Prophet-Qwen3-4B-SFT Open-Source Multilingual Philosophy Dialogue Model - Focusing on Philosophical and Profound Topic Discussions

Prophet Qwen3 4b Sft

Developed by radm

A multilingual philosophical dialogue model fine-tuned based on Qwen/Qwen3-4B, specializing in philosophy and profound topics

Large Language Model

Transformers

Supports Multiple Languages#Philosophical Dialogue #Multilingual SFT #4096 Long Context

Downloads 25

Release Time : 5/5/2025

Model Overview

This is a causal language model supervised fine-tuned (SFT) on Qwen3-4B, focused on handling dialogues about philosophy and profound topics, supporting multiple languages.

Model Features

Philosophical Focus

Specially fine-tuned for philosophical and profound topics, with superior performance in these areas

Multilingual Support

Supports philosophical dialogues in 13 languages

Thinking Mode Switching

Can switch thinking modes via special instructions

Efficient Fine-tuning

Utilizes Unsloth library for efficient fine-tuning with optimization techniques like LoRA

Model Capabilities

Philosophical dialogue generation

Multilingual text generation

Complex concept explanation

Profound topic discussion

Use Cases

Education & Research

Philosophical Concept Exploration

Used for assisting in philosophy courses or student self-study

Helps students understand complex philosophical concepts

Cross-cultural Philosophy Comparison

Comparing philosophical thoughts across different cultural backgrounds

Provides multilingual and multicultural philosophical perspectives

Creative Writing

Philosophical Fiction Creation

Assists in creating literary works containing philosophical ideas

Generates storylines and dialogues rich in philosophical insights

🚀 Model Card for prophet-qwen3-4b-sft

This is a fine - tuned model based on Qwen/Qwen3 - 4B, focusing on philosophical and esoteric topics and supporting multiple languages.

🚀 Quick Start

The model card provides detailed information about the prophet-qwen3-4b-sft model, including its details, uses, biases, and training procedures.

✨ Features

Multilingual Support: Supports multiple languages such as Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.
Philosophical and Esoteric Focus: Specializes in philosophical and esoteric topics.
Switchable Thinking Mode: Allows users to switch the model's thinking mode by adding \n/no_think to user prompts or system messages.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Model Details

Model Description

This model is a fine - tuned version of Qwen/Qwen3 - 4B. Training was conducted with Supervised Fine - Tuning (SFT) using the Unsloth library on a custom reasoning and non - reasoning dataset.

The model focuses on philosophical and esoteric topics and is multilingual.

Property	Details
Developed by	radm
Finetuned from model	`Qwen/Qwen3 - 4B`
Model Type	Causal LM based on the Llama3 architecture
Language(s)	Multilingual
License	Apache 2.0 (inherited from base model)

Uses

This is a reasoning model, but you can add \n/no_think to user prompts or system messages to switch the model's thinking mode from turn to turn.

Out - of - Scope Use

The model is not designed for generating harmful, unethical, biased, or factually incorrect content. Performance on tasks outside its training domain (philosophical/esoteric chat) may be suboptimal.

Bias, Risks, and Limitations

The model inherits biases from its base model (Qwen/Qwen3 - 4B) and the fine - tuning datasets. It may generate plausible - sounding but incorrect or nonsensical information, especially on complex topics. Its "understanding" is based on patterns in the data, not genuine comprehension or consciousness. Use the outputs with critical judgment.

Training Details

Training Data

The model was fine - tuned using a custom reasoning and non - reasoning dataset.

Training Procedure

Training was performed using the Unsloth library integrated with trl's SFTTrainer.

Property	Details
Framework	Unsloth + SFTTrainer
Base Model	`Qwen/Qwen3 - 4B`
LoRA Configuration	`r`: 768 `lora_alpha`: 768 `lora_dropout`: 0.0 `bias`: "none" `target_modules`: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"] `use_rslora`: True `use_dora`: True
Precision	Auto (bfloat16 / float16)
Quantization (load)	4 - bit
Optimizer	Paged AdamW 8 - bit
Learning Rate	2e - 5
LR Scheduler	Cosine
Warmup Steps	10
Batch Size (per device)	1
Gradient Accumulation Steps	64 (Effective Batch Size: 64)
Max Sequence Length	4096
Epochs	1

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