Large Language ModelsMEDIUM

What are Key LLM Parameters

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Q
Question

Can you describe the key parameters of Large Language Models (LLMs) and explain their significance in the model's performance?

A
Answer

Large Language Models (LLMs) have several key parameters that are crucial for their performance. These include the number of layers, hidden size, attention heads, and vocabulary size. The number of layers affects the model's depth, impacting its ability to learn complex representations. Hidden size determines the dimensionality of the model's internal embeddings, influencing its capacity to capture intricate patterns. Attention heads allow the model to focus on different parts of the input simultaneously, enhancing its ability to understand context. Vocabulary size affects the model's ability to handle diverse language inputs. Adjusting these parameters can significantly impact the model's training time, performance, and computational requirements.

E
Explanation

Theoretical Background:

Large Language Models (LLMs), such as Llama-3, Mistral, etc are built on the Transformer architecture, which relies heavily on attention mechanisms. The key parameters in LLMs are:

  1. Number of Layers (L): Determines the depth of the model. More layers can potentially capture more complex patterns but may also lead to overfitting if not regularized properly.
  2. Hidden Size (H): Refers to the size of the hidden vectors in the model. Larger hidden sizes allow the model to store more information but require more computational resources.
  3. Attention Heads (A): Multiple heads allow the model to jointly attend to information from different representation subspaces at different positions.
  4. Vocabulary Size (V): The size of the tokenizer's vocabulary influences the model's ability to understand and generate diverse language inputs.

You can see a configuration of each models in Huggingface, for example "Llama-3.1-8B-Instruct" as below:

{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": [
    128001,
    128008,
    128009
  ],
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 131072,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": {
    "factor": 8.0,
    "low_freq_factor": 1.0,
    "high_freq_factor": 4.0,
    "original_max_position_embeddings": 8192,
    "rope_type": "llama3"
  },
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.42.3",
  "use_cache": true,
  "vocab_size": 128256
}

Practical Applications:

These parameters directly influence the model's ability to generalize from the training data to unseen data. For example, a larger hidden size may improve performance on complex tasks like text generation or summarization, but it may also increase the risk of overfitting and computational costs.

Code Example:

Here is a simple illustration of loading LLMs with Transformer Library:

# Use a pipeline as a high-level helper
from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V3-0324", trust_remote_code=True)
pipe(messages)

External References:

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