rllama
alpaca-lora
rllama | alpaca-lora | |
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7 | 107 | |
519 | 18,197 | |
- | - | |
6.2 | 3.6 | |
3 months ago | 2 months ago | |
Rust | Jupyter Notebook | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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rllama
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Ask HN: Who wants to be hired? (July 2023)
Location: San Francisco
Remote: No preference, as long as I don't have to move far from Bay Area
Willing to relocate: No
Technologies: C, Rust, Golang, Haskell, Lisp, Python, Lua, OpenGL, SQLite3, JavaScript, PostgreSQL, AWS EC2, S3, ECS, Batch.
Resume: https://www.linkedin.com/in/mikjuola
Email: [email protected]
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I've been working at the Bay Area since 2015, most recently at Pinterest. At work, I've done big data pipelines, designed some batch job systems, computing metrics, handling billing APIs, lots of Python, Go and Java and working with AWS, i.e. backend and data engineer stuff.
But I'm trying to look for work that's more in line with what I do on my free time: Challenging low-level C or Rust programming, machine learning implementations (see e.g. this thing I made https://github.com/Noeda/rllama/, graphics programming or research-type work, uncommon programming languages.
If you scroll through my random crap repositories you can see what kind of things I'm interested in: https://github.com/Noeda?tab=repositories
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State-of-the-art open-source chatbot, Vicuna-13B, just released model weights
No, my project is called rllama. No relation to GGML. https://github.com/Noeda/rllama
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Where can I learn more about SIMD, CPU intrinsics and the like in the context of Rust?
I have seen some Rust attempts as well such as https://github.com/Noeda/rllama/ but they are still way behind the C++ ones. This seems like an interesting space to get into.
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Show HN: Alpaca.cpp – Run an Instruction-Tuned Chat-Style LLM on a MacBook
I ran it on a 128 RAM machine with a Ryzen 5950X. It's not fast, 4 seconds per token. But it's just about fits without swapping. https://github.com/Noeda/rllama/
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Llama.rs – Rust port of llama.cpp for fast LLaMA inference on CPU
I've counted three different Rust LLaMA implementations on r/rust subreddit this week:
https://github.com/Noeda/rllama/ (pure Rust+OpenCL)
https://github.com/setzer22/llama-rs/ (ggml based)
https://github.com/philpax/ggllama (also ggml based)
There's also a discussion on GitHub issue on setzer's repo to collaborate a bit on these separate efforts: https://github.com/setzer22/llama-rs/issues/4
- Rust+OpenCL+AVX2 implementation of LLaMA inference code
- Pure Rust CPU and OpenCL implementation of LLaMA language model
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?
llama.cpp - LLM inference in C/C++
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
ultraviolet - A wide linear algebra crate for games and graphics.
litestar - Production-ready, Light, Flexible and Extensible ASGI API framework | Effortlessly Build Performant APIs
gpt4all - gpt4all: run open-source LLMs anywhere
80r3d
llama - Inference code for Llama models
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
ggml - Tensor library for machine learning