Home Assistant blocked from integrating with Garage Door opener API
599 by eamonnsullivan | 328 comments on Hacker News.
599 by eamonnsullivan | 328 comments on Hacker News.
Wednesday, November 8, 2023
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New best story on Hacker News: Improving deep sleep may prevent dementia, study finds
Improving deep sleep may prevent dementia, study finds
457 by clouddrover | 423 comments on Hacker News.
457 by clouddrover | 423 comments on Hacker News.
Wednesday, November 1, 2023
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New best story on Hacker News: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
471 by rushingcreek | 211 comments on Hacker News.
Hi HN, We’re excited to announce that Phind now defaults to our own model that matches and exceeds GPT-4’s coding abilities while running 5x faster. You can now get high quality answers for technical questions in 10 seconds instead of 50. The current 7th-generation Phind Model is built on top of our open-source CodeLlama-34B fine-tunes that were the first models to beat GPT-4’s score on HumanEval and are still the best open source coding models overall by a wide margin: https://ift.tt/D2C79KT... . This new model has been fine-tuned on an additional 70B+ tokens of high quality code and reasoning problems and exhibits a HumanEval score of 74.7%. However, we’ve found that HumanEval is a poor indicator of real-world helpfulness. After deploying previous iterations of the Phind Model on our service, we’ve collected detailed feedback and noticed that our model matches or exceeds GPT-4’s helpfulness most of the time on real-world questions. Many in our Discord community have begun using Phind exclusively with the Phind Model despite also having unlimited access to GPT-4. One of the Phind Model’s key advantages is that it's very fast. We’ve been able to achieve a 5x speedup over GPT-4 by running our model on H100s using the new TensorRT-LLM library from NVIDIA. We can achieve up to 100 tokens per second single-stream while GPT-4 runs around 20 tokens per second at best. Another key advantage of the Phind Model is context – it supports up to 16k tokens. We currently allow inputs of up to 12k tokens on the website and reserve the remaining 4k for web results. There are still some rough edges with the Phind Model and we’ll continue improving it constantly. One area where it still suffers is consistency — on certain challenging questions where it is capable of getting the right answer, the Phind Model might take more generations to get to the right answer than GPT-4. We’d love to hear your feedback. Cheers, The Phind Team
471 by rushingcreek | 211 comments on Hacker News.
Hi HN, We’re excited to announce that Phind now defaults to our own model that matches and exceeds GPT-4’s coding abilities while running 5x faster. You can now get high quality answers for technical questions in 10 seconds instead of 50. The current 7th-generation Phind Model is built on top of our open-source CodeLlama-34B fine-tunes that were the first models to beat GPT-4’s score on HumanEval and are still the best open source coding models overall by a wide margin: https://ift.tt/D2C79KT... . This new model has been fine-tuned on an additional 70B+ tokens of high quality code and reasoning problems and exhibits a HumanEval score of 74.7%. However, we’ve found that HumanEval is a poor indicator of real-world helpfulness. After deploying previous iterations of the Phind Model on our service, we’ve collected detailed feedback and noticed that our model matches or exceeds GPT-4’s helpfulness most of the time on real-world questions. Many in our Discord community have begun using Phind exclusively with the Phind Model despite also having unlimited access to GPT-4. One of the Phind Model’s key advantages is that it's very fast. We’ve been able to achieve a 5x speedup over GPT-4 by running our model on H100s using the new TensorRT-LLM library from NVIDIA. We can achieve up to 100 tokens per second single-stream while GPT-4 runs around 20 tokens per second at best. Another key advantage of the Phind Model is context – it supports up to 16k tokens. We currently allow inputs of up to 12k tokens on the website and reserve the remaining 4k for web results. There are still some rough edges with the Phind Model and we’ll continue improving it constantly. One area where it still suffers is consistency — on certain challenging questions where it is capable of getting the right answer, the Phind Model might take more generations to get to the right answer than GPT-4. We’d love to hear your feedback. Cheers, The Phind Team
