Use Code Llama as Drop-In Replacement for Copilot Chat
Hi HN, Code Llama was released, but we noticed a ton of questions in the main thread about how/where to use it — not just from an API or the terminal, but in your own codebase as a drop-in replacement for Copilot Chat. Without this, developers don't get much utility from the model. This concern is also important because benchmarks like HumanEval don't perfectly reflect the quality of responses. There's likely to be a flurry of improvements to coding models in the coming months, and rather than relying on the benchmarks to evaluate them, the community will get better feedback from people…
In plain words
Continue is an IDE extension that integrates Code Llama as a drop-in replacement for Copilot Chat, enabling developers to use the open-source coding model directly within their codebase. It's designed for developers who want to evaluate and use Code Llama for real-world coding tasks beyond benchmarks. The tool addresses the practical gap between testing models in isolation and applying them to everyday development workflows, allowing users to provide direct feedback on the model's real-world capabilities and performance.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, Code Llama was released, but we noticed a ton of questions in the main thread about how/where to use it — not just from an API or the terminal, but in your own codebase as a drop-in replacement for Copilot Chat. Without this, developers don't get much utility from the model. This concern is also important because benchmarks like HumanEval don't perfectly reflect the quality of responses. There's likely to be a flurry of improvements to coding models in the coming months, and rather than relying on the benchmarks to evaluate them, the community will get better feedback from people actually using the models. This means real usage in real, everyday workflows. We've worked to make this possible with Continue (https://github.com/continuedev/continue) and want to hear what you find to be the real capabilities of Code Llama. Is it on-par with GPT-4, does it require fine-tuning, or does it excel at certain tasks? If you’d like to try Code Llama with Continue, it only takes a few steps to set up (https://continue.dev/docs/walkthroughs/codellama), either locally with Ollama, or through TogetherAI or Replicate's APIs.
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