outlines
llamafile
outlines | llamafile | |
---|---|---|
33 | 36 | |
5,799 | 15,120 | |
11.0% | 29.1% | |
9.7 | 9.6 | |
6 days ago | 3 days ago | |
Python | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
outlines
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Infini-Gram: Scaling unbounded n-gram language models to a trillion tokens
> [2]: https://github.com/outlines-dev/outlines?tab=readme-ov-file#...
It's interesting as speech recognition has become more popular than ever through services like Alexa, and other iot devices support for OS speech recognition
Unfortunately most implementations (especially those that are iot focused) don't have very important features for robust speech recognition.
1. Ability to enable and disable a grammar
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Show HN: LLM-powered NPCs running on your hardware
[4] https://github.com/outlines-dev/outlines/tree/main
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Advanced RAG with guided generation
The next step is defining how to guide generation. For this step, we'll use the Outlines library. Outlines is a library for controlling how tokens are generated. It applies logic to enforce schemas, regular expressions and/or specific output formats such as JSON.
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Anthropic's Haiku Beats GPT-4 Turbo in Tool Use
No benchmarks, just my anecdotal experience trying to get local LLM's to respond with JSON. The method above works for my use case nearly 100% of the time. Other things I've tried (e.g. `outlines`[0]) are really slow or don't work at all. Would love to hear what others have tried!
0 - https://github.com/outlines-dev/outlines
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Show HN: Chess-LLM, using constrained-generation to force LLMs to battle it out
As I was playing with the Outlines library (https://outlines-dev.github.io/outlines/), I discussed with my friend Maxime how funny it would be if we set up a way to pair LLMs in chess matches till one wins. The first time I tried it, it required substantial prompt engineering to get some of those LLMs to propose valid moves. Large language models can mostly stay focused and even play rather well; see https://news.ycombinator.com/item?id=37616170 for example. However small language models aren't as easy to convince.
Some of those LLMs have seen very little chess notation and so after the first few opening moves there aren't any valid tactics, let alone strategy, so they would end up either repeating the same move, or hallucinate moves that are not valid (Kxe5, but there would be a queen on e5!)
Then Outlines came along and we could force them to pick valid moves with little cost! Maxime worked super fast and got a first version of this idea as a gradio space.
I think it is pretty fun to see the (mostly terrible, but otherwise valid) chess that those LLMs play. Maybe it will even be instructive to how we can create small LLMs that can play much better than the ones on the leaderboard.
Anyway, you can check it out here:
https://huggingface.co/spaces/mlabonne/chessllm
What is interactive about it: you can pick the LLMs from available models on HuggingFace (within reason, small LLMs are preferable so that the space does not crash) or push one of your own small models to HF and have it fight with others. At the end of the game the leaderboard is updated.
Hope you find it fun!
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Show HN: Prompts as (WASM) Programs
> The most obvious usage of this is forcing a model to output valid JSON
Isn't this something that Outlines [0], Guidance [1] and others [2] already solve much more elegantly?
0. https://github.com/outlines-dev/outlines
1. https://github.com/guidance-ai/guidance
2. https://github.com/sgl-project/sglang
- Show HN: Fructose, LLM calls as strongly typed functions
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Unlocking the frontend – a call for standardizing component APIs pt.2
And I think “just” Markdown doesn’t quite cut it for safe guidance. For example: directly generating content for your components. But I’m really excited about tooling like outlines appearing, with a greater focus on guided generation for structured data. Because this is often what we actually need!
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Ask HN: What are some actual use cases of AI Agents?
It's pretty easy to force a locally running model to always output valid JSON: when it gives you probabilities for the next tokens, discard all tokens that would result in invalid JSON at that point (basically reverse parsing), and then apply the usual techniques to pick the completion only from the remaining tokens. You can even validate against a JSON schema that way, so long as it is simple enough.
There are a bunch of libraries for this already, e.g.: https://github.com/outlines-dev/outlines
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Launch HN: AgentHub (YC W24) – A no-code automation platform
https://github.com/outlines-dev/outlines/blob/7fae436345e621... squares with my experience using LLMs for anything real
sequence = generator("Alice had 4 apples and Bob ate 2. Write an expression for Alice's apples:")
llamafile
- FLaNK-AIM Weekly 06 May 2024
- llamafile v0.8
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Mistral AI Launches New 8x22B Moe Model
I think the llamafile[0] system works the best. Binary works on the command line or launches a mini webserver. Llamafile offers builds of Mixtral-8x7B-Instruct, so presumably they may package this one up as well (potentially a quantized format).
You would have to confirm with someone deeper in the ecosystem, but I think you should be able to run this new model as is against a llamafile?
[0] https://github.com/Mozilla-Ocho/llamafile
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Apple Explores Home Robotics as Potential 'Next Big Thing'
Thermostats: https://www.sinopetech.com/en/products/thermostat/
I haven't tried running a local text-to-speech engine backed by an LLM to control Home Assistant. Maybe someone is working on this already?
TTS: https://github.com/SYSTRAN/faster-whisper
LLM: https://github.com/Mozilla-Ocho/llamafile/releases
LLM: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-D...
It would take some tweaking to get the voice commands working correctly.
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LLaMA Now Goes Faster on CPUs
While I did not succeed in making the matmul code from https://github.com/Mozilla-Ocho/llamafile/blob/main/llamafil... work in isolation, I compared eigen, openblas, and mkl: https://gist.github.com/Dobiasd/e664c681c4a7933ef5d2df7caa87...
In this (very primitive!) benchmark, MKL was a bit better than eigen (~10%) on my machine (i5-6600).
Since the article https://justine.lol/matmul/ compared the new kernels with MLK, we can (by transitivity) compare the new kernels with Eigen this way, at least very roughly for this one use-case.
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Llamafile 0.7 Brings AVX-512 Support: 10x Faster Prompt Eval Times for AMD Zen 4
Yes, they're just ZIP files that also happen to be actually portable executables.
https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file...
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Show HN: I made an app to use local AI as daily driver
have you seen llamafile[0]?
[0] https://github.com/Mozilla-Ocho/llamafile
- FLaNK Stack 26 February 2024
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Gemma.cpp: lightweight, standalone C++ inference engine for Gemma models
llama.cpp has integrated gemma support. So you can use llamafile for this. It is a standalone executable that is portable across most popular OSes.
https://github.com/Mozilla-Ocho/llamafile/releases
So, download the executable from the releases page under assets. You want either just main or just server. Don't get the huge ones with the model inlined in the file. The executable is about 30MB in size,
https://github.com/Mozilla-Ocho/llamafile/releases/download/...
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Ollama releases OpenAI API compatibility
The improvements in ease of use for locally hosting LLMs over the last few months have been amazing. I was ranting about how easy https://github.com/Mozilla-Ocho/llamafile is just a few hours ago [1]. Now I'm torn as to which one to use :)
1: Quite literally hours ago: https://euri.ca/blog/2024-llm-self-hosting-is-easy-now/
What are some alternatives?
guidance - A guidance language for controlling large language models.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
ollama-webui - ChatGPT-Style WebUI for LLMs (Formerly Ollama WebUI) [Moved to: https://github.com/open-webui/open-webui]
json-schema-spec - The JSON Schema specification
langchain - 🦜🔗 Build context-aware reasoning applications
Constrained-Text-Genera
LLaVA - [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
torch-grammar
llama.cpp - LLM inference in C/C++
langroid - Harness LLMs with Multi-Agent Programming
safetensors - Simple, safe way to store and distribute tensors