llm-ls
text-generation-inference
llm-ls | text-generation-inference | |
---|---|---|
2 | 29 | |
471 | 8,053 | |
11.7% | 8.2% | |
8.2 | 9.6 | |
2 months ago | 4 days ago | |
Rust | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
llm-ls
-
Continue will generate, refactor, and explain entire sections of code
> I'd have expected that the main lever the product has in being better than others is having a custom model that understands code edits much more than others.
True, but this is not something this particular product would solve. There are already models specifically trained to work on code. What's appealing to me is the flexibility of being able to choose which one to use, rather than my workflow being tied to a specific product or company.
> the IDE integration seems to be the "easy bit"
I admittedly haven't researched this much, but this is not currently the case. There is no generic API for LLMs that IDEs can plug into, so all plugins must target a specific model. We ultimately need an equivalent of an LSP server for LLMs, and while such a project exists[1], it looks to be in its infancy, as expected.
[1]: https://github.com/huggingface/llm-ls
-
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.
text-generation-inference
- FLaNK AI-April 22, 2024
-
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?
-
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
-
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.
-
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.
-
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 ?
What are some alternatives?
OpenAI-sublime-text - Sublime Text OpenAI completion plugin with GPT-4 support!
llama-cpp-python - Python bindings for llama.cpp
cody - AI that knows your entire codebase
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
localpilot
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
continue - ⏩ Open-source VS Code and JetBrains extensions that enable you to easily create your own modular AI software development system
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.
refact - WebUI for Fine-Tuning and Self-hosting of Open-Source Large Language Models for Coding
FlexGen - Running large language models on a single GPU for throughput-oriented scenarios.
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
safetensors - Simple, safe way to store and distribute tensors