get-beam VS alpaca-lora

Compare get-beam vs alpaca-lora and see what are their differences.


Run GPU inference and training jobs on serverless infrastructure that scales with you. (by slai-labs)


Instruct-tune LLaMA on consumer hardware (by tloen)
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get-beam alpaca-lora
8 107
89 18,137
- -
8.2 3.6
21 days ago about 2 months ago
Shell Jupyter Notebook
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of get-beam. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-30.


Posts with mentions or reviews of alpaca-lora. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-11.

What are some alternatives?

When comparing get-beam and alpaca-lora you can also consider the following projects:


text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

whisper-turbo - Cross-Platform, GPU Accelerated Whisper 🏎️

qlora - QLoRA: Efficient Finetuning of Quantized LLMs


llama.cpp - LLM inference in C/C++

store-sentry - Manage access to in-app purchase content hosted in Cloudflare based on App Store Server Notifications

gpt4all - gpt4all: run open-source LLMs anywhere

llama - Inference code for Llama models

ggml - Tensor library for machine learning

RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.

alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM