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Crocodile0125/bookend-KING-CXXVI-10kDPO-FINAL
bookend-KING-CXXVI-10kDPO-FINAL is a machine learning model from Crocodile0125. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Merged BF16 weights from bookend DPO on the v125 king pool, exported at global training step 80 (SAVESTEPS=10 → checkpoint-80 merged with LoRA).
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From the Hugging Face model README
Merged BF16 weights from bookend DPO on the v125 king pool, exported at global training step 80 (SAVE_STEPS=10 → checkpoint-80 merged with LoRA).
Use this directory as a vLLM / local duel / private submit model path.
| Item | Role |
|---|---|
model-*.safetensors + model.safetensors.index.json | Sharded merged Qwen3.6-35B-A3B MoE |
config.json, tokenizer.json, chat_template.jinja | HF layout for inference |
train_bookend_v125_step80.py | Training recipe (check-setup / train / export / show-recipe) |
Training data: data/dpo/bookend_dpo_pairs.jsonl (~3277 pairs; join from data/dpo/bookend_dpo_parts/ if missing). See docs/training/BOOKEND_DPO_TRAINING.md.
Base model for training: local_king/king_cxxiv (or current reign king path via KING=...).
From repo root (8× GPU recommended):
set -a && source .env && set +a
pip install -e '.[train]' # once
# merged pairs file
python scripts/train/split_train_pairs.py --join \
--src data/dpo/bookend_dpo_pairs.jsonl \
--parts-dir data/dpo/bookend_dpo_parts
python checkpoints/bookend-v125-step80/train_bookend_v125_step80.py check-setup
python checkpoints/bookend-v125-step80/train_bookend_v125_step80.py train
View baked-in hyperparameters:
python checkpoints/bookend-v125-step80/train_bookend_v125_step80.py show-recipe
Defaults in BookendV125Step80Recipe (override with env):
| Variable | Default |
|---|---|
KING | local_king/king_cxxiv |
TRAIN_PAIRS | data/dpo/bookend_dpo_pairs.jsonl |
OUT_DIR | checkpoints/bookend_v125_lora |
NUM_GPUS | 8 |
EPOCHS | 2 |
LR / BETA | 5e-7 / 0.1 |
BATCH_SIZE / GRAD_ACCUM | 1 / 8 |
SAVE_STEPS | 10 |
TARGET_STEP | 80 |
Training runs export automatically after DPO (merge checkpoint-80 into this folder).
Re-export only (LoRA run already exists):
OUT_DIR=checkpoints/bookend_v125_lora TARGET_STEP=80 \
python checkpoints/bookend-v125-step80/train_bookend_v125_step80.py export
vllm serve checkpoints/bookend-v125-step80 \
--tokenizer assets/tokenizers/Qwen3.6-35B-A3B \
--port 8000
python scripts/local_eval/run_duel.py --mode duel \
--candidate-base-url http://localhost:8000/v1 \
--candidate-model checkpoints/bookend-v125-step80 \
--n-samples 20
Run preflight_gate.py before any on-chain private submit.
set -a && source .env && set +a
.venv/bin/albedo submit-private \
--path checkpoints/bookend-v125-step80 \
--name bookend-v125-step80 \
--coldkey JMax-1 \
--hotkey sn97-15 \
--yes
Ensure the hotkey wallet matches the registered UID (see project field notes if Custom error: 19).
Sibling exports in checkpoints/: bookend-v125-step50, step60, step70, same training run at different checkpoint-* steps.
simple_bot / pre-eval on dashboard after submit..env for HF_TOKEN and wallet passwords.