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patnir41/kaetram-qwen3.5-2b-opd-r3
kaetram-qwen3.5-2b-opd-r3 is a text generation model from patnir41. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
A 2B agent that plays Kaetram by calling typed game tools. This is round 3, the strongest checkpoint of the on-policy distillation (OPD) program.
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From the Hugging Face model README
A 2B agent that plays Kaetram by calling typed game tools. This is round 3, the strongest checkpoint of the on-policy distillation (OPD) program.
| Checkpoint | Core-3 (/30) |
|---|---|
| base Qwen3.5-2B | 12 |
| r1 | 12 |
| r2 | 15 |
| r3 (this model) | 18 |
| 4B teacher (reference) | 17 |
Core-3 = quest-stage progress summed across three archetype agents (10 stages each, 30 max) over the Foresting → Herbalist's Desperation → Rick's Roll quest chain.
The Core-3 arc is monotone — base 12 → r1 12 → r2 15 → r3 18 (+50% over base) — and at r3 every agent completes two Core-3 quest chains unseeded (Foresting and Herbalist's Desperation) where base completes one.
Honest attribution. r3 = round-3 weights plus a harness tool-recovery fix. An ablation (round-2 weights + the same recovery) scores 17/30, so ≈+1 of the 18 comes from the interface and the rest from weights — we label r3 a weights-plus-interface result and do not lean on nominally edging the 4B teacher (18 vs 17, the teacher lacking that recovery fix). The "Rick's Roll" quest remains 0/4: the same-family teacher cannot grade a skill (cooking/fishing chain) it cannot itself reliably perform.
On-policy distillation with a reverse-KL advantage against a scaffolded 4B
teacher, trained with PPO-clipped importance sampling (LoRA r=64, α=64, no rsLoRA,
bf16, 1 epoch, advantage clamp ±3, early-turn step-weight 1.5). Round 3 fits a
fresh LoRA on the merged r2 checkpoint and adds counterfactual-canonicalized grading
of malformed emissions. Full construction:
patnir41/kaetram-opd-2b.
Chain: base Qwen3.5-2B → r1 → (merge) → r2 → (merge) → r3.
Qwen3_5ForConditionalGeneration) — load directly.adapter/: the LoRA adapter alone (applies on top of the merged
r2 checkpoint).Text-only fine-tune of a multimodal-capable base; chat_template.jinja preserves
<think> on every assistant turn.
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("patnir41/kaetram-qwen3.5-2b-opd-r3", torch_dtype="bfloat16", device_map="auto")
t = AutoTokenizer.from_pretrained("patnir41/kaetram-qwen3.5-2b-opd-r3")
Single-task agent for the Kaetram Core-3 benchmark, not a general assistant. Residual malformed tool-call syntax (recovered at the harness level); "Rick's Roll" unsolved. Tokenizer-locked to the Qwen3.5-2B vocabulary.
Apache-2.0, inheriting Qwen3.5-2B
(© 2026 Alibaba Cloud). Game environment/data from
Kaetram-Open (MPL-2.0). See NOTICE.
All training data was generated by Qwen self-play — no third-party proprietary
model outputs were used.
@misc{kaetram_opd_2b_r3_2026,
title = {Kaetram Qwen3.5-2B OPD (Round 3)},
author = {patnir41},
year = {2026},
howpublished = {\url{https://huggingface.co/patnir41/kaetram-qwen3.5-2b-opd-r3}}
}