Llama (language model)

Not to be confused with LaMDA.

Llama

Developer(s)	Meta AI
Initial release	February 24, 2023 (2023-02-24)
Stable release	Llama 3.2 / September 25, 2024 (2024-09-25)
Repository	github.com/meta-llama/llama-models
Written in	Python
Type	Large language model GPT Foundation model
License	Source available (Meta Llama 3.2 Community License)
Website	llama.com

Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023. The latest version is Llama 3.2, released in September 2024.

Model weights for the first version of Llama were made available to the research community under a non-commercial license, and access was granted on a case-by-case basis. Unauthorized copies of the model were shared via BitTorrent. In response, Meta AI issued DMCA takedown requests against repositories sharing the link on GitHub. Subsequent versions of Llama were made accessible outside academia and released under licenses that permitted some commercial use. Llama models are trained at different parameter sizes, ranging between 1B and 405B. Originally, Llama was only available as a foundation model. Starting with Llama 2, Meta AI started releasing instruction fine-tuned versions alongside foundation models.

Alongside the release of Llama 3, Meta added virtual assistant features to Facebook and WhatsApp in select regions, and a standalone website. Both services use a Llama 3 model.

Background

After the release of large language models such as GPT-3, a focus of research was up-scaling models which in some instances showed major increases in emergent capabilities. The release of ChatGPT and its surprise success caused an increase in attention to large language models.

Compared with other responses to ChatGPT, Meta's Chief AI scientist Yann LeCun stated that large language models are best for aiding with writing.

An empirical investigation of the Llama series was the scaling laws. It was observed that the Llama 3 models showed that when a model is trained on data that is more than the "Chinchilla-optimal" amount, the performance continues to scale log-linearly. For example, the Chinchilla-optimal dataset for Llama 3 8B is 200 billion tokens, but performance continued to scale log-linearly to the 75-times larger dataset of 15 trillion tokens.

Initial release

LLaMA was announced on February 24, 2023, via a blog post and a paper describing the model's training, architecture, and performance. The inference code used to run the model was publicly released under the open-source GPLv3 license. Access to the model's weights was managed by an application process, with access to be granted "on a case-by-case basis to academic researchers; those affiliated with organizations in government, civil society, and academia; and industry research laboratories around the world".

Llama was trained on only publicly available information, and was trained at various model sizes, with the intention to make it more accessible to different hardware.

Meta AI reported the 13B parameter model performance on most NLP benchmarks exceeded that of the much larger GPT-3 (with 175B parameters), and the largest 65B model was competitive with state of the art models such as PaLM and Chinchilla.

Leak

On March 3, 2023, a torrent containing LLaMA's weights was uploaded, with a link to the torrent shared on the 4chan imageboard and subsequently spread through online AI communities. That same day, a pull request on the main LLaMA repository was opened, requesting to add the magnet link to the official documentation. On March 4, a pull request was opened to add links to HuggingFace repositories containing the model. On March 6, Meta filed takedown requests to remove the HuggingFace repositories linked in the pull request, characterizing it as "unauthorized distribution" of the model. HuggingFace complied with the requests. On March 20, Meta filed a DMCA takedown request for copyright infringement against a repository containing a script that downloaded LLaMA from a mirror, and GitHub complied the next day.

Reactions to the leak varied. Some speculated that the model would be used for malicious purposes, such as more sophisticated spam. Some have celebrated the model's accessibility, as well as the fact that smaller versions of the model can be run relatively cheaply, suggesting that this will promote the flourishing of additional research developments. Multiple commentators, such as Simon Willison, compared LLaMA to Stable Diffusion, a text-to-image model which, unlike comparably sophisticated models which preceded it, was openly distributed, leading to a rapid proliferation of associated tools, techniques, and software.

Llama 2

On July 18, 2023, in partnership with Microsoft, Meta announced Llama 2, the next generation of Llama. Meta trained and released Llama 2 in three model sizes: 7, 13, and 70 billion parameters. The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. The accompanying preprint also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets.

Llama 2 includes foundation models and models fine-tuned for chat. In a further departure from LLaMA, all models are released with weights and are free for many commercial use cases. However, due to some remaining restrictions, Meta's description of LLaMA as open source has been disputed by the Open Source Initiative (known for maintaining the Open Source Definition).

Code Llama is a fine-tune of Llama 2 with code specific datasets. 7B, 13B, and 34B versions were released on August 24, 2023, with the 70B releasing on the January 29, 2024. Starting with the foundation models from Llama 2, Meta AI would train an additional 500B tokens of code datasets, before an additional 20B token of long-context data, creating the Code Llama foundation models. This foundation model was further trained on 5B instruction following token to create the instruct fine-tune. Another foundation model was created for Python code, which trained on 100B tokens of Python-only code, before the long-context data.

Llama 3

On April 18, 2024, Meta released Llama-3 with two sizes: 8B and 70B parameters. The models have been pre-trained on approximately 15 trillion tokens of text gathered from “publicly available sources” with the instruct models fine-tuned on “publicly available instruction datasets, as well as over 10M human-annotated examples". Meta AI's testing showed in April 2024 that Llama 3 70B was beating Gemini pro 1.5 and Claude 3 Sonnet on most benchmarks. Meta also announced plans to make Llama 3 multilingual and multimodal, better at coding and reasoning, and to increase its context window.

During an interview with Dwarkesh Patel, Mark Zuckerberg said that the 8B version of Llama 3 was nearly as powerful as the largest Llama 2. Compared to previous models, Zuckerberg stated the team was surprised that the 70B model was still learning even at the end of the 15T tokens training. The decision was made to end training to focus GPU power elsewhere.

Llama-3.1 was released on July 23, 2024, with three sizes: 8B, 70B, and 405B parameters.

Comparison of models

For the training cost column, only the largest model's cost is written. So for example, "21,000" is the training cost of Llama 2 69B in units of petaFLOP-day. Also, 1 petaFLOP-day = 1 petaFLOP/sec × 1 day = 8.64E19 FLOP. "T" means "trillion" and "B" means "billion".

Name	Release date	Parameters	Training cost (petaFLOP-day)	Context length (tokens)	Corpus size (tokens)	Commercial viability?
LLaMA	February 24, 2023	6.7B 13B 32.5B 65.2B	6,300	2048	1–1.4T	No
Llama 2	July 18, 2023	6.7B 13B 69B	21,000	4096	2T	Yes
Code Llama	August 24, 2023	6.7B 13B 33.7B 69B
Llama 3	April 18, 2024	8B 70.6B	100,000	8192	15T
Llama 3.1	July 23, 2024	8B 70.6B 405B	440,000	128,000
Llama 3.2	September 25, 2024	1B 3B 11B 90B		128,000

Architecture and training

"recommendation letter for the Magic Unicorn Corporation"

Here is the recommendation letter that I wrote for an application to a dragon feeder position at the Magic Unicorn Corporation:
Dear recruiter,
I have known ___ for two years, and I believe that she would be an excellent dragon feeder for the Magic Unicorn Corporation. ___ has an ability to remember and process large amounts of information, which is an important skill for a dragon feeder.
___, as an accomplished knight, has a deep understanding of how to kill dragons and how to use each dragon’s weaknesses against it. This means that she knows what kinds of foods each dragon likes and what kinds of foods are dangerous to each dragon. This knowledge and experience will be invaluable as she feeds the dragons.
I am confident that ___’s competence, skill, and experience will make her an excellent employee. Please contact me at (___) ___-___ if you have any questions. I look forward to hearing from you.
Best regards,
Honorable Knight
Sir George

– Output of 65 billion parameter LLaMA model before instruction tuning, given the prompt (in bold)

Architecture

Like GPT-3, the Llama series of models are decoder-only Transformers, but there are some minor differences:

• SwiGLU activation function instead of GeLU;
• rotary positional embeddings (RoPE) instead of absolute positional embedding;
• RMSNorm instead of layer normalization;

key hyperparameters of Llama 3.1

	8B	70B	405B
Layers	32	80	126
Model Dimension	4,096	8192	16,384
FFN Dimension	14,336	28,672	53,248
Attention Heads	32	64	128
Key/Value Heads	8	8	8
Peak Learning Rate	3 × 10−4	1.5 × 10−4	0.8 × 10−4
Activation Function	SwiGLU
Vocabulary Size	128,000
Positional Embeddings	$\operatorname {RoPE} (\theta =500,000)$

Training datasets

LLaMA's developers focused their effort on scaling the model's performance by increasing the volume of training data, rather than the number of parameters, reasoning that the dominating cost for LLMs is from doing inference on the trained model rather than the computational cost of the training process.

LLaMA 1 foundational models were trained on a data set with 1.4 trillion tokens, drawn from publicly available data sources, including:

• Webpages scraped by CommonCrawl
• Open source repositories of source code from GitHub
• Wikipedia in 20 languages
• Public domain books from Project Gutenberg
• Books3 books dataset
• The LaTeX source code for scientific papers uploaded to ArXiv
• Questions and answers from Stack Exchange websites

On April 17, 2023, TogetherAI launched a project named RedPajama to reproduce and distribute an open source version of the LLaMA dataset. The dataset has approximately 1.2 trillion tokens and is publicly available for download.

Llama 2 foundational models were trained on a data set with 2 trillion tokens. This data set was curated to remove Web sites that often disclose personal data of people. It also upsamples sources considered trustworthy. Llama 2 - Chat was additionally fine-tuned on 27,540 prompt-response pairs created for this project, which performed better than larger but lower-quality third-party datasets. For AI alignment, reinforcement learning with human feedback (RLHF) was used with a combination of 1,418,091 Meta examples and seven smaller datasets. The average dialog depth was 3.9 in the Meta examples, 3.0 for Anthropic Helpful and Anthropic Harmless sets, and 1.0 for five other sets, including OpenAI Summarize, StackExchange, etc.

Llama 3 consists of mainly English data, with over 5% in over 30 other languages. Its dataset was filtered by a text-quality classifier, and the classifier was trained by text synthesized by Llama 2.

Fine-tuning

Llama 1 models are only available as foundational models with self-supervised learning and without fine-tuning. Llama 2 – Chat models were derived from foundational Llama 2 models. Unlike GPT-4 which increased context length during fine-tuning, Llama 2 and Code Llama - Chat have the same context length of 4K tokens. Supervised fine-tuning used an autoregressive loss function with token loss on user prompts zeroed out. The batch size was 64.

For AI alignment, human annotators wrote prompts and then compared two model outputs (a binary protocol), giving confidence levels and separate safety labels with veto power. Two separate reward models were trained from these preferences for safety and helpfulness using Reinforcement learning from human feedback (RLHF). A major technical contribution is the departure from the exclusive use of Proximal Policy Optimization (PPO) for RLHF – a new technique based on Rejection sampling was used, followed by PPO.

Multi-turn consistency in dialogs was targeted for improvement, to make sure that "system messages" (initial instructions, such as "speak in French" and "act like Napoleon") are respected during the dialog. This was accomplished using the new "Ghost attention" technique during training, which concatenates relevant instructions to each new user message but zeros out the loss function for tokens in the prompt (earlier parts of the dialog).

Applications

The Stanford University Institute for Human-Centered Artificial Intelligence (HAI) Center for Research on Foundation Models (CRFM) released Alpaca, a training recipe based on the LLaMA 7B model that uses the "Self-Instruct" method of instruction tuning to acquire capabilities comparable to the OpenAI GPT-3 series text-davinci-003 model at a modest cost. The model files were officially removed on March 21, 2023, over hosting costs and safety concerns, though the code and paper remain online for reference.

Meditron is a family of Llama-based finetuned on a corpus of clinical guidelines, PubMed papers, and articles. It was created by researchers at École Polytechnique Fédérale de Lausanne School of Computer and Communication Sciences, and the Yale School of Medicine. It shows increased performance on medical-related benchmarks such as MedQA and MedMCQA.

Zoom used Meta Llama 2 to create an AI Companion that can summarize meetings, provide helpful presentation tips, and assist with message responses. This AI Companion is powered by multiple models, including Meta Llama 2.

Reuters reported in 2024 that many Chinese foundation models relied on Llama models for their training.

llama.cpp

Main article: llama.cpp

Software developer Georgi Gerganov released llama.cpp as open-source on March 10, 2023. It's a re-implementation of LLaMA in C++, allowing systems without a powerful GPU to run the model locally. The llama.cpp project introduced the GGUF file format, a binary format that stores both tensors and metadata. The format focuses on supporting different quantization types, which can reduce memory usage, and increase speed at the expense of lower model precision.

llamafile created by Justine Tunney is an open-source tool that bundles llama.cpp with the model into a single executable file. Tunney et al. introduced new optimized matrix multiplication kernels for x86 and ARM CPUs, improving prompt evaluation performance for FP16 and 8-bit quantized data types.

Military

In 2024, researchers from the People's Liberation Army Academy of Military Sciences were reported to have developed a military tool using Llama, which Meta Platforms stated was unauthorized due to its model use prohibition for military purposes.

Reception

Wired describes the 8B parameter version of Llama 3 as being "surprisingly capable" given its size.

The response to Meta's integration of Llama into Facebook was mixed, with some users confused after Meta AI told a parental group that it had a child.

According to the Q4 2023 Earnings transcript, Meta adopted the strategy of open weights to improve on model safety, iteration speed, increase adoption among developers and researchers, and to become the industry standard. Llama 5, 6, and 7 are planned for the future.

The release of Llama models has sparked significant debates on the benefits and misuse risks of open weight models. Such models can be fine-tuned to remove safeguards, notably by cyber criminals, until they comply with harmful requests. Some experts contend that future models may facilitate causing damage more than defending against it, for example by making it relatively easy to engineer advanced bioweapons without specialized knowledge. Conversely, open-weight models can be useful for a wide variety of purposes, including for safety research. Open Source Initiative head Stefano Maffulli criticized Meta for describing Llama as open source, saying that it was causing confusion among users and "polluting" the term.

See also

• Mistral AI
• GPT-4o

Further reading

• Huang, Kalley; O'Regan, Sylvia Varnham (September 5, 2023). "Inside Meta's AI Drama: Internal Feuds Over Compute Power". The Information. Archived from the original on September 5, 2023. Retrieved September 6, 2023.

External links

• Official website
• Official Hugging Face organization for Llama, Llama Guard, and Prompt Guard models