File size: 2,410 Bytes
ccbd209 4e92e62 ccbd209 4e92e62 ccbd209 4e92e62 ccbd209 0e35fb1 ccbd209 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | """Distill accepted data into weights (LoRA SFT), then DISCARD the raw data.
This realizes "weighted, not stored": curated + critique-revised pairs are folded into
the model's parameters via supervised fine-tuning, then the text items are deleted. Only
the weights and a small replay buffer survive -- the model carries the knowledge, not a
growing corpus on disk.
"""
import json
import os
from pathlib import Path
def distill(base_or_adapter, pairs, out_dir, lr, lora_r, lora_alpha, discard_raw=True):
from datasets import Dataset
from peft import LoraConfig
from transformers import AutoTokenizer
from trl import SFTConfig, SFTTrainer
tok = AutoTokenizer.from_pretrained(base_or_adapter)
def fmt(ex):
msg = [{"role": "user", "content": ex["instruction"]},
{"role": "assistant", "content": ex["response"]}]
return {"text": tok.apply_chat_template(msg, tokenize=False)}
ds = Dataset.from_list(pairs).map(fmt)
args = SFTConfig(output_dir=out_dir, per_device_train_batch_size=2,
gradient_accumulation_steps=4, learning_rate=lr,
num_train_epochs=1, bf16=True, gradient_checkpointing=True,
logging_steps=10, save_strategy="no", report_to="none",
dataset_text_field="text", max_length=1024)
peft_cfg = LoraConfig(r=lora_r, lora_alpha=lora_alpha, task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"])
trainer = SFTTrainer(model=base_or_adapter, args=args, train_dataset=ds, peft_config=peft_cfg)
trainer.train()
# merge LoRA into the base -> out_dir is a full, loadable model for eval + next round
merged = trainer.model.merge_and_unload()
merged.save_pretrained(out_dir)
tok.save_pretrained(out_dir)
if discard_raw:
# the knowledge now lives in out_dir's weights; raw pairs are dropped
pairs.clear()
return out_dir
def keep_replay(pairs, frac, path):
"""Persist a tiny stratified replay slice (real, high-score) to fight forgetting."""
import random
top = sorted(pairs, key=lambda x: -x.get("score", 0))
keep = top[:max(1, int(len(top) * frac))]
Path(path).parent.mkdir(parents=True, exist_ok=True)
with open(path, "a", encoding="utf-8") as f:
for k in keep:
f.write(json.dumps(k) + "\n")
return len(keep)
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