LLaMA-8bit-LoRA
trl
| LLaMA-8bit-LoRA | trl | |
|---|---|---|
| 3 | 13 | |
| 145 | 8,120 | |
| 0.7% | 4.3% | |
| 5.1 | 9.7 | |
| 8 months ago | 4 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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LLaMA-8bit-LoRA
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Any news on training LoRAs in 4-bit mode?
https://github.com/serp-ai/LLaMA-8bit-LoRA/blob/main/docs/merging_the_weights.md < merge models
- [R] 🤖🌟 Unlock the Power of Personal AI: Introducing ChatLLaMA, Your Custom Personal Assistant! 🚀💬
trl
- FLaNK Stack 29 Jan 2024
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OOM Error while using TRL for RLHF Fine-tuning
I am using TRL for RLHF fine-tuning the Llama-2-7B model and getting an OOM error (even with batch_size=1). If anyone used TRL for RLHF can please tell me what I am doing wrong? Code details can be found in the GitHub issue.
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[D] Tokenizers Truncation during Fine-tuning with Large Texts
SFTtrainer from huggingface
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New Open-source LLMs! 🤯 The Falcon has landed! 7B and 40B
For lora - PEFT seems to work. I don't have patience to wait 5 hours, but modifying this example seems to work. You don't even need to modify that much, as their model just as neo-x uses query_key_value name for self-attention.
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[D] Using RLHF beyond preference tuning
They have examples of making GPT output more positive (code) by using a sentiment model as reward. There are other examples about reducing toxicity, summarization here: https://github.com/lvwerra/trl/tree/main/examples . Should be fairly simple to modify the sentiment example and try the calculator reward you mentioned above.
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[R] 🤖🌟 Unlock the Power of Personal AI: Introducing ChatLLaMA, Your Custom Personal Assistant! 🚀💬
You can use this -> https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt-neox-20b_peft/merge_peft_adapter.py
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[R] Stanford-Alpaca 7B model (an instruction tuned version of LLaMA) performs as well as text-davinci-003
Just the hh directly. From the results it seems like it might possibly be enough but I might also try instruction tuning then running the whole process from that base. I will also be running the reinforcement learning by using a Lora using this as an example https://github.com/lvwerra/trl/tree/main/examples/sentiment/scripts/gpt-neox-20b_peft
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[R] A simple explanation of Reinforcement Learning from Human Feedback (RLHF)
This package is pretty simple to use! https://github.com/lvwerra/trl
- Transformer Reinforcement Learning
- trl: Train transformer language models with reinforcement learning
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
lm-human-preferences - Code for the paper Fine-Tuning Language Models from Human Preferences
text-generation-webui-testing - A fork of textgen that still supports V1 GPTQ, 4-bit lora and other GPTQ models besides llama.
sparsegpt-for-LLaMA - Code for the paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot" with LLaMA implementation.
trlx - A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
Sparsebit - A model compression and acceleration toolbox based on pytorch.
alpaca_lora_4bit
llama-recipes - Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization and Q&A. Supporting a number of candid inference solutions such as HF TGI, VLLM for local or cloud deployment. Demo apps to showcase Meta Llama3 for WhatsApp & Messenger.
Deep_Object_Pose - Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018)
java-snapshot-testing - Facebook style snapshot testing for JAVA Tests