llamacpp-for-kobold
alpaca_lora_4bit
llamacpp-for-kobold | alpaca_lora_4bit | |
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8 | 41 | |
96 | 529 | |
- | - | |
10.0 | 8.6 | |
about 1 year ago | 5 months ago | |
C | Python | |
GNU Affero General Public License v3.0 | MIT License |
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llamacpp-for-kobold
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[Kobold Ai] Einführung von llamacpp-for-kobold, führen Sie llama.cpp lokal mit einer schicken Web-Benutzeroberfläche, dauerhaften Geschichten, Bearbeitungswerkzeugen, Speicherformaten, Speicher, Weltinformationen, Anmerkung des Autors, Charakteren, Szenarien und mehr mit minimalem Setup aus.
Geben Sie llamacpp-for-kobold ein
- Künstliche Intelligenz: Italien sperrt ChatGPT
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LLAMA Experience so far
30b (alpacacpp and Kobold-TavernAI on windows, this one)
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Using llama.cpp, how to access API?
I am the creator of https://github.com/LostRuins/llamacpp-for-kobold . It runs a local http server serving a koboldai compatible api with a built in webui. Compatible with all llama.cpp and alpaca.cpp models.
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My experience with Alpaca.cpp
I don't know if anything like that exists. There is this project that I played around with at one point if that helps at all.
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Alpaca.cpp is extremely simple to get working.
Try this https://github.com/LostRuins/llamacpp-for-kobold
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Introducing llamacpp-for-kobold, run llama.cpp locally with a fancy web UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and more with minimal setup
What does it mean? You get an embedded llama.cpp with a fancy writing UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer. In a tiny package (under 1 MB compressed with no dependencies except python), excluding model weights. Simply download, extract, and run the llama-for-kobold.py file with the 4bit quantized llama model.bin as the second parameter.
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Introducing llamacpp-for-kobold, run llama.cpp locally with a fancy web UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and more with minimal setup.
Enter llamacpp-for-kobold
alpaca_lora_4bit
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Open Inference Engine Comparison | Features and Functionality of TGI, vLLM, llama.cpp, and TensorRT-LLM
For training there is also https://github.com/johnsmith0031/alpaca_lora_4bit
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Quantized 8k Context Base Models for 4-bit Fine Tuning
I've been trying to fine tune an erotica model on some large context chat history (reverse proxy logs) and a literotica-instruct dataset I made, with a max context of 8k. The large context size eats a lot of VRAM so I've been trying to find the most efficient way to experiment considering I'd like to do multiple runs to test some ideas. So I'm going to try and use https://github.com/johnsmith0031/alpaca_lora_4bit, which is supposed to train faster and use less memory than qlora.
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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.
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Does we still need monkey patch with exllama loader for lora?
" Using LoRAs with GPTQ-for-LLaMa This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit"
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Why isn’t QLoRA being used more widely for fine tuning models?
4-bit GPTQ LoRA training was available since early April. I did not see any comparison to it in the QLoRA paper or even a mention, so it makes me think they were not aware it already existed.
- Fine-tuning with alpaca_lora_4bit on 8k context SuperHOT models
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Any guide/intro to fine-tuning anywhere?
https://github.com/johnsmith0031/alpaca_lora_4bit is still the SOTA - Faster than qlora, trains on a GPTQ base.
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"Samantha-33B-SuperHOT-8K-GPTQ" now that's a great name for a true model.
I would also like to know how one would finetune this in 4 bit? I think one could take the merged 8K PEFT with the LLaMA weights, and then quantize it to 4 bit, and then train with https://github.com/johnsmith0031/alpaca_lora_4bit ?
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Help with QLoRA
I was under the impression that you just git clone this repo into text-generation-webui/repositories (so you would have GPTQ_for_Llama and alpaca_lora_4bit in the folder), and then just load with monkey patch. Is that not correct? I also tried just downloading alpaca_lora_4bit on its own, git cloning text-gen-webui within it, and installing requirements.txt for both and running with monkey patch. I was following the sections of alpaca_lora_4bit, "Text Generation Webui Monkey Patch" and "monkey patch inside webui"
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Best uncensored model for an a6000
I dont have any familiarity with esxi, but I can say that there are quite a few posts about people doing it on proxmox. I've currently got a machine with 2x3090 passing through to VM's. When I'm training, I pass them both through to the same VM and can do lora 4-bit training on llama33 using https://github.com/johnsmith0031/alpaca_lora_4bit. Then, at inference time, I run a single card into a different VM, and have an extra card available for experimentation.
What are some alternatives?
llama.cpp - LLM inference in C/C++
flash-attention - Fast and memory-efficient exact attention
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
StableLM - StableLM: Stability AI Language Models
koboldcpp - Port of Facebook's LLaMA model in C/C++
safetensors - Simple, safe way to store and distribute tensors
gpt4all - gpt4all: run open-source LLMs anywhere
alpaca-lora - Instruct-tune LLaMA on consumer hardware
TavernAI - TavernAI for nerds [Moved to: https://github.com/Cohee1207/SillyTavern]
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.