Our custom
qwen3.5-MoE (≈1B total /
610M active parameters),
fine-tuned on the
mind-of-tashi-selfplay
corpus to play the blind-commit duel: read the opponent's move history, reason
in an English + Hindi/Sanskrit (IAST) code-switched register inside
<think>…</think>, then commit one legal move as JSON.
Its sibling is the
micro student
(0.4B total / 200M active) — the mini fields ~3× the active parameters, and the
two can duel each other live in the
game Space's
self-play mode.
LoRA r=32 / α=64 over the attention + MLP projections
(q/k/v/o + gate/up/down), dropout 0.05, LR 2e-5 cosine with 10% warmup,
bf16, seq 4096, completion-only loss, effective batch 16. The bare adapter
ships at
mind-of-tashi-mini-sft-lora;
this repo is the merged model.
20 unseen game states across all 10 ladder personas; a reply is valid only if
it has a <think> block, parseable {"move", "taunt"} JSON, and a legal move:
Watch-mode duels on the deployed Space (mini as challenger, GRPO micro as the
house mind): the
200M-active GRPO model beats this 610M-active SFT model
decisively. Imitation learns the format; reinforcement learns the
game.
Reproduce it: open the
Space,
pick "Tashi mini SFT" in the self-play picker, and watch.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2m = AutoModelForCausalLM.from_pretrained("build-small-hackathon/mind-of-tashi-mini-sft",
3 torch_dtype="bfloat16", trust_remote_code=True)
4# sample at temperature >= 0.7 — greedy decoding can loop on small models
Part of the
Mind of Tashi bundle (Build Small Hackathon, Track Two) — see
the
collection.
