llm
llama
llm | llama | |
---|---|---|
23 | 184 | |
2,991 | 53,371 | |
- | 3.0% | |
9.4 | 8.1 | |
5 days ago | 12 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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
- 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
llama
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Mark Zuckerberg: Llama 3, $10B Models, Caesar Augustus, Bioweapons [video]
derivative works thereof).”
https://github.com/meta-llama/llama/blob/b8348da38fde8644ef0...
Also even if you did use Llama for something, they could unilaterally pull the rug on you when you got 700 million years, AND anyone who thinks Meta broke their copyright loses their license. (Checking if you are still getting screwed is against the rules)
Therefore, Zuckerberg is accountable for explicitly anticompetitive conduct, I assumed an MMA fighter would appreciate the value of competition, go figure.
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Hello OLMo: A Open LLM
One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.
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Reaching LLaMA2 Performance with 0.1M Dollars
It looks like Llama 2 7B took 184,320 A100-80GB GPU-hours to train[1]. This one says it used a 96×H100 GPU cluster for 2 weeks, for 32,256 hours. That's 17.5% of the number of hours, but H100s are faster than A100s [2] and FP16/bfloat16 performance is ~3x better.
If they had tried to replicate Llama 2 identically with their hardware setup, it'd cost a little bit less than twice their MoE model.
[1] https://github.com/meta-llama/llama/blob/main/MODEL_CARD.md#...
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DBRX: A New Open LLM
Ironically, the LLaMA license text [1] this is lifted verbatim from is itself copyrighted [2] and doesn't grant you the permission to copy it or make changes like s/meta/dbrx/g lol.
[1] https://github.com/meta-llama/llama/blob/main/LICENSE#L65
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How Chain-of-Thought Reasoning Helps Neural Networks Compute
This is kind of an epistemological debate at this level, and I make an effort to link to some source code [1] any time it seems contentious.
LLMs (of the decoder-only, generative-pretrained family everyone means) are next token predictors in a literal implementation sense (there are some caveats around batching and what not, but none that really matter to the philosophy of the thing).
But, they have some emergent behaviors that are a trickier beast. Probably the best way to think about a typical Instruct-inspired “chat bot” session is of them sampling from a distribution with a KL-style adjacency to the training corpus (sidebar: this is why shops that do and don’t train/tune on MMLU get ranked so differently than e.g. the arena rankings) at a response granularity, the same way a diffuser/U-net/de-noising model samples at the image batch (NCHW/NHWC) level.
The corpus is stocked with everything from sci-fi novels with computers arguing their own sentience to tutorials on how to do a tricky anti-derivative step-by-step.
This mental model has adequate explanatory power for anything a public LLM has ever been shown to do, but that only heavily implies it’s what they’re doing.
There is active research into whether there is more going on that is thus far not conclusive to the satisfaction of an unbiased consensus. I personally think that research will eventually show it’s just sampling, but that’s a prediction not consensus science.
They might be doing more, there is some research that represents circumstantial evidence they are doing more.
[1] https://github.com/meta-llama/llama/blob/54c22c0d63a3f3c9e77...
- Asking Meta to stop using the term "open source" for Llama
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Markov Chains Are the Original Language Models
Predicting subsequent text is pretty much exactly what they do. Lots of very cool engineering that’s a real feat, but at its core it’s argmax(P(token|token,corpus)):
https://github.com/facebookresearch/llama/blob/main/llama/ge...
The engineering feats are up there with anything, but it’s a next token predictor.
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Meta AI releases Code Llama 70B
https://github.com/facebookresearch/llama/pull/947/
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Stuff we figured out about AI in 2023
> Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!
actually its not just a basic version. Llama 1/2's model.py is 500 lines: https://github.com/facebookresearch/llama/blob/main/llama/mo...
Mistral (is rumored to have) forked llama and is 369 lines: https://github.com/mistralai/mistral-src/blob/main/mistral/m...
and both of these are SOTA open source models.
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[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
In transformers, they tried really hard to have a single function or method to deal with both self and cross attention mechanisms, masking, positional and relative encodings, interpolation etc. While it allows a user to use the same function/method for any model, it has led to severe parameter bloat. Just compare the original implementation of llama by FAIR with the implementation by HF to get an idea.
What are some alternatives?
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
langroid - Harness LLMs with Multi-Agent Programming
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
chatgpt-vscode - A VSCode extension that allows you to use ChatGPT
multi-gpt - A Clojure interface into the GPT API with advanced tools like conversational memory, task management, and more
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
jehuty - Fluent API to interact with chat based GPT model
llm-replicate - LLM plugin for models hosted on Replicate
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.