VectorDBBench
alpaca-lora
VectorDBBench | alpaca-lora | |
---|---|---|
16 | 107 | |
408 | 18,217 | |
10.0% | - | |
8.5 | 3.6 | |
5 days ago | 3 months ago | |
Python | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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VectorDBBench
- FLaNK-AIM Weekly 06 May 2024
- GPU index supports in Vector Database benchmark latest version
- Benchmarking Tool for Vector DBs
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Vespa.ai is spinning out of Yahoo as a separate company
We conducted benchmark tests on Elastic's queries per second (QPS) performance using datasets of 500,000 and 1 million vectors. Result was Zilliz is 13x and 22x faster, per number of vectors respectively. https://zilliz.com/blog/elasticsearch-cloud-vs-zilliz
Feel free to explore our open-source benchmarking tool, which allows you to examine our methodology and even compare it with your vector database. https://github.com/zilliztech/VectorDBBench
- Vector Database benchmark with 1536/768 dim data
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Vector Dataset benchmark with 1536/768 dim data
"
the link is: https://github.com/zilliztech/VectorDBBench/issues/200#issue...
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Comparison of Vector Databases
Interesting graphic, bland and unvoiced conclusion
You're also missing a lot of details. For example, Milvus and Zilliz are actually a little different, check this out for more details: https://github.com/zilliztech/VectorDBBench (of course run it on your own stuff, don't blindly trust companies just because their product is open source)
Also if you want to throw some more comparisons in their checkout elastic search
- VectorDB benchmark for both cloud and open source
- Cloud Vector Database Benchmark Result
- FLaNK Stack Weekly for 20 June 2023
alpaca-lora
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How to deal with loss for SFT for CausalLM
Here is a example: https://github.com/tloen/alpaca-lora/blob/main/finetune.py
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How to Finetune Llama 2: A Beginner's Guide
In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.
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Fine-tuning LLMs with LoRA: A Gentle Introduction
Implement the code in Llama LoRA repo in a script we can run locally
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Newbie here - trying to install a Alpaca Lora and hitting an error
Hi all - relatively new to GitHub / programming in general, and I wanted to try to set up Alpaca Lora locally. Following the guide here: https://github.com/tloen/alpaca-lora
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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.
- FLaNK Stack Weekly for 20 June 2023
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Converting to GGML?
If instead you want to apply a LoRa to a pytorch model, a lot of people use this script to apply to LoRa to the 16 bit model and then quantize it with a GPTQ program afterwards https://github.com/tloen/alpaca-lora/blob/main/export_hf_checkpoint.py
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Simple LLM Watermarking - Open Lllama 3b LORA
There are a few papers on watermarking LLM output, but from what I have seen they all use complex methods of detection to allow the watermark to go unseen by the end user, only to be detected by algorithm. I believe that a more overt system of watermarking might also be beneficial. One simple method that I have tried is character substitution. For this model, I LORA finetuned openlm-research/open_llama_3b on the alpaca_data_cleaned_archive.json dataset from https://github.com/tloen/alpaca-lora/ modified by replacing all instances of the "." character in the outputs with a "ι" The results are pretty good, with the correct the correct substitutions being generated by the model in most cases. It doesn't always work, but this was only a LORA training and for two epochs of 400 steps each, and 100% substitution isn't really required.
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text-generation-webui's "Train Only After" option
I am kind of new to finetuning LLM's and am not able to understand what this option exactly refers to. I guess it has the same meaning as the "train_on_inputs" parameter of alpacalora though.
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Learning sources on working with local LLMs
Read the paper and also: https://github.com/tloen/alpaca-lora
What are some alternatives?
jsoncrack.com - ✨ Innovative and open-source visualization application that transforms various data formats, such as JSON, YAML, XML, CSV and more, into interactive graphs.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
chroma - the AI-native open-source embedding database
llama.cpp - LLM inference in C/C++
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
gpt4all - gpt4all: run open-source LLMs anywhere
motorhead - 🧠 Motorhead is a memory and information retrieval server for LLMs.
llama - Inference code for Llama models
vectara-answer - LLM-powered Conversational AI experience using Vectara
ggml - Tensor library for machine learning