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I reproduced Code Llama fill-in-the-middle code completion training

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I reproduced Code Llama fill-in-the-middle code completion training

As part of my master's thesis on optimizing LLMs for code completion in OpenAPI format, I implemented a script for fine-tuning Code Llama for Copilot-style code completion. Surprisingly, I was not able to find a similar implementation before since most works focus on instruction fine-tuning for chat-like interaction. Related publication: https://arxiv.org/abs/2405.15729 Code Llama fill-in-the-middle fine-tuning: https://github.com/BohdanPetryshyn/code-llama-fim-fine-tunin... OpenAPI completion benchmark: https://github.com/BohdanPetryshyn/openapi-completion-benchm...

Visit productView on Hacker NewsJun 18, 2024
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, 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: llama, io · Missing: https docs, excited, just released
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
20%20% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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