![]() ![]() You can also check this chat-based demo and duplicate it for your use – it's self-contained, so you can examine the source code and adapt it as you wish! Make sure to specify the Code Llama model. If you want to try out the bigger instruct-tuned 34B model, it is now available on HuggingChat! You can try it out here: hf.co/chat. Under the hood, this playground uses Hugging Face's Text Generation Inference, the same technology that powers HuggingChat, and we'll share more in the following sections. You can easily try the Code Llama Model (13 billion parameters!) in this Space or in the playground embedded below: Until transformers 4.33 is released, please install it from the main branch. The self-instruct dataset was created by using Llama 2 to create interview programming questions and then using Code Llama to generate unit tests and solutions, which are later evaluated by executing the tests.Ĭode Llama is available in the Hugging Face ecosystem, starting with transformers version 4.33. Unfortunately, there is not more information about the dataset.įor the instruction model, they used two datasets: the instruction tuning dataset collected for Llama 2 Chat and a self-instruct dataset. The dataset also contains some natural language datasets, such as discussions about code and code snippets. In the case of Code Llama, the frequency domain scaling is done with a slack: the fine-tuning length is a fraction of the scaled pretrained length, giving the model powerful extrapolation capabilities.Īll models were initially trained with 500 billion tokens on a near-deduplicated dataset of publicly available code. The community found that Llama’s position embeddings can be interpolated linearly or in the frequency domain, which eases the transition to a larger context window through fine-tuning. Increasing Llama 2’s 4k context window to Code Llama’s 16k (that can extrapolate up to 100k) was possible due to recent developments in RoPE scaling. ![]() In addition, the three model variants had additional long-context fine-tuning, allowing them to manage a context window of up to 100,000 tokens. The 7B and 13B base and instruct variants support infilling based on surrounding content, making them ideal for use as code assistants.Ĭode Llama was trained on a 16k context window. The models show state-of-the-art performance in Python, C++, Java, PHP, C#, TypeScript, and Bash. Meta fine-tuned those base models for two different flavors: a Python specialist (100 billion additional tokens) and an instruction fine-tuned version, which can understand natural language instructions. The base models are initialized from Llama 2 and then trained on 500 billion tokens of code data. The Code Llama release introduces a family of models of 7, 13, and 34 billion parameters.
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