Use Code Llama as Drop-In Replacement for Copilot Chat

Hacker News

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.

Visit product View on Hacker NewsAug 24, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, tasks · Missing: mac, agents, macos

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: lua, llama, io · Missing: https docs, excited, just released

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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