Fine-Tuning a Llama-2 7B Model for Python Code Generation
<p>About 2 weeks ago, the world of generative AI was shocked by the company Meta's release of the new Llama-2 AI model. Its predecessor, Llama-1, was a breaking point in the LLM industry, as with the release of its weights along with new finetuning techniques, there was a massive creation of open-source LLM models that led to the emergence of high-performance models such as Vicuna, Koala, …</p>
<p>In this article, we will briefly discuss some of this model's relevant points but will focus on showing how we can quickly train the model for a specific task using libraries and tools standard in this world. We will not make an exhaustive analysis of the new model, there are already numerous articles published on the subject.</p>
<h1>New Llama-2 model</h1>
<p>In mid-July, Meta released its new family of pre-trained and finetuned models called <strong>Llama-2,</strong> with an open source and commercial character to facilitate its use and expansion. The base model was released with a chat version and sizes 7B, 13B, and 70B. Together with the models, the corresponding papers were published describing their characteristics and relevant points of the learning process, which provide very interesting information on the subject.</p>
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<p>An updated version of Llama 1, trained on a new mix of publicly available data. The pretraining corpus size was increased by 40%, the model’s context length was doubled, and grouped-query attention was adopted. Variants with 7B, 13B, and 70B parameters are released, along with 34B variants reported in the paper but not released.[1]</p>
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<p>For pre-training, <strong>40% more tokens were used</strong>, reaching 2T, the context length was doubled and the grouped-query attention (GQA) technique was applied to speed up inference on the heavier 70B model. On the standard transformer architecture, RMSNorm normalization, SwiGLU activation, and rotatory positional embedding are used, the context length reaches 4096 tokens, and an Adam optimizer is applied with a cosine learning rate schedule, a weight decay of 0.1 and gradient clipping.</p>
<p><a href="https://pub.towardsai.net/fine-tuning-a-llama-2-7b-model-for-python-code-generation-865453afdf73"><strong>Website</strong></a></p>