serge
stanford_alpaca
serge | stanford_alpaca | |
---|---|---|
40 | 108 | |
5,543 | 28,816 | |
0.7% | 0.7% | |
9.8 | 2.0 | |
3 days ago | about 2 months ago | |
Svelte | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
serge
- Show HN: I made an app to use local AI as daily driver
- chatgpt alternative
-
Show HN: LlamaGPT – Self-hosted, offline, private AI chatbot, powered by Llama 2
Very cool, this looks like a combination of chatbot-ui and llama-cpp-python? A similar project I've been using is https://github.com/serge-chat/serge. Nous-Hermes-Llama2-13b is my daily driver and scores high on coding evaluations (https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...).
-
LeCun: Qualcomm working with Meta to run Llama-2 on mobile devices
You might be pleased to hear that nothing really stops you from doing this today. If you ran Serge[0] on a Mac with Tailscale, you could hack together a decently-accelerated Llama chatbot.
[0] https://github.com/serge-chat/serge
-
Chatbot frontend library in Svelte?
Cannot help you with libraries specifically but both Serge and ChatUI are built using SvelteKit, so the code might be of some use to you.
- We’re back and…
-
Best way to use AMD CPU and GPU
Serge made it really easy for me to get started, but it all CPU-based.
-
Need Help
All that said this project probably solves your problem: https://github.com/serge-chat/serge
- Are you selfhosting a ChatGPT alternative?
-
What the hell??
You can play a little bit with more straightforward local models (the simplest to setup is https://github.com/nsarrazin/serge ), to see that any LLM is basically a party trick.
stanford_alpaca
-
How Open is Generative AI? Part 2
Alpaca is an instruction-oriented LLM derived from LLaMA, enhanced by Stanford researchers with a dataset of 52,000 examples of following instructions, sourced from OpenAI’s InstructGPT through the self-instruct method. The extensive self-instruct dataset, details of data generation, and the model refinement code were publicly disclosed. This model complies with the licensing requirements of its base model. Due to the utilization of InstructGPT for data generation, it also adheres to OpenAI’s usage terms, which prohibit the creation of models competing with OpenAI. This illustrates how dataset restrictions can indirectly affect the resulting fine-tuned model.
- Ask HN: AI/ML papers to catch up with current state of AI?
- OpenAI board in discussions with Sam Altman to return as CEO
- Are there any AI like ChatGPT without content restrictions?
-
Fine-tuning LLMs with LoRA: A Gentle Introduction
In this article, we're going to experiment with LoRA and fine-tune Llama Alpaca using commercial hardware.
-
Creating a new Finetuned model
Most papers I did read showed at least a thousand, even 10000 at several cases, so I assumed that to be the trend in the case of Low rank adapter(PEFT) training.(source: [2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs (arxiv.org) , Stanford CRFM (Alpaca) and the minimum being openchat/openchat · Hugging Face ; There are a lot more examples)
-
Shock tick up for wage growth to 7.3% in blow for Bank of England
I'm not talking about OpenAI ChatGPT I'm talking about things ALPACA, and where did they train these models? Off the existing models for a fraction of a fraction of a fraction of the cost: https://crfm.stanford.edu/2023/03/13/alpaca.html
- Bye bye Bing
-
The idea maze for AI startups (2015)
I think there's a new approach for “How do you get the data?” that wasn't available when this article was written in 2015. The new text and image generative models can now be used to synthesize training datasets.
I was working on an typing autocorrect project and needed a corpus of "text messages". Most of the traditional NLP corpuses like those available through NLTK [0] aren't suitable. But it was easy to script ChatGPT to generate thousands of believable text messages by throwing random topics at it.
Similarly, you can synthesize a training dataset by giving GPT the outputs/labels and asking it to generate a variety of inputs. For sentiment analysis... "Give me 1000 negative movie reviews" and "Now give me 1000 positive movie reviews".
The Alpaca folks used GPT-3 to generate high-quality instruction-following datasets [1] based on a small set of human samples.
Etc.
[0] https://www.nltk.org/nltk_data/
[1] https://crfm.stanford.edu/2023/03/13/alpaca.html
-
Repos and tutorials for a full finetune (not LoRA)
AFAIK, the original alpaca repo was a full finetune. https://github.com/tatsu-lab/stanford_alpaca
What are some alternatives?
gpt4all - gpt4all: run open-source LLMs anywhere
alpaca-lora - Instruct-tune LLaMA on consumer hardware
langflow - ⛓️ Langflow is a dynamic graph where each node is an executable unit. Its modular and interactive design fosters rapid experimentation and prototyping, pushing hard on the limits of creativity.
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
llama.cpp - LLM inference in C/C++
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
llama-gpt - A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device. New: Code Llama support!
Alpaca-Turbo - Web UI to run alpaca model locally