LLaMa

LLaMa

LLaMA model,github download,Meta(Facebook)'s open and efficient foundation language model

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Official LLaMa Model Website, GitHub Download, Meta (Facebook) Open and Efficient Base Language Model

What is the LLaMa Model?

On February 25th, Meta's official website released a new large language model called LLaMa (Large Language Model Meta AI). Meta offers four different parameter-scale LLaMa models: 7 billion, 13 billion, 33 billion, and 65 billion, trained in 20 languages. Compared to existing top large language models, LLaMa models are competitive in performance.

LLaMa Model Official Website:

https://github.com/facebookresearch/llama

Paper Address:https://arxiv.org/abs/2302.13971

Chinese LLaMa&Alpaca Large Language Model + Local CPU/GPU Training and Deployment (Chinese LLaMA)&Alpaca LLMs):

https://github.com/ymcui/Chinese-LLaMA-Alpaca

How is the LLaMa Model?

LLaMa(Large Language Model Meta AI), an open and efficient large base language model released by Meta AI, comes in four versions: 7B, 13B, 33B, and 65B (6.5 billion). The dataset sources are all public datasets, with no customized datasets, ensuring compatibility with open-source work and reproducibility. The entire training dataset, after tokenization, contains approximately 1.4T tokens.7B,13B,33B,65B(650 billion)

In terms of model performance, the LLaMa model is outstanding: the 13 billion parameter LLaMa model can outperform the 175 billion parameter GPT-3 on most benchmarks and can run on a single V100 GPU; the largest 65 billion parameter LLaMa model is comparable to Google's Chinchilla-70B and PaLM-540B.outperformGPT-3

The dataset sources are all public datasets, with no customized datasets, ensuring compatibility with open-source work and reproducibility. The entire training dataset, after tokenization, contains approximately 1.4T tokens. Among them, LLaMa-65B and LLaMa-33B are trained on 14 trillion tokens, while the smallest model LLaMa-7B is trained on 1 trillion tokens.tokenOverall, the LLaMa model performs exceptionally well: the 13 billion parameter LLaMa model outperforms the 175 billion parameter GPT-3 on most benchmarks and can run on a single V100 GPU; the 65 billion parameter LLaMa model is comparable in performance to Google's Chinchilla-70B and PaLM-540B.

Mark Zuckerberg, Meta's CEO, said that the LLaMa model aims to help researchers make progress in more complex tasks such as generating text, dialogues, summarizing written materials, proving mathematical theorems, or predicting protein structures, with broad prospects.

What are the advantages and characteristics of the LLaMa model compared to models like Chinchilla, PaLM, GPT-3, and ChatGPT?

Currently, Meta has uploaded the related paper to arXiv.

Similar to other large language models, the LLaMa model also has some issues, such as the potential to produce biased, toxic, or false content. Meta is attracting more researchers to participate by opening up the source code to address these issues.

To ensure integrity and prevent misuse, Meta will open up open-source access to LLaMa for non-commercial research institutions and grant access to academic researchers based on specific circumstances. These permissions will be gradually granted to governments, non-governmental organizations, the academic community, and organizations in global industrial research laboratories.

Notably, LLaMa is not just a chatbot but a research tool that may be used to solve problems related to AI language models. It is a "smaller, better-performing" model and is different from Google's LaMDA and OpenAI's GPT models, as it is trained on publicly available data. LLaMa is not a model fine-tuned for specific tasks but a general-purpose model suitable for many different tasks.

The LLaMa model will be open-sourced to the entire AI research community, granting access to universities, non-governmental organizations, and industrial laboratories. This open-source approach helps address the risks of inevitable biases, toxicity, and false content in large language models.

Through more data training, small models can surpass large models, such as LLaMa-13B being superior to the 175 billion parameter GPT-3 on most benchmarks; while LLaMa-65B's performance is comparable to the larger-scale Chinchilla-70B and PaLM-540B.

Large language models like ChatGPT, GPT-4, etc., have sparked a new wave of research in the field of natural language processing, demonstrating the capabilities of general artificial intelligence (AGI), and have received extensive attention from the industry. However, due to the high cost of training and deployment of large language models, it has posed some obstacles to building transparent and open academic research.

Chinese LLaMA Model

To promote open research in the Chinese NLP community, this project has open-sourced the Chinese LLaMA model and the instruction-tuned Alpaca large model.

These modelsexpand the Chinese vocabulary on the original LLaMa and use Chinese data for secondary pre-training, further enhancing the ability to understand Chinese fundamental semantics. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, significantly improving the model's understanding and execution of instructions. For detailed information, please refer to the technical report (Cui, Yang, and Yao, 2023).Main contents of this project:🚀Expanded the Chinese vocabulary of the original LLaMa model, improving Chinese encoding and decoding efficiency🚀

Open-sourced the Chinese LLaMA and the instruction-tuned Chinese Alpaca pre-trained with Chinese text data

  • 🚀 Open-sourced pre-training scripts and instruction-tuning scripts, allowing users to further train according to their needs
  • 🚀 Experience of deploying large models quickly using the CPU/GPU of a laptop (personal PC)
  • 🚀 Supports 🤗transformers, llama.cpp, text-generation-webui, LlamaChat, LangChain, privateGPT, and other ecosystems
  • Currently open-sourced model versions: 7B (Standard, Plus), 13B (Standard, Plus) 💡
  • Below is the actual experience effect of the locally quantized and deployed Chinese Alpaca-7B model (GIF not accelerated, tested under M1 Max). Supports 🤗transformers, llama.cpp, text-generation-webui, LlamaChat, LangChain, privateGPT, and other ecosystems
  • Currently open-source model versions: 7B (Standard, Plus),13B (Standard, Plus))💡The following image shows the actual experience effect of the Chinese Alpaca-7B model after local CPU quantization deployment (GIF is not accelerated, tested on M1 Max).

✨ Below is the actual experience effect of the Chinese Alpaca-7B model after local CPU quantization deployment (GIF is not accelerated, tested on M1 Max).