Hacker News
- Stanford Alpaca: An Instruction-following LLaMA model https://github.com/tatsu-lab/stanford_alpaca 2 comments
Linking pages
- GitHub - antimatter15/alpaca.cpp: Locally run an Instruction-Tuned Chat-Style LLM https://github.com/antimatter15/alpaca.cpp 287 comments
- The genie escapes: Stanford copies the ChatGPT AI for less than $600 https://newatlas.com/technology/stanford-alpaca-cheap-gpt/ 244 comments
- Using ChatGPT Plugins with LLaMA. Use OpenAI’s chatgpt-retrieval-plugin… | by Sarmad Qadri | Mar, 2023 | lastmile ai https://blog.lastmileai.dev/using-openais-retrieval-plugin-with-llama-d2e0b6732f14 165 comments
- Building LLM applications for production https://huyenchip.com/2023/04/11/llm-engineering.html 136 comments
- GitHub - mikeroyal/Self-Hosting-Guide: Self-Hosting Guide. Learn all about locally hosting (on premises & private web servers) and managing software applications by yourself or your organization. Including Cloud, LLMs, WireGuard, Automation, Home Assistant, and Networking. https://github.com/mikeroyal/Self-Hosting-Guide 108 comments
- GitHub - context-labs/autodoc: oolkit for auto-generating codebase documentation using LLMs https://github.com/context-labs/autodoc 86 comments
- GitHub - Lightning-AI/lit-llama: Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed. https://github.com/Lightning-AI/lit-llama 69 comments
- GitHub - sahil280114/codealpaca https://github.com/sahil280114/codealpaca 63 comments
- Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces https://arxiv.org/html/2410.09918v1 54 comments
- GitHub - nichtdax/awesome-totally-open-chatgpt: A list of totally open alternatives to ChatGPT https://github.com/nichtdax/awesome-totally-open-chatgpt 48 comments
- How to use Alpaca-LoRA to fine-tune a model like ChatGPT - Replicate – Replicate https://replicate.com/blog/fine-tune-alpaca-with-lora 48 comments
- Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation) https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms 37 comments
- GitHub - chris-alexiuk/alpaca-lora: Instruct-tune LLaMA on consumer hardware https://github.com/chris-alexiuk/alpaca-lora 30 comments
- GitHub - minosvasilias/godot-dodo: Finetuning large language models for GDScript generation. https://github.com/minosvasilias/godot-dodo 28 comments
- How to run your own LLM (GPT) https://blog.rfox.eu/en/Programming/How_to_run_your_own_LLM_GPT.html 26 comments
- GitHub - go-skynet/LocalAI: :robot: Self-hosted, community-driven, local OpenAI compatible API. Drop-in replacement for OpenAI running LLMs on consumer-grade hardware. Free Open Source OpenAI alternative. No GPU required. Runs ggml, gguf, GPTQ, onnx, TF compatible models: llama, llama2, gpt4all, rwkv, whisper, vicuna, koala, cerebras, falcon, dolly, starcoder, and many others https://github.com/go-skynet/LocalAI 23 comments
- GitHub - ViperX7/Alpaca-Turbo: Web UI to run alpaca model locally https://github.com/ViperX7/Alpaca-Turbo 19 comments
- Stanford Take Down Alpaca AI Due To Hallucinations https://www.theinsaneapp.com/2023/03/stanford-take-down-alpaca-ai.html 18 comments
- GitHub - tloen/alpaca-lora: Instruct-tune LLaMA on consumer hardware https://github.com/tloen/alpaca-lora 11 comments
- GitHub - imaurer/awesome-decentralized-llm: Collection of LLM resources that can be used to build products you can "own" or to perform reproducible research. https://github.com/imaurer/awesome-decentralized-llm 10 comments
Linked pages
- Python Release Python 3.8.0 | Python.org https://www.python.org/downloads/release/python-380/ 361 comments
- Stanford CRFM https://crfm.stanford.edu/2023/03/13/alpaca.html 298 comments
- Stanford Alpaca https://crfm.stanford.edu/alpaca/ 11 comments
- GitHub - psf/black: The uncompromising Python code formatter https://github.com/psf/black 8 comments
- [2212.10560] Self-Instruct: Aligning Language Model with Self Generated Instructions https://arxiv.org/abs/2212.10560 1 comment
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