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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 | """Handicate self-refinement trainer -- central config."""
import os
# --- base policy model (the thing that improves) ---
BASE_MODEL = os.environ.get("HANDICATE_BASE", "Qwen/Qwen2.5-3B-Instruct")
# swap to "Qwen/Qwen2.5-Coder-7B-Instruct" for more code/system muscle (slower RL)
# --- judge / critic model (rates responses + writes criticism) ---
# Can be the same family (cheap) or a stronger model for better grading.
JUDGE_MODEL = os.environ.get("HANDICATE_JUDGE", "Qwen/Qwen2.5-7B-Instruct")
# --- Hub repos ---
HF_USER = "AmongTheCouch23"
MODEL_REPO = f"{HF_USER}/handicate-policy" # the refined weights
SEED_PROMPTS_REPO = f"{HF_USER}/handicate-prompts" # task prompts to train on
# --- web corpus the curator streams ("internet data at scale", pre-filtered) ---
CORPUS_DATASET = os.environ.get("HANDICATE_CORPUS", "HuggingFaceFW/fineweb-edu")
CORPUS_CONFIG = os.environ.get("HANDICATE_CORPUS_CONFIG", "sample-10BT")
# --- refinement loop budget / knobs (env-overridable for smoke runs) ---
ROUNDS = int(os.environ.get("HANDICATE_ROUNDS", "5"))
GRPO_NUM_GENERATIONS = int(os.environ.get("HANDICATE_NUMGEN", "8")) # candidates per prompt
GRPO_STEPS_PER_ROUND = int(os.environ.get("HANDICATE_STEPS", "200"))
CURATE_PER_ROUND = int(os.environ.get("HANDICATE_CURATE", "200")) # web docs kept each round
ACCEPT_THRESHOLD = float(os.environ.get("HANDICATE_ACCEPT", "0.7")) # judge score to accept
REPLAY_FRACTION = 0.15 # small real-data replay kept to fight forgetting
# --- the "weighted not stored" rule ---
# Accepted knowledge is DISTILLED INTO WEIGHTS (LoRA), then raw data is DISCARDED.
# Only weights + a tiny replay buffer + the frozen eval set persist on disk.
DISCARD_RAW_AFTER_DISTILL = True
# --- collapse guard ---
# A round is KEPT only if it does not regress on this frozen, human-grounded eval.
EVAL_SET = "data/eval_heldout.jsonl" # NEVER generated/rated by the AI itself
EVAL_REGRESS_TOLERANCE = 0.0 # require >= previous score to keep a round
# --- LoRA ---
LORA_R = 16
LORA_ALPHA = 32
LEARNING_RATE = 1e-5
SEED = 42
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