self-instruct
llama.cpp
| self-instruct | llama.cpp | |
|---|---|---|
| 3 | 780 | |
| 3,666 | 57,984 | |
| - | - | |
| 2.3 | 10.0 | |
| about 1 year ago | 6 days ago | |
| Python | C++ | |
| Apache License 2.0 | MIT License |
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self-instruct
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The next generation of AI for developers and Google Workspace
When they say Augment your dataset with synthetic data on https://developers.googleblog.com/2023/03/announcing-palm-ap... do they mean something like this https://github.com/yizhongw/self-instruct ?
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Alpaca- An Instruct Tuned Llama 7B. Responses on par with txt-DaVinci-3. Demo up
It says
> We train the Alpaca model on 52K instruction-following demonstrations generated in the style of self-instruct using text-davinci-003
Which leads to self-instruct https://github.com/yizhongw/self-instruct
From a glimpse they used a LM to classify instructions & train the model which IMHO very similar to RLHF
- Alpaca: A Strong Open-Source Instruction-Following Model
llama.cpp
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IBM Granite: A Family of Open Foundation Models for Code Intelligence
if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp
the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...
And you can search for any GGUF on huggingface
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Ask HN: Affordable hardware for running local large language models?
Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182
- Xmake: A modern C/C++ build tool
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Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
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Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3
What are some alternatives?
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
CodeCapypara - [Moved to: https://github.com/FSoft-AI4Code/CodeCapybara]
gpt4all - gpt4all: run open-source LLMs anywhere
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
LongForm - Reverse Instructions to generate instruction tuning data with corpus examples
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
ggml - Tensor library for machine learning
replika-research - Replika.ai Research Papers, Posters, Slides & Datasets
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM