Ne

New Launch OrionStar-Yi-34B-Chat beats Llama2-70B and GPT-3.5-turbo

Hacker News

New Launch OrionStar-Yi-34B-Chat beats Llama2-70B and GPT-3.5-turbo

OrionStar-Yi-34B-Chat from OrionStarAI is based on the open-source Yi-34B model, fine-tuned on a high-quality corpus of over 15 million sentences. OrionStar-Yi-34B-Chat aims to provide an excellent interactive experience for users in the large model community. The Yi series models, open-sourced by the 01-ai team, have shown impressive performance on various benchmarks in Chinese, English, and general domains. OrionStar-Yi-34B-Chat further explores the potential of Yi-34B. Through extensive fine-tuning on a large and high-quality corpus, OrionStar-Yi-34B-Chat performs exceptionally well on evaluation data. We strive to make it an outstanding open-source alternative in the ChatGPT domain!

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
94%94% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, llama, ide · Missing: https docs, excited, just released
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
14%14% 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
13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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