llemmings
codealpaca
llemmings | codealpaca | |
---|---|---|
1 | 20 | |
19 | 1,381 | |
- | - | |
9.0 | 4.4 | |
11 months ago | about 1 year ago | |
JavaScript | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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llemmings
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Ask HN: Those with success using GPT-4 for programming – what are you doing?
I did this experiment (a game) to see what's up and what's down around all this: https://github.com/romland/llemmings.
While there is some GPT4 in there, it's mostly ChatGPT and a small handful of LLaMA solutions.
That project is a contrived scenario and not realistic, but I wanted to experiment with _exactly_ what you are talking about.
Very often I could have done things a lot faster myself, but there is one aspect that was actually helpful, and I did not foresee it. When inspiration gets a bit low and you're not in the "zone"; throwing something into an LLM will very often give me a push to keep at it. Even if what is coming up is mostly grunt work.
The other day I threw together a script to show the commits in a reverse order and filter out (most of) the human commits (glue) over at https://llemmings.com/
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?
codemancer - AI coding assistant in your command line.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
alpaca-electron - The simplest way to run Alpaca (and other LLaMA-based local LLMs) on your own computer
llm-code - An OpenAI LLM based CLI coding assistant.
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.
stacksort - Sorts an array by downloading snippets from StackOverflow. Inspired by http://xkcd.com/1185/. I'm sorry.