codealpaca
flan-alpaca
codealpaca | flan-alpaca | |
---|---|---|
20 | 5 | |
1,373 | 336 | |
- | -0.3% | |
4.4 | 5.7 | |
12 months ago | 10 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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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
flan-alpaca
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Is it feasible to develop multiple specialised language models that are small in size and expertise-specific, which can be merged to achieve comparable results to those obtained from a single large language model?
If you have enough task or domain specific training data, the model size becomes less important. For example, you can take an instruction tuned smaller model like FlanT5 and fine tune for your specific case: https://github.com/declare-lab/flan-alpaca
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Best Instruct-Trained Alternative to Alpaca/Vicuna?
Hi, you can try Flan-Alpaca here which does not have such restrictions: https://github.com/declare-lab/flan-alpaca
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Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models
I've been following open source LLMs for a while and at first glance this doesn't seem too powerful compared to other open models, Flan-Alpaca[0] is licensed under Apache 2.0, and it seems to perform much better. Although I'm not sure about the legalities about that licensing, since it's basically Flan-T5 fine-tuned using the Alpaca dataset (which is under a Non-Commercial license).
Nonetheless, it's exciting to see all these open models popping up, and I hope that a LLM equivalent to Stable Diffusion comes sooner than later.
[0]: https://github.com/declare-lab/flan-alpaca
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[D] What is the best open source chatbot AI to do transfer learning on?
Someone's already taking care of that - Flan-Alpaca
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[P] ChatLLaMA - A ChatGPT style chatbot for Facebook's LLaMA
I think this might be exactly what you're looking for https://github.com/declare-lab/flan-alpaca
What are some alternatives?
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
agents - An Open-source Framework for Autonomous Language Agents
llm-code - An OpenAI LLM based CLI coding assistant.
llama - Inference code for Llama models
llm-humaneval-benchmarks
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
awesome-ai-coding - Awesome AI Coding
stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
openplayground-api - A reverse engineered Python API wrapper for OpenPlayground (nat.dev)
supercharger - Supercharge Open-Source AI Models