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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. [29] 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 ...
Llama 3.1 July 2024: Meta AI 405 15.6T tokens 440,000: Llama 3 license 405B version took 31 million hours on H100-80GB, at 3.8E25 FLOPs. [97] [98] DeepSeek V3 December 2024: DeepSeek: 671 14.8T tokens 56,000: DeepSeek License 2.788M hours on H800 GPUs. [99] Amazon Nova December 2024: Amazon: Unknown Unknown Unknown Proprietary
Codestral is Mistral's first code focused open weight model. Codestral was launched on 29 May 2024. It is a lightweight model specifically built for code generation tasks. As of its release date, this model surpasses Meta's Llama3 70B and DeepSeek Coder 33B (78.2% - 91.6%), another code-focused model on the HumanEval FIM benchmark. [40]
The new Llama 3 model can converse in eight languages, write higher-quality computer code and solve more complex math problems than previous versions, the Facebook parent company said in blog ...
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llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
Based on the training of previously employed language models, it has been determined that if one doubles the model size, one must also have twice the number of training tokens. This hypothesis has been used to train Chinchilla by DeepMind. Similar to Gopher in terms of cost, Chinchilla has 70B parameters and four times as much data. [3]