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  • Llama2
    • Llama2 - Model Configuration
    • Llama2 - Model Quantization
    • Llama2 - Data Loading and Paths
    • Llama2 - Sequence Configuration
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    • Llama2 - Training Configuration
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  • Llama3
    • Downloading the model
    • Analysis of model files
      • Model Analysis - Configuration Parameters
      • Model Analysis - Safetensors
      • Tokenizer Configuration Files
        • Model Analysis - tokenizer.json
        • Model Analysis - Special Tokens
    • Llama3 - Model Configuration
    • Llama3 - Model Quantization
    • Llama3 - Data Loading and Paths
    • Llama3 - Sequence Configuration
    • Llama3 - Lora Configuration
    • Llama3 - Logging
    • Llama3 - Training Configuration
    • Llama3 - Data and Precision
    • Llama3 - Optimisations
    • Llama3 - Extra Hyperparameters
    • Llama3- All Configurations
    • Llama3 - Preprocessing
    • Llama3 - Training
    • Full Fine Tune
  • Special Tokens
  • Prompt Construction for Fine-Tuning Large Language Models
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  • After fine tuning LLama3
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  1. Llama3

Analysis of model files

The provided file structure represents the various components and files of the Llama3 language model. Let's explain each file and how they relate to one another:

original

This folder contains the original checkpoint of the Llama3 model. It serves as a backup or reference to the initial state of the model.

config.json

This file contains the configuration parameters for the Llama3 model. It defines the model architecture, hyperparameters, and other settings used during training and inference.

generation_config.json

This file specifies the configuration for text generation using the Llama3 model. It includes settings like maximum sequence length, temperature, top-k sampling, and other generation-related parameters.

model-00001-of-00004.safetensors to model-00004-of-00004.safetensors

These files contain the actual weights and parameters of the Llama3 model.

The model is divided into multiple files (in this case, four files) due to its large size.

The ".safetensors" extension indicates that the files are stored in a memory-mapped format for efficient loading and inference.

model.safetensors.index.json

This file serves as an index for the ".safetensors" files.

It maps the model's parameters to their corresponding locations in the ".safetensors" files, allowing the model to be loaded efficiently during inference.

special_tokens_map.json

This file defines the mapping between special token names and their corresponding token IDs.

Special tokens are used to represent specific entities or control sequences in the input and output of the model.

tokenizer.json

This file contains the vocabulary and mappings used by the tokenizer to convert input text into token IDs. It defines the mapping between words, subwords, or characters and their corresponding unique token IDs.

tokenizer_config.json

This file specifies the configuration settings for the tokenizer.

It defines the behavior and properties of the tokenizer, such as the special tokens, maximum sequence length, and input tensor names.

The files in this structure work together to define and use Llama3

  • The model weights and parameters are stored in the ".safetensors" files, with the "model.safetensors.index.json" file serving as an index for efficient loading.

  • The "config.json" and "generation_config.json" files provide the necessary configuration settings for the model architecture and text generation.

  • The "tokenizer.json" and "tokenizer_config.json" files are used by the tokenizer to convert input text into token IDs and handle special tokens.

  • The "special_tokens_map.json" file defines the mapping between special token names and their IDs.

  • The "README.md", "LICENSE", and "USE_POLICY.md" files provide documentation, licensing information, and usage guidelines for the model.

During inference, the model weights are loaded from the ".safetensors" files, and the configuration files are used to set up the model architecture and generation settings.

The tokenizer, guided by the "tokenizer.json" and "tokenizer_config.json" files, converts the input text into token IDs, which are then fed into the model for processing and generating outputs.

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