Knowledge distillation in three steps
Enforceable based on fine-tuned permissions allowed by open source protocols:
Step 1: Data preparation
Construct domain-specific QA pair dataset (10-50k samples recommended), generated using Grok-2 itselfSynthetic datareplenishment
Step 2: Efficient fine-tuning of parameters
Only 0.1-11 TP3T parameters were trained using LoRA or QLoRA techniques:peft_config = LoraConfig(task_type='CAUSAL_LM', r=8, lora_alpha=32)
Step 3: Expert selective fine-tuning
By analyzing MoE routing records (needs to be modified)router_logitsoutput), targeted fine-tuning of HF-activated expert modules
Caveats:
1. The need forGrok-2 licenseUse within permitted limits
2. Recommended use--freeze-base-modelFreeze base model parameters
3. Typical results may be published inNeurIPSMoE Symposium at IsoTop
This answer comes from the articleGrok-2: xAI's Open Source Hybrid Expert Large Language ModelThe
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