Thursday, August 31, 2023
Wednesday, August 30, 2023
Tuesday, August 29, 2023
New best story on Hacker News: Ask HN: Why did Python win?
Ask HN: Why did Python win?
336 by MatthiasPortzel | 587 comments on Hacker News.
I started programming in ~2013 in JavaScript. I’ve since learned and tried a handful of languages, including Python, but JavaScript was always my favorite. Just within the last year I learned Ruby, and I was blown away by how fun and easy to use it is. At the present time, I’m starting all my new projects in Ruby. My impression is that in the ‘00s, Python and Ruby were both relatively new, dynamically typed, “English-like” languages. And for a while these languages had similar popularity. Now Ruby is still very much alive; there are plenty of Rails jobs available and exciting things happening with Ruby itself. But Python has become a titan in the last ten years. It has continued to grow exponentially and Ruby has not. I can guess as to why (Python’s math libraries, numpy and pandas make it appealing to academics; Python is simpler and possibly easier to learn; Rails was so popular that it was synonymous with Ruby) but I wasn’t paying attention at that time. So I’m interested in hearing from some of the older programmers about why Ruby has stalled out and Python has become possibly the most popular programming language (when, in my opinion, Ruby is the better language).
336 by MatthiasPortzel | 587 comments on Hacker News.
I started programming in ~2013 in JavaScript. I’ve since learned and tried a handful of languages, including Python, but JavaScript was always my favorite. Just within the last year I learned Ruby, and I was blown away by how fun and easy to use it is. At the present time, I’m starting all my new projects in Ruby. My impression is that in the ‘00s, Python and Ruby were both relatively new, dynamically typed, “English-like” languages. And for a while these languages had similar popularity. Now Ruby is still very much alive; there are plenty of Rails jobs available and exciting things happening with Ruby itself. But Python has become a titan in the last ten years. It has continued to grow exponentially and Ruby has not. I can guess as to why (Python’s math libraries, numpy and pandas make it appealing to academics; Python is simpler and possibly easier to learn; Rails was so popular that it was synonymous with Ruby) but I wasn’t paying attention at that time. So I’m interested in hearing from some of the older programmers about why Ruby has stalled out and Python has become possibly the most popular programming language (when, in my opinion, Ruby is the better language).
Monday, August 28, 2023
Sunday, August 27, 2023
Saturday, August 26, 2023
New best story on Hacker News: Show HN: Open-source obsidian.md sync server
Show HN: Open-source obsidian.md sync server
383 by acheong08 | 137 comments on Hacker News.
https://ift.tt/9zJdbmu Hello HN, I'm a recent high school graduate and can't afford $8 per month for the official sync service, so I tried my hand at replicating the server. It's still missing a few features, such as file recovery and history, but the basic sync is working. To the creators of Obsidian.md: I'm probably violating the TOS, and I'm sorry. I'll take down the repository if asked. It's not ready for production and is highly inefficient; Not competition, so I hope you'll be lenient.
383 by acheong08 | 137 comments on Hacker News.
https://ift.tt/9zJdbmu Hello HN, I'm a recent high school graduate and can't afford $8 per month for the official sync service, so I tried my hand at replicating the server. It's still missing a few features, such as file recovery and history, but the basic sync is working. To the creators of Obsidian.md: I'm probably violating the TOS, and I'm sorry. I'll take down the repository if asked. It's not ready for production and is highly inefficient; Not competition, so I hope you'll be lenient.
Friday, August 25, 2023
New best story on Hacker News: Beating GPT-4 on HumanEval with a fine-tuned CodeLlama-34B
Beating GPT-4 on HumanEval with a fine-tuned CodeLlama-34B
418 by rushingcreek | 144 comments on Hacker News.
Hi HN, We have fine-tuned CodeLlama-34B and CodeLlama-34B-Python on an internal Phind dataset that achieved 67.6% and 69.5% pass@1 on HumanEval, respectively. GPT-4 achieved 67%. To ensure result validity, we applied OpenAI's decontamination methodology to our dataset. The CodeLlama models released yesterday demonstrate impressive performance on HumanEval. - CodeLlama-34B achieved 48.8% pass@1 on HumanEval - CodeLlama-34B-Python achieved 53.7% pass@1 on HumanEval We have fine-tuned both models on a proprietary dataset of ~80k high-quality programming problems and solutions. Instead of code completion examples, this dataset features instruction-answer pairs, setting it apart structurally from HumanEval. We trained the Phind models over two epochs, for a total of ~160k examples. LoRA was not used — both models underwent a native fine-tuning. We employed DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in three hours using 32 A100-80GB GPUs, with a sequence length of 4096 tokens. Furthermore, we applied OpenAI's decontamination methodology to our dataset to ensure valid results, and found no contaminated examples. The methodology is: - For each evaluation example, we randomly sampled three substrings of 50 characters or used the entire example if it was fewer than 50 characters. - A match was identified if any sampled substring was a substring of the processed training example. For further insights on the decontamination methodology, please refer to Appendix C of OpenAI's technical report. Presented below are the pass@1 scores we achieved with our fine-tuned models: - Phind-CodeLlama-34B-v1 achieved 67.6% pass@1 on HumanEval - Phind-CodeLlama-34B-Python-v1 achieved 69.5% pass@1 on HumanEval Note on GPT-4 According to the official technical report in March, OpenAI reported a pass@1 score of 67% for GPT-4's performance on HumanEval. Since then, there have been claims reporting higher scores. However, it's essential to note that there hasn't been any concrete evidence pointing towards an enhancement in the model's coding abilities since then. It's also crucial to highlight that these elevated figures lack the rigorous contamination analysis that the official statistic underwent, making them less of a reliable comparison. As a result, we consider 67% as the pass@1 score for GPT-4. Download We are releasing both models on Huggingface for verifiability and to bolster the open-source community. We welcome independent verification of results. Phind-CodeLlama-34B-v1: https://ift.tt/u9eMyab Phind-CodeLlama-34B-Python-v1: https://ift.tt/DYXAbSZ We'd love to hear your thoughts! Best, The Phind Team
418 by rushingcreek | 144 comments on Hacker News.
Hi HN, We have fine-tuned CodeLlama-34B and CodeLlama-34B-Python on an internal Phind dataset that achieved 67.6% and 69.5% pass@1 on HumanEval, respectively. GPT-4 achieved 67%. To ensure result validity, we applied OpenAI's decontamination methodology to our dataset. The CodeLlama models released yesterday demonstrate impressive performance on HumanEval. - CodeLlama-34B achieved 48.8% pass@1 on HumanEval - CodeLlama-34B-Python achieved 53.7% pass@1 on HumanEval We have fine-tuned both models on a proprietary dataset of ~80k high-quality programming problems and solutions. Instead of code completion examples, this dataset features instruction-answer pairs, setting it apart structurally from HumanEval. We trained the Phind models over two epochs, for a total of ~160k examples. LoRA was not used — both models underwent a native fine-tuning. We employed DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in three hours using 32 A100-80GB GPUs, with a sequence length of 4096 tokens. Furthermore, we applied OpenAI's decontamination methodology to our dataset to ensure valid results, and found no contaminated examples. The methodology is: - For each evaluation example, we randomly sampled three substrings of 50 characters or used the entire example if it was fewer than 50 characters. - A match was identified if any sampled substring was a substring of the processed training example. For further insights on the decontamination methodology, please refer to Appendix C of OpenAI's technical report. Presented below are the pass@1 scores we achieved with our fine-tuned models: - Phind-CodeLlama-34B-v1 achieved 67.6% pass@1 on HumanEval - Phind-CodeLlama-34B-Python-v1 achieved 69.5% pass@1 on HumanEval Note on GPT-4 According to the official technical report in March, OpenAI reported a pass@1 score of 67% for GPT-4's performance on HumanEval. Since then, there have been claims reporting higher scores. However, it's essential to note that there hasn't been any concrete evidence pointing towards an enhancement in the model's coding abilities since then. It's also crucial to highlight that these elevated figures lack the rigorous contamination analysis that the official statistic underwent, making them less of a reliable comparison. As a result, we consider 67% as the pass@1 score for GPT-4. Download We are releasing both models on Huggingface for verifiability and to bolster the open-source community. We welcome independent verification of results. Phind-CodeLlama-34B-v1: https://ift.tt/u9eMyab Phind-CodeLlama-34B-Python-v1: https://ift.tt/DYXAbSZ We'd love to hear your thoughts! Best, The Phind Team
