local_llama
llama.cpp
local_llama | llama.cpp | |
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10 | 772 | |
179 | 56,891 | |
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6.6 | 10.0 | |
10 days ago | 4 days ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
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local_llama
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Discussion: Biggest Roadblocks to Deploy LLMs to Production
I work with AWS daily, terraform, Python and java creating and maintaining enterprise solutions. I have played with sagemaker but it is so expensive I hate to leave it up for longer than a day. I downloaded and created a chat with your docs (entirely in airplane mode) here point being that I’ve hosted models both locally and in the cloud. But just ended up sticking to API calls as it’s so cheap
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You can now chat with your documents privately!
I posted the speed of mine in the readme https://github.com/jlonge4/local_llama
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Textgen webui for gpt_chatwithPDF
I would like to use this tool https://github.com/jlonge4/gpt_chatwithPDF/blob/main/gpt_chat_api.py but unfortunately the local version (https://github.com/jlonge4/local_llama) is bound to the CPU and thus quiet slow. Is there any way i could get textgenwebui working with the above stated tool?
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Is there a way to ask questions about ç multiple PDF files?
This is what you want https://github.com/jlonge4/local_llama it’s fully offline with no third parties, but the setup is a bit involved
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Newbie here. Need help with choosing a llm model for pdf ingestion and summarization locally
Or try this https://github.com/jlonge4/local_llama
- Local GPT (completely offline and no OpenAI!)
- Local GPT (completely offline and no OpenAI!) [P]
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Offline llama
Code here if interested
llama.cpp
- 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
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Embeddings are a good starting point for the AI curious app developer
Have just done this recently for local chat with pdf feature in https://recurse.chat. (It's a macOS app that has built-in llama.cpp server and local vector database)
Running an embedding server locally is pretty straightforward:
- Get llama.cpp release binary: https://github.com/ggerganov/llama.cpp/releases
- Mixtral 8x22B
- Llama.cpp: Improve CPU prompt eval speed
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Ollama 0.1.32: WizardLM 2, Mixtral 8x22B, macOS CPU/GPU model split
Ah, thanks for this! I can't edit my parent comment that you replied to any longer unfortunately.
As I said, I only compared the contributors graphs [0] and checked for overlaps. But those apparently only go back about year and only list at most 100 contributors ranked by number of commits.
[0]: https://github.com/ollama/ollama/graphs/contributors and https://github.com/ggerganov/llama.cpp/graphs/contributors
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.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
gpt4all - gpt4all: run open-source LLMs anywhere
EmbedAI - An app to interact privately with your documents using the power of GPT, 100% privately, no data leaks
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
zep - Zep: Long-Term Memory for AI Assistants.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
chatdocs - Chat with your documents offline using AI.
ggml - Tensor library for machine learning
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM