Fine-Tuning Llama 3 on Custom JSON Datasets

Unlock domain-specific performance by fine-tuning Llama 3 8B Instruct using structured JSON datasets. This hands-on guide details the mandatory Llama 3 instruction format, ensuring correct role assignment (system, user, assistant) to prevent catastrophic forgetting. We implement Parameter Efficient Fine-Tuning (PEFT) with LoRA, specifying R=16 and targeting 'q_proj', 'k_proj', and 'v_proj' layers. Learn the critical steps for 4-bit NF4 quantization, applying the tokenizer's chat template, configuring SFTTrainer parameters (learning rate schedules, gradient accumulation), and finally, merging the LoRA adapters for production deployment. Achieve precise, context-aware outputs tailored exactly to your custom data schema.

00:00: Structured Data for Llama 3
00:37: Defining the JSON Schema
01:12: Environment and Quantization Setup
01:44: Configuring LoRA Parameters
02:14: Tokenization and Chat Template
02:44: SFTTrainer Arguments Definition
03:15: Execution and Loss Monitoring
03:43: Merging Adapters for Deployment
04:08: Inference Validation and Testing

Llama3 #FineTuning #LoRA #SFTTrainer #NF4 #JSONData #LLMDevelopment

Видео Fine-Tuning Llama 3 on Custom JSON Datasets канала ByteDistrict