Implementation:PacktPublishing LLM Engineers Handbook FastLanguageModel From Pretrained

Field	Value
Implementation Name	FastLanguageModel From Pretrained
Type	Wrapper Doc (Unsloth external API)
Source File	llm_engineering/model/finetuning/finetune.py:L29-43 (within load_model())
Workflow	LLM_Finetuning
Repo	PacktPublishing/LLM-Engineers-Handbook
Implements	Principle:PacktPublishing_LLM_Engineers_Handbook_Quantized_Model_Loading

Function Signature

FastLanguageModel.from_pretrained(
    model_name: str,
    max_seq_length: int,
    load_in_4bit: bool,
) -> tuple[model, tokenizer]

Import

from unsloth import FastLanguageModel

Description

FastLanguageModel.from_pretrained() is an Unsloth library method that loads a pre-trained language model and its corresponding tokenizer from a HuggingFace model identifier. It wraps the standard HuggingFace AutoModelForCausalLM.from_pretrained() with additional optimizations including fused attention kernels, memory-efficient loading, and optional 4-bit quantization via bitsandbytes.

This method is called within the load_model() function in the repository's fine-tuning pipeline.

Parameters

Parameter	Type	Value in Repo	Description
model_name	str	HuggingFace model ID (e.g., "meta-llama/Meta-Llama-3.1-8B")	The HuggingFace Hub identifier or local path for the pre-trained model.
max_seq_length	int	2048	Maximum sequence length the model will handle during fine-tuning. Determines positional encoding size and memory allocation.
load_in_4bit	bool	False	Whether to load model weights in 4-bit NF4 quantization. Set to False in this repository.

Returns

A tuple of (model, tokenizer):

• model: The loaded language model (with Unsloth optimizations applied), ready for LoRA adapter injection.
• tokenizer: The corresponding tokenizer configured for the model.

Key Code in Repository

# From llm_engineering/model/finetuning/finetune.py (within load_model())

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_id,
    max_seq_length=max_seq_length,
    load_in_4bit=False,
)

Notes on Repository Usage

• load_in_4bit=False: In this repository, 4-bit quantization is not enabled at load time. The model is loaded in its default precision (typically BF16/FP16). This suggests the target instance (SageMaker ml.g5.2xlarge with 24GB VRAM) has sufficient memory for the chosen model size.
• max_seq_length=2048: The sequence length is set to 2048 tokens, which is a reasonable default for fine-tuning instruction-following models.
• model_id: The model identifier is configured externally and passed into the load_model() function.

External Dependencies

Package	Purpose
unsloth	Optimized model loading and inference
transformers	Underlying HuggingFace model/tokenizer classes
bitsandbytes	4-bit quantization support (used when load_in_4bit=True)

External References

• Unsloth GitHub Repository
• QLoRA Paper (Dettmers et al., 2023)
• HuggingFace Transformers Model Documentation

See Also

• Principle:PacktPublishing_LLM_Engineers_Handbook_Quantized_Model_Loading
• Environment:PacktPublishing_LLM_Engineers_Handbook_Unsloth_Finetuning_Environment