Piaget-4B

Presentation

Piaget, a language model finetuned on 15k psychological and philosophical reasoning traces.

Piaget is based on Qwen3 and was finetuned on a subset of open reasoning traces from Dolphin R1 and General Reasoning.

Available sizes are: 0.6B, 1.7B, 4B, 8B.

How to use

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-4B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
   repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
   [
       {
           "role": "user",
           "content": "Create a new psychotherapeutic technique based on cybernetic principles",
       }
   ],
   tokenize=False,
   add_generation_prompt=True,
   enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

Methodology

We performed domain filtering on Dolphin R1 and General Reasoning.

Prompts were embedded, clustered with k-means (k=20 000) and majority-voted for domain labels using Qwen3-1.7B, following the Intelligent Internet pipeline.

Clusters tagged psychology or philosophy were retained for LoRA finetuning (rank=8, alpha=16, max length=2048, epoch=1, batch size=16).

This work was performed using HPC resources (Jean Zay supercomputer) from GENCI-IDRIS (Grant 20XX-AD011014205).

Inspiration

Piaget aims to reason about psychological and philosophical concepts such as self-image, emotion, and existence.

Piaget was inspired by my position paper on emotion analysis: Improving Language Models for Emotion Analysis: Insights from Cognitive Science.

Contact

Mail: [email protected]

Website: gustavecortal.com