File size: 3,705 Bytes
1c0d385 | 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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | """Build conservative v4 SFT mix for TinyLiquid.
v4 is designed to avoid the v3 failure mode: full-model SFT overfit into
broken analyst jargon. The mix keeps short, fully-visible assistant answers,
uses a smaller forensic/SOP slice, and adds much more raw language replay.
Output: data/sft_mix_v4.jsonl
"""
import json
import random
from collections import Counter
from pathlib import Path
from data.tokenizer import load_tokenizer
rng = random.Random(20260801)
SEQ = 256
MIN_ASSISTANT = 24
MAX_USER = 160
MAX_ASSISTANT = 180
OUT = Path('data/sft_mix_v4.jsonl')
PERSONA_T = {'analyst': '<|analyst|>', 'skeptic': '<|skeptic|>', 'none': ''}
def load_jsonl(path):
return [json.loads(line) for line in open(path, encoding='utf-8') if line.strip()]
def write_jsonl(path, rows):
with open(path, 'w', encoding='utf-8') as f:
for row in rows:
f.write(json.dumps(row, ensure_ascii=False) + '\n')
def dedupe(rows):
seen = set()
out = []
for row in rows:
key = (row.get('persona', 'analyst'), row.get('user', '')[:180])
if key in seen:
continue
seen.add(key)
out.append(row)
return out
def visible_chat(row, tok, u_id, a_id, eot_id):
if 'raw' in row:
return True
if not row.get('user') or not row.get('assistant'):
return False
persona = row.get('persona', 'analyst')
p_ids = tok.encode(PERSONA_T.get(persona, '<|analyst|>')).ids if persona != 'none' else []
user = tok.encode(row['user']).ids
assistant = tok.encode(row['assistant']).ids
if len(user) > MAX_USER or len(assistant) > MAX_ASSISTANT or len(assistant) < MIN_ASSISTANT:
return False
ids = p_ids + [u_id] + user + [a_id] + assistant + [eot_id]
return len(ids) <= SEQ
def sample(rows, n):
rows = list(rows)
rng.shuffle(rows)
return rows[:min(n, len(rows))]
def main():
tok = load_tokenizer('data/tokenizer.json')
u_id = tok.token_to_id('<|user|>')
a_id = tok.token_to_id('<|assistant|>')
eot_id = tok.token_to_id('<|endoftext|>')
v3 = dedupe(load_jsonl('data/sft_mix_v3.jsonl'))
truth = [r for r in v3 if r.get('user', '').startswith('Answer truthfully:')]
chatish = [r for r in v3 if r.get('persona') == 'analyst' and 'raw' not in r and len(r.get('user', '')) < 90]
forensic = dedupe(load_jsonl('data/sft_forensic.jsonl'))
forensic_a = [r for r in forensic if r.get('persona') != 'skeptic']
forensic_s = [r for r in forensic if r.get('persona') == 'skeptic']
mix = []
mix += load_jsonl('data/general_chat.jsonl')
mix += load_jsonl('data/persona_dialogue.jsonl')
mix += load_jsonl('data/tool_use.jsonl')
mix += sample(truth, 40)
mix += sample(chatish, 80)
mix += sample(load_jsonl('data/sft_distill_mix.jsonl'), 160)
mix += sample(load_jsonl('data/sft_sop_mix.jsonl'), 120)
mix += sample(forensic_a, 140)
mix += sample(forensic_s, 40)
clean = []
dropped = 0
for row in dedupe(mix):
if visible_chat(row, tok, u_id, a_id, eot_id):
clean.append(row)
else:
dropped += 1
# Raw replay is intentionally large. For this tiny model, preserving fluent
# language is more important than forcing domain style in one pass.
lines = [line.strip() for line in open('data/TinyStoriesV2-GPT4-train.txt', encoding='utf-8') if line.strip()]
rng.shuffle(lines)
for line in lines[:700]:
clean.append({'raw': line, 'persona': 'none'})
rng.shuffle(clean)
write_jsonl(OUT, clean)
print('total', len(clean), 'dropped_chat', dropped, dict(Counter(r.get('persona', '?') for r in clean)))
if __name__ == '__main__':
main()
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