java-snapshot-testing
trl
| java-snapshot-testing | trl | |
|---|---|---|
| 2 | 14 | |
| 110 | 10,216 | |
| 0.9% | 3.6% | |
| 4.5 | 9.8 | |
| 4 months ago | 3 days ago | |
| Java | Python | |
| MIT License | Apache License 2.0 |
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java-snapshot-testing
- FLaNK Stack 29 Jan 2024
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📸 Snapshot Testing with Kotlin
In this PoC I will use origin-energy/java-snapshot-testing and as stated in "the testing framework loved by lazy productive devs" I use it whenever I find myself manually saving test expectations as text files 😅
trl
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ORPO, DPO, and PPO: Optimizing Models for Human Preferences
Implementation: ORPO has been integrated into popular fine-tuning libraries like TRL, Axolotl, and LLaMA-Factory.
- 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
What are some alternatives?
reor - Private & local AI personal knowledge management app for high entropy people.
lm-human-preferences - Code for the paper Fine-Tuning Language Models from Human Preferences
pong-wars
alpaca-lora - Instruct-tune LLaMA on consumer hardware
llm-classifier - Classify data instantly using an LLM
trlx - A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
finagg - A Python package for aggregating and normalizing historical data from popular and free financial APIs.
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.
Deep_Object_Pose - Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018)
llama-recipes - Scripts for fine-tuning Meta Llama 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 Llama for WhatsApp & Messenger.
ml-engineering - Machine Learning Engineering Open Book
sparsegpt-for-LLaMA - Code for the paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot" with LLaMA implementation.