Llama-3

10 things that you need to know

1. Overview The development and performance of Large Language Models (LLMs), particularly focusing on the Llama series:

• Scaling LLMs: It’s noted that LLMs show improved task performance when scaled up, but recent studies suggest smaller models trained on more data can be more effective within a given compute budget.
• Inference Efficiency: The text emphasizes the importance of inference efficiency over training speed, suggesting that smaller models trained longer can be more cost-effective at inference. Llama Models: The Llama series, ranging from 7B to 65B parameters, is introduced12. These models are trained on more tokens than usual and are designed to perform well within various inference budgets.
• Performance Benchmark: Specifically, the LlamA-13B model is highlighted for outperforming GPT-3 on most benchmarks while being significantly smaller, suggesting it could democratize access to LLMs due to its ability to run on a single GPU.

1. Training-Dataset Here are the key takeaways:

• Data Sources: The training dataset includes a mix of several sources such as CommonCrawl, Wikipedia, GitHub, arXiv, and book corpora, ensuring a wide coverage of domains.
• Data Processing: Techniques like deduplication, language identification, quality filtering, and removal of non-essential content were applied to improve data quality.
• Tokenization: The byte-pair encoding (BPE) algorithm was used for tokenization, with special handling for numbers and unknown UTF-8 characters.
• Dataset Size: After tokenization, the entire training dataset contains roughly 1.4 trillion tokens, with most tokens used only once during training.

1. Architecture The architecture of a series of large language models (LLMs) called Llama, focusing on the improvements made to the transformer architecture:

• Pre-normalization: The input of each transformer sub-layer is normalized using the RMSNorm function for better training stability, inspired by GPT-31. SwiGLU Activation: ReLU is replaced with SwiGLU activation function to enhance performance, with a dimension of (3/2)​4d

instead of

4d

following PaLM’s approach.

• Rotary Embeddings: Absolute positional embeddings are removed and replaced with rotary positional embeddings (RoPE) at each network layer, an idea taken from GPTNeo.

Additionally, the text mentions that the hyper-parameters for the models are detailed in Table 2, and Figure 1 shows the training loss over train tokens for models with different parameter sizes. The larger models (LLaMA-33B and LLaMA-65B) were trained on 1.4 trillion tokens, while the smaller ones on 1.0 trillion tokens, all with a batch size of 4 million tokens.

1. Optimizer The AdamW optimizer for training language models, specifically the LLaMA models. Here are the key takeaways:

• Optimizer Choice: The AdamW optimizer is utilized, known for its effectiveness in deep learning tasks. Hyper-parameters: It employs specific hyper-parameters like β1​=0.9

&

β2​=0.95

with a cosine learning rate schedule.

• Efficiency: The optimizer contributes to the training efficiency, allowing the models to process a significant number of tokens per second per GPU.
• Training Scale: It supports the training of models with up to 65 billion parameters, processing around 380 tokens/sec/GPU on 2048 A100 GPUs.
• The optimizer plays a crucial role in the training process, impacting the speed and performance of the resulting language models.

1. Efficient Implementation Here are the key points from the current page discussing the efficient implementation of Llama, a collection of foundation language models:

• Model Range: LlamA includes models ranging from 7B to 65B parameters, trained on trillions of tokens using publicly available datasets1.
• Performance: Llama-13B outperforms GPT-3 despite being smaller, and Llama-65B competes with larger models like Chinchilla-70B and PaLM-540B.
• Training Data: The training dataset is a mix of sources such as CommonCrawl, Wikipedia, and GitHub, ensuring diversity and public availability.
• Optimizations: Several optimizations were made to the transformer architecture and training methods to improve stability, performance, and efficiency.

1. Common Sense Reasoning Benchmark The selected text discusses the evaluation of the Llama language models on common sense reasoning benchmarks. Here are the key takeaways:

Benchmarks Used: The evaluation includes eight benchmarks such as BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, and OpenBookQA, which involve tasks like Cloze, Winograd, and multiple choice question answering.
Zero-Shot Setting: The models are evaluated in a zero-shot setting, a method used in language modeling where the model generates answers without prior exposure to the specific task2.
Llama Performance: LLaMA-65B outperforms Chinchilla-70B on all benchmarks except BoolQ and surpasses PaLM-540B on all but BoolQ and WinoGrande. The smaller LLaMA-13B model also outperforms GPT-3 on most benchmarks despite being significantly smaller in size.

1. Closed Book Questions Answering The selected text discusses Closed-book Question Answering performance of Llama models:

Llama vs. Other Models: Llama models are compared with other large language models on two benchmarks: Natural Questions and TriviaQA.
Exact Match Performance: LLaMA-65B achieves state-of-the-art performance in both zero-shot and few-shot settings.
LLaMA-13B’s Competitiveness: Despite being 5–10 times smaller, LLaMA-13B competes well with GPT-3 and Chinchilla on these benchmarks.
Inference Capability: LLaMA-13B can run on a single V100 GPU during inference, highlighting its efficiency.
Benchmark Overview: The RACE benchmark consists of English reading comprehension exams for middle and high school Chinese students.
Evaluation Protocol: The evaluation follows the setup from Brown et al. (2020), with results reported in a referenced table.
Model Performance: The LLaMA-65B model shows competitive performance with PaLM-540B, while the LLaMA-13B model outperforms GPT-3 by a small margin.

1. Code Generation The selected text discusses the Code Generation capabilities of language models, particularly focusing on the Llama model’s performance in generating Python code from natural language descriptions. Here are the key takeaways:

Benchmarks Used: The models were evaluated on two benchmarks: HumanEval and MBPP, which require generating Python code that fits a given description and satisfies test cases.
Llama's Performance: Llama models, especially the 13B and 65B parameter versions, outperformed other general models like LaMDA and PaLM, which were not specifically trained for code generation1.
Pass@1 Scores: Llama's pass@1 scores, which indicate the model’s ability to generate correct code on the first attempt, were higher than those of LaMDA and PaLM, showcasing its effectiveness in code generation tasks.
Potential for Improvement: The performance on code generation can be further improved by finetuning on code-specific tokens, as demonstrated by PaLM-Coder’s increased pass@1 score on HumanEval2. Finetuning on code tokens was not covered in this paper.

1. Instruction Finetuning The selected text discusses Instruction Finetuning and its impact on the performance of language models on the MMLU benchmark:

Finetuning Impact: A small amount of instruction finetuning significantly improves the performance of LLaMA-65B on MMLU.
Llama-I Performance: The instruction finetuned model, Llama-I, achieves a 68.9% score on MMLU, outperforming other models of similar size.
Comparison with State-of-the-Art: Despite the improvements, Llama-I’s performance is below the state-of-the-art model, GPT code-davinci-002, which scores 77.4% on MMLU.

1. Carbon Footprint The selected text discusses the carbon footprint associated with training large language models. Here are the key takeaways:

Carbon Emission Factors: Carbon emissions depend on the data center’s location1. For comparison, the US national average carbon intensity factor of 0.385 kg CO2eq/KWh is used.
Emission Estimates: Using the above factors, the training of models like BLOOM and OPT have resulted in 27 tCO2eq and 82 tCO2eq respectively. The development of the models discussed in the paper resulted in approximately 1,015 tCO2eq over 5 months.
Reducing Future Emissions: Releasing these models is hoped to reduce future carbon emissions, as the training is complete and some models can run on a single GPU, making them more energy-efficient.
Conclusion

The selected text from the paper discusses the Llama language models and highlights their significance:
Competitive Performance: LLaMA-13B surpasses GPT-3, and LLaMA-65B competes with Chinchilla-70B and PaLM-540B, despite being significantly smaller.
Public Data Training: Demonstrates that state-of-the-art results can be achieved using only publicly available data, without proprietary datasets.
Community Contribution: The release of these models aims to spur further research and address issues like toxicity and bias in large language models.
Future Plans: The authors intend to explore instruction finetuning and release even larger models trained on more extensive corpora.
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