---
library_name: transformers
tags:
- unsloth
- philosophy
- debate
- sft
license: apache-2.0
datasets:
- mattwesney/CoT_Philosophical_Understanding
language:
- en
base_model:
- unsloth/Qwen3-1.7B
pipeline_tag: text-generation
---

# Model Card for Averroes-R1


![20250406_1550_Purple Thought Chain_simple_compose_01jr6ae7f7fwcv2jkp99qpjr8z.png](https://cdn-uploads.huggingface.co/production/uploads/65dbedfd2f6d2dfc27763b98/sOvQj9PxajgTatFbUbV1b.png)


## Model Details

### Model Description

- **Base Model:** Qwen3-1.7B
- **Language(s) (NLP):** English
- **License:** MIT
- **Task:** Foundational Philosophical Reasoning with Chain-of-Thought (CoT)
- **Model Type:** Instruction-tuned model emphasizing logical and conceptual reasoning in philosophy
- **Dataset:** [moremilk/CoT_Philosophical_Understanding](https://huggingface.co/datasets/moremilk/CoT_Philosophical_Understanding)


## Uses

### Direct Use

The model is designed for:

- Educational use in teaching and learning philosophy
- Supporting AI assistants and chatbots focused on structured reasoning and conceptual understanding
- Serving as a tool for structured philosophical explanation
- Enhancing automated reasoning systems in conceptual and abstract domains

It is not intended to replace human philosophical analysis or provide moral/personal advice.

## Out of Scope

This model is not designed for:

- In-depth exploration of specialized or niche philosophical debates
- Providing personal philosophical advice or opinions
- Real-time discussion or analysis of ongoing philosophical issues
- Handling highly subjective or interpretive arguments lacking foundational grounding

## Bias, Risks, and Limitations

- May simplify nuanced philosophical perspectives
- Not suitable for advanced research or subjective debate
- Outputs depend on prompt clarity; ambiguous inputs may yield incomplete reasoning
- Trained for foundational reasoning, not for exhaustive domain knowledge


## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/Averroes-R1")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/Averroes-R1",
    device_map={"": 0}
)

question = """
What is the existentialist dilemma of freedom, and how do concepts like responsibility, anguish, and bad faith relate to it, according to Sartre?
"""

messages = [
    {"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
    enable_thinking = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 2200,
    temperature = 0.6,
    top_p = 0.95,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)
```


## Training Data

**Scope**

This model was fine-tuned on tasks emphasizing foundational philosophical reasoning, focusing on:

- Understanding key philosophical concepts across major branches (ethics, epistemology, metaphysics, logic, etc.)

- Explaining philosophical principles through clear examples and structured reasoning

- Highlighting the logical and conceptual steps behind philosophical inquiry

- Building a strong foundational understanding of philosophical thought

**Illustrative Examples**

- Explaining the difference between empiricism and rationalism

- Describing the reasoning behind the categorical imperative

- Analyzing simple logical fallacies within philosophical arguments

**Emphasis on Chain-of-Thought (CoT)**

The dataset explicitly teaches step-by-step reasoning, allowing the model to show intermediate thoughts when analyzing or explaining philosophical ideas.

**Focus on Foundational Knowledge**

Rather than diving into complex, specialized debates, the dataset helps build a broad, structured foundation for philosophical reasoning.