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A sensible introduction to LLM fine-tuning
With the discharge of LLaMA v1, we noticed a Cambrian explosion of fine-tuned fashions, together with Alpaca, Vicuna, and WizardLM, amongst others. This development inspired totally different companies to launch their very own base fashions with licenses appropriate for business use, similar to OpenLLaMA, Falcon, XGen, and many others. The discharge of Llama 2 now combines the perfect components from each side: it affords a extremely environment friendly base mannequin together with a extra permissive license.
In the course of the first half of 2023, the software program panorama was considerably formed by the widespread use of APIs (like OpenAI API) to create infrastructures based mostly on Massive Language Fashions (LLMs). Libraries similar to LangChain and LlamaIndex performed a important position on this development. Shifting into the latter half of the yr, the method of fine-tuning these fashions is ready to turn into a regular process within the LLMOps workflow. This development is pushed by numerous elements: the potential for value financial savings, the power to course of confidential knowledge, and even the potential to develop fashions that exceed the efficiency of outstanding fashions like ChatGPT and GPT-4 in sure particular duties.
On this article, we’ll see why fine-tuning works and the right way to implement it in a Google Colab pocket book to create your individual Llama 2 mannequin. As regular, the code is offered on Colab and GitHub.
LLMs are pretrained on an intensive corpus of textual content. Within the case of Llama 2, we all know little or no in regards to the composition of the coaching set, in addition to its size of two trillion tokens. As compared, BERT (2018) was “solely” skilled on the BookCorpus (800M phrases) and English Wikipedia (2,500M phrases). From expertise, this can be a very expensive and lengthy course of with quite a lot of {hardware} points. If you wish to know extra about it, I like to recommend studying Meta’s logbook in regards to the pretraining of the OPT-175B mannequin.
When the pretraining is full, auto-regressive fashions like Llama 2 can predict the subsequent token in a sequence. Nonetheless, this doesn’t make them…
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