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LoRA SFT adapter โ recipe sft on mixture da-lowstakes-original-7 (qwen36, seed 0)
| field | value |
|---|---|
experiment |
LoRA SFT adapter โ recipe sft on mixture da-lowstakes-original-7 (qwen36, seed 0) |
date_generated |
20260916 |
constitution |
none |
source_repo |
https://github.com/Matthew-Bozoukov/teaching_claude_why_replication.git @ d18adc6dabfdcdfa1e02064df513f074beb2e0fa |
models |
base: Qwen/Qwen3.6-27B@6a9e13bd6fc8f0983b9b99948120bc37f49c13e9 |
generation_config |
{"recipe": "sft", "seed": 0, "thinking": true, "epochs": 1.0, "lr": 0.0001, "batch_size": 1, "grad_accum": 16, "max_seq_len": 8192, "lora": {"r": 64, "alpha": 128, "dropout": 0.05}, "dynamic_batching": {"token_budget": 8000, "loss_agg": "seq-mean-token-mean"}} |
schema |
PEFT LoRA adapter (safetensors) + tokenizer + train_config.yaml (the resolved config that ran, every launch argument and pin written back: uv run train --config train_config.yaml re-runs it) + training_meta.json {organism, thinking, recipe, mix_subject, train_config, base_model, base_model_revision, model_profile, dataset{repo,file,revision}, git_sha, timestamp} |
provenance |
scripts/train/train_lora.py --config configs/train/sft.yaml model=qwen36 seed=0 wandb=false constitution=none data_repo=dougalldeepmind/2026-09-16-da-lowstakes-original-7-mix data_revision=41d80cc3e616d48739af6935d5705fd82a9a56f3 base_model_revision=6a9e13bd6fc8f0983b9b99948120bc37f49c13e9 allow_default_supervise=true |
dataset |
hf.co/datasets/dougalldeepmind/2026-09-16-da-lowstakes-original-7-mix@41d80cc3e616d48739af6935d5705fd82a9a56f3 (mixture.jsonl) |
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