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What is LLAMA 3
LLAMA 3 is the new generation large language model (LLM) launched by Meta’s latest open source. It contains models with 8B and 70B parameters, marking another major progress in the field of open source artificial intelligence. As the third -generation product of the LLAMA series, LLAMA 3 not only inherits the powerful functions of the previous generation models, but also provides more efficient and reliable AI solutions through a series of innovations and improvements. It aims to use advanced natural language processing technology It supports extensive application scenarios, including but not limited to programming, problem solving, translation and dialogue generation.
LLAMA 3 series model
LLAMA 3 currently provides two models: 8B (8 billion parameters) and 70B (70 billion parameters). These two models are designed to meet the application needs of different levels, providing users with flexibility and freedom of choice for freedom of choice. Spend.
- Llama-3-8b: 8B parameter model, which is a relatively small but efficient model with 8 billion parameters. The application scenarios that are designed for fast reasoning and less computing resources, while maintaining higher performance standards.
- Llama-3-70b: 70B parameter model, which is a larger -scale model with 70 billion parameters. It can handle more complicated tasks, provide deeper language understanding and generation ability, and suitable for applications with higher performance requirements.
In the future, LLAMA 3 will also launch a model of 400B parameter scale, which is still under training. Meta also said that waiting for LLAMA 3 training, it will also publish a detailed research paper.
LLAMA 3’s official website entrance
LLAMA 3 improvement place
- Parameter scale: LLAMA 3 provides models with two parameters of 8B and 70B. Compared with LLAMA 2, the increase in the number of parameters enables the model to capture and learn more complex language models.
- Training data set: LLAMA 3’s training dataset is 7 times larger than LLAMA, including more than 1.5 trillion token, including 4 times code data, which makes LLAMA 3 better in understanding and generating code.
- Model architecture: LLAMA 3 adopts a more efficient segmentation and group query attempt (GQA) technology, which improves the model’s reasoning efficiency and the ability to handle long text.
- Performance improvement: Through improved pre -training and post -training process, LLAMA 3 has made progress in reducing error rejection rates, increasing response alignment, and increasing model response diversity.
- Security: Introduced new trust and security tools such as LLAMA Guard 2, as well as Code Shield and Cybersec Eval 2, which enhances the security and reliability of the model.
- Multi -language support: LLAMA 3 adds high -quality non -English data in the pre -training data, which has laid the foundation for future multi -language capabilities.
- Inference and code generation: LLAMA 3 shows the ability of greatly improved in terms of reasoning, code generation and instructions, making it more accurate and efficient in complex task processing.
LLAMA 3 performance evaluation
According to META’s official blog, the LLAMA 3 8B model that was fine-tuned by instructions was better than models of the same level parameters in MMLU, GPQA, Humaneval, GSM-8K, MATH and other data sets (GEMMA 7B, Mistral 7B). The fine-tuning LLAMA 3 70B is also better than Gemini Pro 1.5 and CLAUDE 3 Sonnet models of the same scale in MLLU, Humaneval, GSM-8K and other reference tests.
In addition, META has also developed a new set of high -quality human evaluation sets, including 1,800 tips, covering 12 key cases: seeking suggestions, brainstorming, classification, closed quiz, coding, creative writing, extraction, shaping character/character , Open Q & A, reasoning, rewriting and summary. By comparison with Claude Sonnet, Mistral Medium, GPT-3.5 and other competitive models, human appraisers prefer the preferences based on the evaluation set. Rate.
LLAMA 3’s technical architecture
- Decoder architecture: LLAMA 3 adopts the Decoder-only architecture, which is a standard Transformer model architecture, which is mainly used to handle natural language generation tasks.
- Words and vocabulary: LLAMA 3 uses a polymer with 128K token, which enables the model to encode the language more efficiently, thereby significantly improved performance.
- Group query attention: In order to improve the efficiency of reasoning, LLAMA 3 uses GQA technology in 8B and 70B models. This technology reduces the calculation volume by the grouping groups in the inquiry mechanism, while maintaining the performance of the model.
- Long sequence processing: LLAMA 3 supports 8,192 token sequences, using masking technology to ensure that Self-Wartention does not cross the document boundary, which is particularly important for processing long text.
- Pre -training data set: LLAMA 3 conducted pre -training on the token of more than 15TB. This data set is not only huge in scale, but also high in quality, providing rich language information for the model.
- Multi -language data: In order to support multi -language capabilities, the pre -training dataset of LLAMA 3 contains more than 5%of non -English high -quality data and covers more than 30 languages.
- Data filtering and quality control: The development team of LLAMA 3 has developed a series of data filter pipes, including inspiration filters, NSFW (not suitable for workplace) filters, semantics to heavy methods, and text classifiers to ensure the high quality of training data.
- Expansion and parallelization: During the training process of LLAMA 3, data parallelization, model parallelization, and pipeline are used in parallelization. The application of these technologies enables models to efficiently train on a large number of GPUs.
- Instructions fine-tuning (Instrument Fine-Tuning): On the basis of the pre -training model, LLAMA 3 further enhances the performance of the model on specific tasks by instruction, such as dialogue and programming tasks.
How to use LLAMA 3
Developer
Meta has open source its LLAMA 3 model on GitHub, Hugging Face, and Replicate. Developers can use TORCHTUNE and other tools to customize and fine -tune LLAMA 3 to meet specific use cases and needs. Interested developers can view the official officials.Entry guideAnd go to download the deployment.
Ordinary user
Ordinary users who do not understand technology want to experience LLAMA 3 to use in the following ways:
- Visit Meta’s latest launchMeta aiChat assistant to experience (Note: Meta.ai will lock the area, only some countries can use)
- Visit the Chat with LLAMA provided by Replicate for experiencehttps://llama3.replicate.dev/
- Use Hugging Chat (https://huggingface.co/chat/), You can manually switch the model to LLAMA 3
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