lit-gpt
llama.cpp
| lit-gpt | llama.cpp | |
|---|---|---|
| 4 | 780 | |
| 5,243 | 57,984 | |
| - | - | |
| 9.6 | 10.0 | |
| 2 months ago | 6 days ago | |
| Python | C++ | |
| Apache License 2.0 | MIT License |
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lit-gpt
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LLMs on your local Computer (Part 1)
git clone --depth=1 https://github.com/Lightning-AI/lit-gpt cd lit-gpt pip install -r requirements.txt pip install bitsandbytes==0.41.0 huggingface_hub python scripts/download.py --repo_id stabilityai/stablelm-zephyr-3b --from_safetensors=True python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/stabilityai/stablelm-zephyr-3b --dtype float32
- LoRA from Scratch implementation for LLM finetuning
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My experience on starting with fine tuning LLMs with custom data
I'm also working on the finetuning of models for Q&A and I've finetuned llama-7b, falcon-40b, and oasst-pythia-12b using HuggingFace's SFT, H2OGPT's finetuning script and lit-gpt.
- finetune Falcon 40B in 30 minutes using LLaMA adapter
llama.cpp
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IBM Granite: A Family of Open Foundation Models for Code Intelligence
if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp
the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...
And you can search for any GGUF on huggingface
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Ask HN: Affordable hardware for running local large language models?
Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182
- Xmake: A modern C/C++ build tool
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Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
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Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3
What are some alternatives?
h2ogpt - Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
axolotl - Go ahead and axolotl questions
gpt4all - gpt4all: run open-source LLMs anywhere
Nuggt - An Autonomous LLM Agent that runs on Wizcoder-15B
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
instructor-embedding - [ACL 2023] One Embedder, Any Task: Instruction-Finetuned Text Embeddings
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
semantra - Multi-tool for semantic search
ggml - Tensor library for machine learning
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.