Instructions to use aimatey/PunTune-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aimatey/PunTune-0.5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("thinkingmachines/Inkling") model = PeftModel.from_pretrained(base_model, "aimatey/PunTune-0.5") - Notebooks
- Google Colab
- Kaggle
PunTune 0.5
A GRPO-trained dad-joke LoRA adapter for Inkling.
PunTune 0.5 is a rank-32 LoRA adapter on Thinking Machines' Inkling (975B MoE, 41B active), trained with GRPO on Tinker to answer any topic with exactly one original dad joke. It's the checkpoint from reward round R5 (the "objective-originality" reward: a comparative rubric plus a corpus-based originality gate) in the DadBench project.
What training actually did
The jokes aren't the point. Measured on a frozen benchmark across the full thinking-effort dial, training didn't teach the model new wit β it taught it to pick one answer and stop. Base Inkling brainstorms brilliantly and fails at the commit (rambling into truncation); PunTune reaches the same punchlines at a fraction of the tokens.
- Frozen-ladder score: 0.421 (base Inkling: 0.374 β an effort-matched lift of +0.047).
- What RL bought: reliability and economy, not humor creation.
- Training: GRPO (policy-gradient) via
tinker-cookbook, rank-32 LoRA, thinking-effort 0.9.
Sibling checkpoint: aimatey/PunTune-0.6 (reward round R6, the pre-registered "reader's-objection" rubric) β statistically tied with this one under both instruments; different reward, same model quality.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "thinkingmachines/Inkling"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", trust_remote_code=True)
model = PeftModel.from_pretrained(model, "aimatey/PunTune-0.5")
msgs = [
{"role": "system", "content": "You are a master of dad jokes. When given a topic, reply with exactly one original dad joke about that topic. No preamble, no explanation."},
{"role": "user", "content": "Topic: garden hoses"},
]
# ...generate as usual
Or serve the adapter with vLLM / SGLang via --lora-modules.
Links
- π Write-up + live arena (play it, judge it yourself): https://aimatey.co/dadbench/
- π§βπ» Code: https://github.com/aimatey-co/dadbench
- π Benchmark:
aimatey/dadbench - π§± Base model:
thinkingmachines/Inkling
License
Apache-2.0, inherited from the Inkling base model. Please credit both PunTune and Thinking Machines' Inkling.
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Base model
thinkingmachines/Inkling