Deleuze-Qwen-1.5B

A fine-tuned language model specialized in the philosophy of Gilles Deleuze, based on DeepSeek-R1-Distill-Qwen-1.5B.

Model Description

This model was fine-tuned on a corpus of Gilles Deleuze's philosophical works using LoRA (Low-Rank Adaptation) to specialize it in understanding and generating content related to Deleuzian concepts and philosophy.

Base Model

• Name: DeepSeek-R1-Distill-Qwen-1.5B
• Type: Causal Language Model
• Size: 1.5 billion parameters

Training Data

The model was trained on a dataset compiled from various books and texts by Gilles Deleuze, including:

• A Thousand Plateaus
• Difference and Repetition
• Logic of Sense
• Anti-Oedipus
• Cinema 1 & 2
• And other philosophical works

Training Procedure

• Method: LoRA fine-tuning
• LoRA Parameters:
  • Rank: 64
  • Alpha: 128
  • Dropout: 0.05
  • Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training Parameters:
  • Learning rate: 5.0e-5
  • Epochs: 3
  • Batch size: 2 (with gradient accumulation steps: 4)
  • Sequence length: 2048
  • Optimizer: AdamW
  • LR scheduler: Cosine

Intended Use

This model is intended for:

• Research on Deleuze's philosophy
• Generating explanations of Deleuzian concepts
• Exploring philosophical ideas through the lens of Deleuze's work
• Educational purposes related to continental philosophy

Limitations

• The model may occasionally generate content that sounds plausible but is philosophically inaccurate
• It has limited knowledge of philosophical works published after its training data cutoff
• The model may struggle with very specific or obscure references in Deleuze's work
• As with all language models, it may exhibit biases present in the training data

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("wisdomfunction/deleuze-qwen-1.5b")
tokenizer = AutoTokenizer.from_pretrained("wisdomfunction/deleuze-qwen-1.5b")

# Example prompt
prompt = "What are the key concepts in Deleuze's philosophy?"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Citation

If you use this model in your research, please cite:

@misc{deleuze-qwen-1.5b,
  author = {wisdomfunction},
  title = {Deleuze-Qwen-1.5B: A Fine-tuned Language Model for Deleuzian Philosophy},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/wisdomfunction/deleuze-qwen-1.5b}}
}