lora-instruct
FastLoRAChat
| lora-instruct | FastLoRAChat | |
|---|---|---|
| 1 | 2 | |
| 97 | 119 | |
| - | - | |
| 7.0 | 7.2 | |
| 5 months ago | about 1 year ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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lora-instruct
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Training a LoRA with MPT Models
Hi, i have created custom data, same format as alphachas json file. And fine tuned mpt-7b-instruct using this link https://github.com/leehanchung/lora-instruct I have also used your patch, the fine tuning got successfull and also the loss got decreased but when am trying to make prediction using the fine tuned model am not getting correct output even on the trained data, it's generating output with lots of nonsense
FastLoRAChat
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[P] FastLoRAChat Instruct-tune LLaMA on consumer hardware with shareGPT data
Announcing FastLoRAChat , training chatGPT without A100.
- FastLoRAChat – Lora finetuned LLM with ChatGPT capabality
What are some alternatives?
AutoLearn-GPT - ChatGPT learns automatically.
ragas - Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
LMOps - General technology for enabling AI capabilities w/ LLMs and MLLMs
hyde - HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels
mpt-lora-patch - Patch for MPT-7B which allows using and training a LoRA
ReAct - [ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models
Roy - Roy: A lightweight, model-agnostic framework for crafting advanced multi-agent systems using large language models.
llama2-haystack - Using Llama2 with Haystack, the NLP/LLM framework.
punica - Serving multiple LoRA finetuned LLM as one
gpt-j-fine-tuning-example - Fine-tuning 6-Billion GPT-J (& other models) with LoRA and 8-bit compression
alpaca-lora - Instruct-tune LLaMA on consumer hardware
Anima - 33B Chinese LLM, DPO QLORA, 100K context, AirLLM 70B inference with single 4GB GPU