| Parameter | Value | Explanation |
|---|---|---|
| architectures | Llama ForCausalLM | Specifies the architecture class used for the language model. This class is designed for causal language modeling tasks, where the model predicts the next token based on the previous tokens. |
| attention_bias | false | Indicates that attention bias, which can be used to mask out certain positions or modify attention weights, is not used in the model. |
| attention_dropout | 0.0 | The dropout probability for the attention layers. Dropout is a regularization technique that randomly sets some input units to 0 during training. In this case, dropout is not applied to the attention layers. |
| bos_token_id | 128,000 | The ID of the special token used to indicate the beginning of a sequence. This token is added to the start of the input sequence. |
| eos_token_id | 128,001 | The ID of the special token used to indicate the end of a sequence. This token is added to the end of the input sequence. |
| hidden_act | "silu" | The activation function used in the hidden layers of the model. In this case, the Sigmoid Linear Unit (SiLU) function is used, which is defined as x * sigmoid(x). |
| hidden_size | 4,096 | The dimensionality of the hidden states in the model. This determines the size of the vectors that the model uses to represent the input and output at each layer. |
| initializer_range | 0.02 | The range used for initialising the model's weights. The weights are initialised randomly within this range to break symmetry and encourage diverse representations. |
| intermediate_size | 14,336 | The dimensionality of the intermediate (feed-forward) layer in the model. This layer is applied after the attention layer and expands the hidden state size before projecting it back to the original hidden_size. |
max_position embeddings | 8,192 | The maximum sequence length that the model can process, in tokens. This limits the context size that the model can attend to. |
| model_type | "llama" | Specifies the type of the language model. In this case, it is the "llama" model, which is a large language model developed by Meta. |
| num_attention_heads | 32 | The number of attention heads in each attention layer. Attention heads allow the model to attend to different parts of the input sequence simultaneously, capturing different relationships and patterns. |
| num_hidden_layers | 32 | The number of hidden layers (transformer blocks) in the model. Each layer consists of an attention mechanism followed by a feed-forward network. |
num_key_value heads | 8 | The number of key-value heads in each attention layer. This is used in the query-key-value attention mechanism, where the key-value heads are used to compute the attention weights. |
| pretraining_tp | 1 | The tensor parallelism used during pretraining. Tensor parallelism allows for parallel computation across multiple devices or nodes. |
| rms_norm_eps | 1e-05 | The epsilon value used for RMSNorm (Root Mean Square Normalization) layers. RMSNorm is a normalization technique that normalizes the activations based on their root mean square values. The epsilon is added for numerical stability. |
| rope_scaling | null | Indicates that RoPE (Rotary Position Embedding) scaling is not used. RoPE is a technique for encoding positional information in the attention mechanism. |
| rope_theta | 500,000 | The scaling factor for RoPE. This value determines the frequency of the rotary position embeddings. |
| tie_word_embeddings | false | Indicates that word embeddings are not tied with the output layer. Tying word embeddings means sharing the weights between the input embedding layer and the output softmax layer. |
| torch_dtype | "bfloat16" | The data type used for the model's weights and activations. In this case, Brain Floating Point 16-bit (bfloat16) is used, which is a 16-bit floating-point format that offers better performance and memory usage compared to the standard 32-bit float. |
| transformers_version | 4.40.0.dev0 | The version of the Transformers library used. This specifies the version of the library that the model configuration is compatible with. |
| use_cache | true | Enables caching of key-value pairs during inference for faster generation. Caching stores the computed key-value pairs for each layer, avoiding redundant computations when generating sequences. |
| vocab_size | 128,256 | The size of the model's vocabulary, in tokens. This represents the number of unique tokens that the model can recognize and generate, including regular words, subwords, and special tokens. |