llm
ad-llama
llm | ad-llama | |
---|---|---|
23 | 6 | |
2,991 | 47 | |
- | - | |
9.4 | 8.9 | |
6 days ago | about 1 month ago | |
Python | TypeScript | |
Apache License 2.0 | MIT License |
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llm
- FLaNK AI-April 22, 2024
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Show HN: I made a tool to clean and convert any webpage to Markdown
That's a great use case, you might be able to do this if you've got a copy and paste on the command line with
https://github.com/simonw/llm
In between. An alias like pdfwtf translating to "paste | llm command | copy"
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Command R+: A Scalable LLM Built for Business
I added support for this model to my LLM CLI tool via a new plugin: https://github.com/simonw/llm-command-r
So now you can do this:
pipx install llm
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The Next Generation of Claude (Claude 3)
If you're willing to use the CLI, Simon Willison's llm library[0] should do the trick.
[0] https://github.com/simonw/llm
- Show HN: I made an app to use local AI as daily driver
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Localllm lets you develop gen AI apps on local CPUs
I'm not thrilled about https://github.com/GoogleCloudPlatform/localllm/blob/main/ll... calling their Python package "llm" and installing "llm" as a CLI command, when my similar https://llm.datasette.io/ project has that namespace reserved on PyPI already: https://pypi.org/project/llm/
- FLaNK 15 Jan 2024
- Show HN: Simple Script for Enhanced LLM Interaction in Vim
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Bash One-Liners for LLMs
I've been gleefully exploring the intersection of LLMs and CLI utilities for a few months now - they are such a great fit for each other! The unix philosophy of piping things together is a perfect fit for how LLMs work.
I've mostly been exploring this with my https://llm.datasette.io/ CLI tool, but I have a few other one-off tools as well: https://github.com/simonw/blip-caption and https://github.com/simonw/ospeak
I'm puzzled that more people aren't loudly exploring this space (LLM+CLI) - it's really fun.
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Semantic Kernel
Seems nice if you're using c# or java. It also supports python, but for that Simon's llm library is nice because he designed it as both a library and a command line tool: https://github.com/simonw/llm
ad-llama
- Show HN: A murder mystery game built on an open-source gen-AI agent framework
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Guidance: A guidance language for controlling large language models
I took a stab at making something[1] like guidance - I'm not sure exactly how guidance does it (and I'm also really curious how it would work with chat api's) but here's how my solution works.
Each expression becomes a new inference request, so it's not a single inference pass. Because each subsequent pass includes the previously inferenced text, the LLM ends up doing a lot of prefill and less decode. You only decode as much as you actually inference, the repeated passes only end up costing more in prefill (which tend to be much faster tok/s).
To work with chat tuned instruction models, you can basically still treat it as a completion model. I provide the previously completed inference text as a partially completed assistant response, e.g. with llama 2 it goes after [/INST]. You can add a bit of instruction for each inference expression which gets added to the [INST]. This approach lets you start off the inference with `{ "someField": "` for example to guarantee (at least the start of) a json response and allow you to add a little bit of instruction or context just for that field.
I didn't even try with openai api's since afaict you can't provide a partial assistant response for it to continue from. Even if you were to request a single token at a time and use logit_bias for biased sampling, I don't see how you can get it to continue a partially completed inference.
[1] https://github.com/gsuuon/ad-llama
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Simulating History with ChatGPT
Can you point me to some text-adventure engines? I'm hacking on an in-browser local llm structured inference library[1] and am trying to put together a text game demo[2] for it. It didn't even occur to me that text-adventure game engines exist, I was apparently re-inventing the wheel.
[1] https://github.com/gsuuon/ad-llama
[2] https://ad-llama.vercel.app/murder/
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Ask HN: Which programming language to learn in AI era?
Yup, I'm building a library that runs LLM's in browser with tagged template literals: https://github.com/gsuuon/ad-llama
I think it has fundamental DX benefits over python for complex prompt chaining (or I wouldn't be building it!) Even still -- if their focus is purely on AI, python is still the better choice starting from scratch. The python AI ecosystem has many more libraries, stack overflow answers, tutorials, etc available.
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Show HN: LLMs can generate valid JSON 100% of the time
Generating an FSM over the vocabulary is a really interesting approach to guided sampling! I'm hacking on a structured inference library (https://github.com/gsuuon/ad-llama) - I also tried to add a vocab preprocessing step to generate a valid tokens mask (just with regex or static strings initially) but discovered that doing so would cause unlikely / unnatural tokens to be masked rather than the token which represents the natural encoding given the existing sampled tokens.
Given the stateful nature of tokenizers, I decided that trying to preprocess the individual token ids was a losing battle. Even in the simple case of whitespace - tokenizer merges can really screw up generating a static mask, e.g. we expect a space next, but a token decodes to 'foo', but is actually a '_foo' and would've decoded with a whitespace if it were following a valid pair. When I go to construct the static vocab mask, it would then end up matching against 'foo' instead of ' foo'.
How did you work around this for the FSM approach? Does it somehow include information about merges / whitespace / tokenizer statefulness?
What are some alternatives?
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
grontown - A murder mystery featuring generative agents
langroid - Harness LLMs with Multi-Agent Programming
llm-mlc - LLM plugin for running models using MLC
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
eastworld - Framework for Generative Agents in Games
multi-gpt - A Clojure interface into the GPT API with advanced tools like conversational memory, task management, and more
hof - Framework that joins data models, schemas, code generation, and a task engine. Language and technology agnostic.
jehuty - Fluent API to interact with chat based GPT model
outlines - Structured Text Generation
llm-replicate - LLM plugin for models hosted on Replicate
api - Structured LLM APIs