights while considering the distribution characteristics of the weights.
Double Quantization: QLoRa employs double quantization to further decrease the memory footprint. Double quantization could involve applying quantization techniques sequentially or to specific parts of the model to achieve additional memory savings.
Optimization of NVIDIA Unified Memory: The optimization of NVIDIA unified memory suggests that QLoRa leverages the memory management capabilities provided by NVIDIA GPUs. Unified memory allows for seamless memory sharing between the CPU and GPU, which can be particularly advantageous for memory-intensive tasks like training large language models.
Memory Efficiency and Lighter Training: The primary objective of QLoRa is to achieve memory efficiency in training large language models. By employing quantization, memory optimization techniques, and leveraging unified memory capabilities, QLoRa aims to make the training process “lighter” in terms of memory consumption and associated costs.
I Choose QLoRa:
Memory Efficiency is Vital: If memory efficiency is a top priority and you want to achieve lighter and less expensive training, QLoRa’s quantization and memory optimization techniques could be beneficial.
Cost Reduction: If you’re looking to reduce hardware costs associated with memory-intensive training, QLoRa’s focus on memory optimization might align with your goals.
Trade-off with Precision: Consider QLoRa if you’re willing to trade off a bit of precision in model weights for substantial memory savings, and your application’s performance isn’t highly sensitive to such a trade-off.
Loading Pretrained Model(meta llama2-7b)
Quantize the Pre-trained Model to 4 Bits: Quantization is a technique used to reduce the precision of numerical values in a model, thereby reducing memory usage and potentially improving performance. In this case, you’re suggesting quantizing the pre-trained model to use only 4 bits per parameter value instead of the typical 32 bits (single-precision floating-point). This can make the model more memory-efficient but might lead to a loss of precision.
Freeze the Quantized Model: Freezing the model means that the weights and parameters of the quantized model are not updated during training. This is done to retain the knowledge learned by the pre-trained model and ensure that it’s not altered during subsequent training steps.
Attach Small, Trainable Adapter Layers (LoRA): Adapter layers are additional layers added to a pre-trained model to enable it to learn new tasks without completely retraining it. LoRA stands for “Learned Relevance Adaptation,” and it’s a technique that allows the model to learn new tasks with minimal additional parameters.
Finetune Only the Adapter Layers: Fine-tuning involves training a model further on a specific task using a smaller learning rate. In this case, you’re suggesting that only the newly attached adapter layers should be trained on the specific task, while the quantized pre-trained model remains frozen.
Using Frozen Quantized Model for Context: The frozen quantized model serves as a fixed context for the training of the adapter layers. This context helps the adapter layers learn task-specific information while benefiting from the knowledge captured by the pre-trained model.
bitsnbytes_config = BitsAndBytesConfig(
Now Load the model:
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bitsnbytes_config, use_cache = False, device_map=device_map)
We used QLoRa parameters like:
a)LoRA attention dimension
b)Alpha parameter for LoRA scaling
c)Dropout probability for LoRA layers
In addition to the aforementioned configuration, we need to provide the Trainer (SFTTrainer) with additional hyperparameters required for the training process, and subsequently initiate the training procedure.
Conducting a human evaluation on the test dataset involves assessing the correspondence between queries and responses contained within it. Enhanced accuracy can be achieved by adjusting hyperparameters and employing a greater number of bits in the process.
In summary, the fine-tuning of the LLAMA-2-7B model to generate Python code has yielded impressive results. Through this process, we’ve harnessed the power of advanced language models to enhance the precision and sophistication of code generation. This achievement underscores the remarkable potential of combining machine learning with programming, pointing toward a future where AI-driven code synthesis could significantly expedite software development. As we continue refining models like LLAMA-2, we’re taking bold strides into an era where innovative technologies reshape the landscape of coding as we know it.