text-generation-inference
ollama
text-generation-inference | ollama | |
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29 | 197 | |
7,881 | 62,615 | |
6.2% | 19.1% | |
9.6 | 9.9 | |
5 days ago | 1 day ago | |
Python | Go | |
Apache License 2.0 | MIT License |
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text-generation-inference
- FLaNK AI-April 22, 2024
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Zephyr 141B, a Mixtral 8x22B fine-tune, is now available in Hugging Chat
I wanted to write that TGI inference engine is not Open Source anymore, but they have reverted the license back to Apache 2.0 for the new version TGI v2.0: https://github.com/huggingface/text-generation-inference/rel...
Good news!
- Hugging Face reverts the license back to Apache 2.0
- HuggingFace text-generation-inference is reverting to Apache 2.0 License
- FLaNK Stack 05 Feb 2024
- Is there any open source app to load a model and expose API like OpenAI?
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AI Code assistant for about 50-70 users
Setting up a server for multiple users is very different from setting up LLM for yourself. A safe bet would be to just use TGI, which supports continuous batching and is very easy to run via Docker on your server. https://github.com/huggingface/text-generation-inference
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LocalPilot: Open-source GitHub Copilot on your MacBook
Okay, I actually got local co-pilot set up. You will need these 4 things.
1) CodeLlama 13B or another FIM model https://huggingface.co/codellama/CodeLlama-13b-hf. You want "Fill in Middle" models because you're looking at context on both sides of your cursor.
2) HuggingFace llm-ls https://github.com/huggingface/llm-ls A large language mode Language Server (is this making sense yet)
3) HuggingFace inference framework. https://github.com/huggingface/text-generation-inference At least when I tested you couldn't use something like llama.cpp or exllama with the llm-ls, so you need to break out the heavy duty badboy HuggingFace inference server. Just config and run. Now config and run llm-ls.
4) Okay, I mean you need an editor. I just tried nvim, and this was a few weeks ago, so there may be better support. My expereicen was that is was full honest to god copilot. The CodeLlama models are known to be quite good for its size. The FIM part is great. Boilerplace works so much easier with the surrounding context. I'd like to see more models released that can work this way.
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Mistral 7B Paper on ArXiv
A simple microservice would be https://github.com/huggingface/text-generation-inference .
Works flawlessly in Docker on my Windows machine, which is extremely shocking.
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best way to serve llama V2 (llama.cpp VS triton VS HF text generation inference)
I am wondering what is the best / most cost-efficient way to serve llama V2. - llama.cpp (is it production ready or just for playing around?) ? - Triton inference server ? - HF text generation inference ?
ollama
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Create an AI prototyping environment using Jupyter Lab IDE with Typescript, LangChain.js and Ollama for rapid AI prototyping
Ollama for running LLMs locally
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Setup Llama 3 using Ollama and Open-WebUI
curl -fsSL https://ollama.com/install.sh | sh
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Ollama v0.1.33 with Llama 3, Phi 3, and Qwen 110B
Streaming is not a problem (it's just a simple flag: https://github.com/wiktor-k/llama-chat/blob/main/index.ts#L2...) but I've never used voice input.
The examples show image input though: https://github.com/ollama/ollama/blob/main/docs/api.md#reque...
Maybe you can file an issue here: https://github.com/ollama/ollama/issues
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I Said Goodbye to ChatGPT and Hello to Llama 3 on Open WebUI - You Should Too
I’m a huge fan of open source models, especially the newly release Llama 3. Because of the performance of both the large 70B Llama 3 model as well as the smaller and self-host-able 8B Llama 3, I’ve actually cancelled my ChatGPT subscription in favor of Open WebUI, a self-hostable ChatGPT-like UI that allows you to use Ollama and other AI providers while keeping your chat history, prompts, and other data locally on any computer you control.
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Let’s build AI-tools with the help of AI and Typescript!
Ollama for running LLMs locally
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One LLaMa to rule them all
There are various other interesting options to set, but for those, I will direct you to the link to the documentation. During the OS Day, I had the chance to experiment a bit with the models offered by Ollama; in fact, if you need some inspiration, I invite you to check out the YouTube channel of Shroedinger Hat where you can find the videos of the individual talks, also organized in a single playlist; you will find more than one showing the use of Ollama for various projects and in various ways 😁
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How to Run Llama 3 Locally with Ollama and Open WebUI
That’s where Ollama comes in! Ollama is a free and open-source application that allows you to run various large language models, including Llama 3, on your own computer, even with limited resources. Ollama takes advantage of the performance gains of llama.cpp, an open source library designed to allow you to run LLMs locally with relatively low hardware requirements. It also includes a sort of package manager, allowing you to download and use LLMs quickly and effectively with just a single command.
- Ollama: Acknowledge the work done by Georgi and team
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Mixtral 8x22B
easiest is probably with ollama [0]. I think the ollama API is OpenAI compatible.
[0]https://ollama.com/
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Ollama 0.1.32: WizardLM 2, Mixtral 8x22B, macOS CPU/GPU model split
They ended up addressing this issue by including it on the last line of their readme as one of the "Supported backends[sic]".
https://github.com/ollama/ollama/issues/3697
What are some alternatives?
llama-cpp-python - Python bindings for llama.cpp
llama.cpp - LLM inference in C/C++
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
gpt4all - gpt4all: run open-source LLMs anywhere
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
FlexGen - Running large language models on a single GPU for throughput-oriented scenarios.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
llama - Inference code for Llama models
safetensors - Simple, safe way to store and distribute tensors
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.