openplayground
alpaca-lora
openplayground | alpaca-lora | |
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12 | 107 | |
6,108 | 18,252 | |
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0.6 | 3.6 | |
15 days ago | 3 months ago | |
TypeScript | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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openplayground
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Show HN: Unified access to top AI models, supporting GPT4, Claude and more
https://github.com/nat/openplayground
I load up $5 into my account using my credit card and then reload it whenever it gets low, it also has a tab for comparing multiple resulta from different models together.
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I love how many people want a way bigger context window for example for GPT-4 (like 100k-1m). May I introduce you the cost of one API call at the full 32k context window? 2$. So 1m would approximately cost you 60$. One call. 60$.
https://github.com/nat/openplayground https://discord.gg/uT98U9HJ.
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How good is the 100k context model?
Try here: https://github.com/nat/openplayground
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Performance of GPT-4 vs PaLM 2
From there you have lots of other models: One of the best places to easily start using multiple models is using a multiple model UI program lik GPT4All, there are also some programs that provide access to more models or use different ways of interfacing with them, here are some of what I've found are the best / most popular programs to play around with lots of different models and compare them: LocalAI, text-generation-webui, open playground
- Show HN: Promptfoo – a tool for comparing LLM prompts and models
- Show HN: AI Playground by Vercel Labs
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What is this subreddit about? I can't tell if its wifaus or locally run LLMs
Here's another interesting engine called AI playground that lets you do side-by-side comparisons of language models based on the same prompts: https://github.com/nat/openplayground
- An LLM playground you can run on your laptop
alpaca-lora
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How to deal with loss for SFT for CausalLM
Here is a example: https://github.com/tloen/alpaca-lora/blob/main/finetune.py
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How to Finetune Llama 2: A Beginner's Guide
In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.
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Fine-tuning LLMs with LoRA: A Gentle Introduction
Implement the code in Llama LoRA repo in a script we can run locally
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Newbie here - trying to install a Alpaca Lora and hitting an error
Hi all - relatively new to GitHub / programming in general, and I wanted to try to set up Alpaca Lora locally. Following the guide here: https://github.com/tloen/alpaca-lora
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A simple repo for fine-tuning LLMs with both GPTQ and bitsandbytes quantization. Also supports ExLlama for inference for the best speed.
Follow up the popular work of u/tloen alpaca-lora, I wrapped the setup of alpaca_lora_4bit to add support for GPTQ training in form of installable pip packages. You can perform training and inference with multiple quantizations method to compare the results.
- FLaNK Stack Weekly for 20 June 2023
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Converting to GGML?
If instead you want to apply a LoRa to a pytorch model, a lot of people use this script to apply to LoRa to the 16 bit model and then quantize it with a GPTQ program afterwards https://github.com/tloen/alpaca-lora/blob/main/export_hf_checkpoint.py
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Simple LLM Watermarking - Open Lllama 3b LORA
There are a few papers on watermarking LLM output, but from what I have seen they all use complex methods of detection to allow the watermark to go unseen by the end user, only to be detected by algorithm. I believe that a more overt system of watermarking might also be beneficial. One simple method that I have tried is character substitution. For this model, I LORA finetuned openlm-research/open_llama_3b on the alpaca_data_cleaned_archive.json dataset from https://github.com/tloen/alpaca-lora/ modified by replacing all instances of the "." character in the outputs with a "ι" The results are pretty good, with the correct the correct substitutions being generated by the model in most cases. It doesn't always work, but this was only a LORA training and for two epochs of 400 steps each, and 100% substitution isn't really required.
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text-generation-webui's "Train Only After" option
I am kind of new to finetuning LLM's and am not able to understand what this option exactly refers to. I guess it has the same meaning as the "train_on_inputs" parameter of alpacalora though.
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Learning sources on working with local LLMs
Read the paper and also: https://github.com/tloen/alpaca-lora
What are some alternatives?
llama.cpp - LLM inference in C/C++
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
BetterChatGPT - An amazing UI for OpenAI's ChatGPT (Website + Windows + MacOS + Linux)
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
promptfoo - Test your prompts, models, and RAGs. Catch regressions and improve prompt quality. LLM evals for OpenAI, Azure, Anthropic, Gemini, Mistral, Llama, Bedrock, Ollama, and other local & private models with CI/CD integration.
gpt4all - gpt4all: run open-source LLMs anywhere
ChatALL - Concurrently chat with ChatGPT, Bing Chat, Bard, Alpaca, Vicuna, Claude, ChatGLM, MOSS, 讯飞星火, 文心一言 and more, discover the best answers
llama - Inference code for Llama models
galai - Model API for GALACTICA
ggml - Tensor library for machine learning