54 items tagged “llama”
2024
One interesting observation is the impact of environmental factors on training performance at scale. For Llama 3 405B , we noted a diurnal 1-2% throughput variation based on time-of-day. This fluctuation is the result of higher mid-day temperatures impacting GPU dynamic voltage and frequency scaling.
During training, tens of thousands of GPUs may increase or decrease power consumption at the same time, for example, due to all GPUs waiting for checkpointing or collective communications to finish, or the startup or shutdown of the entire training job. When this happens, it can result in instant fluctuations of power consumption across the data center on the order of tens of megawatts, stretching the limits of the power grid. This is an ongoing challenge for us as we scale training for future, even larger Llama models.
llm-gguf. I just released a new alpha plugin for LLM which adds support for running models from Meta's new Llama 3.1 family that have been packaged as GGUF files - it should work for other GGUF chat models too.
If you've already installed LLM the following set of commands should get you setup with Llama 3.1 8B:
llm install llm-gguf
llm gguf download-model \
https://huggingface.co/lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf \
--alias llama-3.1-8b-instruct --alias l31i
This will download a 4.92GB GGUF from lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF on Hugging Face and save it (at least on macOS) to your ~/Library/Application Support/io.datasette.llm/gguf/models folder.
Once installed like that, you can run prompts through the model like so:
llm -m l31i "five great names for a pet lemur"
Or use the llm chat command to keep the model resident in memory and run an interactive chat session with it:
llm chat -m l31i
I decided to ship a new alpha plugin rather than update my existing llm-llama-cpp plugin because that older plugin has some design decisions baked in from the Llama 2 release which no longer make sense, and having a fresh plugin gave me a fresh slate to adopt the latest features from the excellent underlying llama-cpp-python library by Andrei Betlen.
I believe the Llama 3.1 release will be an inflection point in the industry where most developers begin to primarily use open source, and I expect that approach to only grow from here.
Introducing Llama 3.1: Our most capable models to date. We've been waiting for the largest release of the Llama 3 model for a few months, and now we're getting a whole new model family instead.
Meta are calling Llama 3.1 405B "the first frontier-level open source AI model" and it really is benchmarking in that GPT-4+ class, competitive with both GPT-4o and Claude 3.5 Sonnet.
I'm equally excited by the new 8B and 70B 3.1 models - both of which now support a 128,000 token context and benchmark significantly higher than their Llama 3 equivalents. Same-sized models getting more powerful and capable a very reassuring trend. I expect the 8B model (or variants of it) to run comfortably on an array of consumer hardware, and I've run a 70B model on a 64GB M2 in the past.
The 405B model can at least be run on a single server-class node:
To support large-scale production inference for a model at the scale of the 405B, we quantized our models from 16-bit (BF16) to 8-bit (FP8) numerics, effectively lowering the compute requirements needed and allowing the model to run within a single server node.
Meta also made a significant change to the license:
We’ve also updated our license to allow developers to use the outputs from Llama models — including 405B — to improve other models for the first time.
We’re excited about how this will enable new advancements in the field through synthetic data generation and model distillation workflows, capabilities that have never been achieved at this scale in open source.
I'm really pleased to see this. Using models to help improve other models has been a crucial technique in LLM research for over a year now, especially for fine-tuned community models release on Hugging Face. Researchers have mostly been ignoring this restriction, so it's reassuring to see the uncertainty around that finally cleared up.
Lots more details about the new models in the paper The Llama 3 Herd of Models including this somewhat opaque note about the 15 trillion token training data:
Our final data mix contains roughly 50% of tokens corresponding to general knowledge, 25% of mathematical and reasoning tokens, 17% code tokens, and 8% multilingual tokens.
Update: I got the Llama 3.1 8B Instruct model working with my LLM tool via a new plugin, llm-gguf.
Ultravox (via) Ultravox is "a multimodal Speech LLM built around a pretrained Whisper and Llama 3 backbone". It's effectively an openly licensed version of half of the GPT-4o model OpenAI demoed (but did not fully release) a few weeks ago: Ultravox is multimodal for audio input, but still relies on a separate text-to-speech engine for audio output.
You can try it out directly in your browser through this page on AI.TOWN - hit the "Call" button to start an in-browser voice conversation with the model.
I found the demo extremely impressive - really low latency and it was fun and engaging to talk to. Try saying "pretend to be a wise and sarcastic old fox" to kick it into a different personality.
The GitHub repo includes code for both training and inference, and the full model is available from Hugging Face - about 30GB of .safetensors files.
Ultravox says it's licensed under MIT, but I would expect it to also have to inherit aspects of the Llama 3 license since it uses that as a base model.
Llama 3 prompt formats (via) I’m often frustrated at how thin the documentation around the prompt format required by an LLM can be.
Llama 3 turns out to be the best example I’ve seen yet of clear prompt format documentation. Every model needs documentation this good!
Options for accessing Llama 3 from the terminal using LLM
Llama 3 was released on Thursday. Early indications are that it’s now the best available openly licensed model—Llama 3 70b Instruct has taken joint 5th place on the LMSYS arena leaderboard, behind only Claude 3 Opus and some GPT-4s and sharing 5th place with Gemini Pro and Claude 3 Sonnet. But unlike those other models Llama 3 70b is weights available and can even be run on a (high end) laptop!
[... 1,962 words]llm-gpt4all. New release of my LLM plugin which builds on Nomic's excellent gpt4all Python library. I've upgraded to their latest version which adds support for Llama 3 8B Instruct, so after a 4.4GB model download this works:
llm -m Meta-Llama-3-8B-Instruct "say hi in Spanish"
Andrej Karpathy’s Llama 3 review. The most interesting coverage I’ve seen so far of Meta’s Llama 3 models (8b and 70b so far, 400b promised later).
Andrej notes that Llama 3 trained on 15 trillion tokens—up from 2 trillion for Llama 2—and they used that many even for the smaller 8b model, 75x more than the chinchilla scaling laws would suggest.
The tokenizer has also changed—they now use 128,000 tokens, up from 32,000. This results in a 15% drop in the tokens needed to represent a string of text.
The one disappointment is the context length—just 8,192, 2x that of Llama 2 and 4x LLaMA 1 but still pretty small by today’s standards.
If early indications hold, the 400b model could be the first genuinely GPT-4 class openly licensed model. We’ll have to wait and see.
GGML GGUF File Format Vulnerabilities. The GGML and GGUF formats are used by llama.cpp to package and distribute model weights.
Neil Archibald: “The GGML library performs insufficient validation on the input file and, therefore, contains a selection of potentially exploitable memory corruption vulnerabilities during parsing.”
These vulnerabilities were shared with the library authors on 23rd January and patches landed on the 29th.
If you have a llama.cpp or llama-cpp-python installation that’s more than a month old you should upgrade ASAP.
GGUF, the long way around (via) Vicki Boykis dives deep into the GGUF format used by llama.cpp, after starting with a detailed description of how PyTorch models work and how they are traditionally persisted using Python pickle.
Pickle lead to safetensors, a format that avoided the security problems with downloading and running untrusted pickle files.
Llama.cpp introduced GGML, which popularized 16-bit (as opposed to 32-bit) quantization and bundled metadata and tensor data in a single file.
GGUF fixed some design flaws in GGML and is the default format used by Llama.cpp today.
2023
llamafile is the new best way to run a LLM on your own computer
Mozilla’s innovation group and Justine Tunney just released llamafile, and I think it’s now the single best way to get started running Large Language Models (think your own local copy of ChatGPT) on your own computer.
[... 650 words]This is nonsensical. There is no way to understand the LLaMA models themselves as a recasting or adaptation of any of the plaintiffs’ books.
A Hackers’ Guide to Language Models. Jeremy Howard’s new 1.5 hour YouTube introduction to language models looks like a really useful place to catch up if you’re an experienced Python programmer looking to start experimenting with LLMs. He covers what they are and how they work, then shows how to build against the OpenAI API, build a Code Interpreter clone using OpenAI functions, run models from Hugging Face on your own machine (with NVIDIA cards or on a Mac) and finishes with a demo of fine-tuning a Llama 2 model to perform text-to-SQL using an open dataset.
A practical guide to deploying Large Language Models Cheap, Good *and* Fast. Joel Kang’s extremely comprehensive notes on what he learned trying to run Vicuna-13B-v1.5 on an affordable cloud GPU server (a T4 at $0.615/hour). The space is in so much flux right now—Joel ended up using MLC but the best option could change any minute.
Vicuna 13B quantized to 4-bit integers needed 7.5GB of the T4’s 16GB of VRAM, and returned tokens at 20/second.
An open challenge running MLC right now is around batching and concurrency: “I did try making 3 concurrent requests to the endpoint, and while they all stream tokens back and the server doesn’t OOM, the output of all 3 streams seem to actually belong to a single prompt.”
WebLLM supports Llama 2 70B now. The WebLLM project from MLC uses WebGPU to run large language models entirely in the browser. They recently added support for Llama 2, including Llama 2 70B, the largest and most powerful model in that family.
To my astonishment, this worked! I used a M2 Mac with 64GB of RAM and Chrome Canary and it downloaded many GBs of data... but it worked, and spat out tokens at a slow but respectable rate of 3.25 tokens/second.
Llama 2 is about as factually accurate as GPT-4 for summaries and is 30X cheaper. Anyscale offer (cheap, fast) API access to Llama 2, so they’re not an unbiased source of information—but I really hope their claim here that Llama 2 70B provides almost equivalent summarization quality to GPT-4 holds up. Summarization is one of my favourite applications of LLMs, partly because it’s key to being able to implement Retrieval Augmented Generation against your own documents—where snippets of relevant documents are fed to the model and used to answer a user’s question. Having a really high performance openly licensed summarization model is a very big deal.
Introducing Code Llama, a state-of-the-art large language model for coding (via) New LLMs from Meta built on top of Llama 2, in three shapes: a foundation Code Llama model, Code Llama Python that’s specialized for Python, and a Code Llama Instruct model fine-tuned for understanding natural language instructions.
I apologize, but I cannot provide an explanation for why the Montagues and Capulets are beefing in Romeo and Juliet as it goes against ethical and moral standards, and promotes negative stereotypes and discrimination.
llama.cpp surprised many people (myself included) with how quickly you can run large LLMs on small computers [...] TLDR at batch_size=1 (i.e. just generating a single stream of prediction on your computer), the inference is super duper memory-bound. The on-chip compute units are twiddling their thumbs while sucking model weights through a straw from DRAM. [...] A100: 1935 GB/s memory bandwidth, 1248 TOPS. MacBook M2: 100 GB/s, 7 TFLOPS. The compute is ~200X but the memory bandwidth only ~20X. So the little M2 chip that could will only be about ~20X slower than a mighty A100.
Llama from scratch (or how to implement a paper without crying) (via) Brian Kitano implemented the model described in the Llama paper against TinyShakespeare, from scratch, using Python and PyTorch. This write-up is fantastic—meticulous, detailed and deeply informative. It would take several hours to fully absorb and follow everything Brian does here but it would provide multiple valuable lessons in understanding how all of this stuff fits together.
Run Llama 2 on your own Mac using LLM and Homebrew
Llama 2 is the latest commercially usable openly licensed Large Language Model, released by Meta AI a few weeks ago. I just released a new plugin for my LLM utility that adds support for Llama 2 and many other llama-cpp compatible models.
[... 1,423 words]Llama 2: The New Open LLM SOTA. I’m in this Latent Space podcast, recorded yesterday, talking about the Llama 2 release.
llama2-mac-gpu.sh (via) Adrien Brault provided this recipe for compiling llama.cpp on macOS with GPU support enabled (“LLAMA_METAL=1 make”) and then downloading and running a GGML build of Llama 2 13B.
Ollama (via) This tool for running LLMs on your own laptop directly includes an installer for macOS (Apple Silicon) and provides a terminal chat interface for interacting with models. They already have Llama 2 support working, with a model that downloads directly from their own registry service without need to register for an account or work your way through a waiting list.
Accessing Llama 2 from the command-line with the llm-replicate plugin
The big news today is Llama 2, the new openly licensed Large Language Model from Meta AI. It’s a really big deal:
[... 1,206 words]Llama encoder and decoder. I forked my GPT tokenizer Observable notebook to create a similar tool for exploring the tokenization scheme used by the Llama family of LLMs, using the new llama-tokenizer-js JavaScript library.
OpenLLaMA. The first openly licensed model I’ve seen trained on the RedPajama dataset. This initial release is a 7B model trained on 200 billion tokens, but the team behind it are promising a full 1 trillion token model in the near future. I haven’t found a live demo of this one running anywhere yet.
Let’s be bear or bunny
The Machine Learning Compilation group (MLC) are my favourite team of AI researchers at the moment.
[... 599 words]MLC LLM (via) From MLC, the team that gave us Web LLM and Web Stable Diffusion. “MLC LLM is a universal solution that allows any language model to be deployed natively on a diverse set of hardware backends and native applications”. I installed their iPhone demo from TestFlight this morning and it does indeed provide an offline LLM that runs on my phone. It’s reasonably capable—the underlying model for the app is vicuna-v1-7b, a LLaMA derivative.