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Model: gustavecortal/Qwen3-psychological-reasoning-1.7B
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---
license: mit
datasets:
- cognitivecomputations/dolphin-r1
- GeneralReasoning/GeneralThought-430K
- gustavecortal/PsychologicalReasoning-15k
language:
- en
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-1.7B
tags:
- philosophy
- psychology
- reasoning
- social
- emotion
---
## Presentation
Qwen3-psychological-reasoning, a language model finetuned on 15k psychological and philosophical reasoning traces.
Qwen3-psychological-reasoning is based on Qwen3 and was finetuned on a subset of open reasoning traces from [Dolphin R1](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1) and [General Reasoning](https://huggingface.co/datasets/GeneralReasoning/GeneralThought-430K).
Available sizes are: [0.6B](https://huggingface.co/gustavecortal/Qwen3-psychological-reasoning-0.6B), [1.7B](https://huggingface.co/gustavecortal/Qwen3-psychological-reasoning-1.7B), [4B](https://huggingface.co/gustavecortal/Qwen3-psychological-reasoning-4B), [8B](https://huggingface.co/gustavecortal/Qwen3-psychological-reasoning-8B).
## How to use
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch
repo = "gustavecortal/Qwen3-psychological-reasoning-1.7B"
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 perform domain filtering on [Dolphin R1](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1) and [General Reasoning](https://huggingface.co/datasets/GeneralReasoning/GeneralThought-430K).
Prompts are embedded, clustered with k-means (k=20 000) and majority-voted for domain labels using [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B), following the [Intelligent Internet pipeline](https://huggingface.co/Intelligent-Internet/II-Medical-8B-1706).
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
Qwen3-psychological-reasoning aims to reason about psychological and philosophical concepts such as self-image, emotion, and existence.
Qwen3-psychological-reasoning was inspired by my position paper on emotion analysis: [Improving Language Models for Emotion Analysis: Insights from Cognitive Science](https://aclanthology.org/2024.cmcl-1.23/).
## Contact
Mail: gustave.cortal@ens-paris-saclay.fr
Website: [gustavecortal.com](gustavecortal.com)

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{%- if tools %}
{{- '<|im_start|>system\n' }}
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{{- messages[0].content + '\n\n' }}
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{{- tool | tojson }}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
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{%- endif %}
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{%- endif %}
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{
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