codemancer
codealpaca
| codemancer | codealpaca | |
|---|---|---|
| 3 | 20 | |
| 65 | 1,381 | |
| - | - | |
| 4.5 | 4.4 | |
| 20 days ago | about 1 year ago | |
| TypeScript | Python | |
| - | Apache License 2.0 |
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codemancer
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Ask HN: Those with success using GPT-4 for programming – what are you doing?
I'm using it for single file programs and building command line utilities, it excels at building apps from scratch, but struggles to follow code it didn't write.
I've had so much success that I built my own command line utility (https://github.com/0xmmo/codemancer) to use in the VSCode terminal and my side projects are now ~70% written by LLM.
- codemancer: Coding superpowers with GPT-4 in your command line
- Codemancer: Code with GPT-4 from your command line
codealpaca
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Just put together a programming performance ranking for popular LLaMAs using the HumanEval+ Benchmark!
CodeAlpaca 7B
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OpenAI isn’t doing enough to make ChatGPT’s limitations clear
This is great!
Addressing the model limitations a bit: in the demonstration data that is provided to the base model, we should prevent computed or "looked up" answers.
I've seen some of the demonstration data that people are using to train instruction-tuned models and are being taught to respond by making up answers to solutions it shouldn't try to compute. Btw, the output is wrong.
{ "instruction": "What would be the output of the following JavaScript snippet?", "input": "let area = 6 * 5;\nlet radius = area / 3.14;", "output": "The output of the JavaScript snippet is the radius, which is 1.91." }, [1]
The UI note for now would get us very far but by filtering out demonstrations that retrieve or compute information should be filtered out.
Symbol tuning [2] is addressing the quality of demonstrations but we can take it further by removing retrievals and computations altogether.
Bonus: we can demonstrate how to make it respond so that the user/agent be informed of how to compute or retrieve.
1: https://github.com/sahil280114/codealpaca/commit/0d265112c70...
2: https://arxiv.org/abs/2305.08298
- How to Finetune GPT Like Large Language Models on a Custom Dataset
- Ask HN: Those with success using GPT-4 for programming – what are you doing?
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Is there a colab or guide for fine tuning a 13b model for instruction following?
I found guides like this: https://github.com/sahil280114/codealpaca
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Can LLMs do static code analysis?
Try, https://github.com/sahil280114/codealpaca, or we’re you trying to stick with more generalist models?
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LoRA in LLaMAc++? Converting to 4bit? How to use models that are split into multiple .bin ?
Oh, I see. That makes sense. I'm also sleep deprived over here so my reading comprehension is a bit low ;|. Well in that case check out this link: https://github.com/sahil280114/codealpaca
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Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models
Sorry for the late reply, as I said Flan-UL2 (or Flan-T5 if you want lighter models) fine-tuned against a dataset like CodeAlpaca's[0] is probably the best solution if it's intended for commercial use (otherwise LLaMa should perform better).
[0]: https://github.com/sahil280114/codealpaca
- CodeAlpaca – Instruction following code generation model
What are some alternatives?
llemmings - Llemmings is a game being written by LLMs. Only. Humans copy and paste code.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
llm-code - An OpenAI LLM based CLI coding assistant.
alpaca-electron - The simplest way to run Alpaca (and other LLaMA-based local LLMs) on your own computer
llm-humaneval-benchmarks
awesome-ai-coding - Awesome AI Coding
openplayground-api - A reverse engineered Python API wrapper for OpenPlayground (nat.dev)
supercharger - Supercharge Open-Source AI Models
flan-alpaca - This repository contains code for extending the Stanford Alpaca synthetic instruction tuning to existing instruction-tuned models such as Flan-T5.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.