Items tagged llama in 2024
Filters: Year: 2024 × llama × Sorted by date
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! # 1st May 2024, 6:32 pm
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!
[... 1962 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" # 20th April 2024, 5:58 pm
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. # 18th April 2024, 8:50 pm
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. # 26th March 2024, 6:47 am
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. # 29th February 2024, 9:39 pm