trl
FLiPStackWeekly
| trl | FLiPStackWeekly | |
|---|---|---|
| 13 | 80 | |
| 8,176 | 14 | |
| 4.9% | - | |
| 9.7 | 9.9 | |
| about 21 hours ago | 6 days ago | |
| Python | ||
| Apache License 2.0 | Apache License 2.0 |
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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.
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
FLiPStackWeekly
What are some alternatives?
lm-human-preferences - Code for the paper Fine-Tuning Language Models from Human Preferences
gorilla-cli - LLMs for your CLI
alpaca-lora - Instruct-tune LLaMA on consumer hardware
awk-raycaster - Pseudo-3D shooter written completely in gawk using raycasting technique
trlx - A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
litellm - Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)
LLaMA-8bit-LoRA - Repository for Chat LLaMA - training a LoRA for the LLaMA (1 or 2) models on HuggingFace with 8-bit or 4-bit quantization. Research only.
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
sparsegpt-for-LLaMA - Code for the paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot" with LLaMA implementation.
create-nifi-pulsar-flink-apps - How to create a real-time scalable streaming app using Apache NiFi, Apache Pulsar and Apache Flink SQL
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.
FLiP-PulsarSummit2022Asia - FLiP-PulsarSummit2022Asia: Pulsar Summit Asia 2022