Llama 3.2 1B - Distillation Off-Policy LoRA
LoRA adapter trained with Tinker (by Thinking Machines) using off-policy distillation on OpenThoughts3 dataset.
Training Details
- Base model: meta-llama/Llama-3.2-1B
- Method: Off-policy distillation (SFT on OpenThoughts3)
- LoRA rank: 32, alpha: 32
- Target modules: all-linear
- Checkpoint: batch 700
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
model = PeftModel.from_pretrained(base, "arvindcr4/llama-3.2-1b-distillation-offpolicy-lora")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
Platform
Trained using Tinker - hosted fine-tuning service for open-source LLMs.
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