---
license: cc-by-nc-4.0
language:
- en
base_model: Qwen/Qwen3-14B
pipeline_tag: text-generation
library_name: transformers
tags:
- philosophy
- dpo
- causal-reasoning
- specialist-model
- tunedai
inference: false
---

# Philosopher-14B

A 14B parameter philosophical reasoning specialist, fine-tuned from `Qwen/Qwen3-14B` via Direct Preference Optimization (DPO) on curated philosophy preference data.

**Built by [TunedAI Labs](https://tunedailabs.com).** Demonstrates that domain-specialized small models can outperform general-purpose frontier models on their target domain — at roughly **1/17 the parameters of Qwen3-235B**.

---

## What this model is for

Philosopher-14B is built for **depth and thoroughness on philosophical questions** — covering the major positions, thinkers, dates, and works for any topic in the field, then going deeper on the underlying disagreements.

Where most chat models give a polished surface-level summary of a philosophical question, Philosopher-14B is trained to:

1. Lay out the full landscape — every relevant theory, position, and thinker, including dates and major works
2. Go deeper on each position — the underlying assumptions, where each argument holds and breaks
3. Surface the root disagreement — what's actually at stake beneath the surface positions, what can be established, and what remains genuinely open

It is **not** a general-purpose assistant. It is a specialist tool for studying philosophy — useful for students, researchers, philosophy-curious readers, and anyone wanting deeper-than-Wikipedia treatment of a question.

## Quick example

> **Q:** "Could there be a fact that is true but permanently unknowable?"

A general-purpose frontier model produces a summary of Fitch's paradox plus one or two passes of agnosticism. Philosopher-14B opens with Fitch, then walks through verificationism (Schlick, Carnap), epistemic constructivism (Dummett, Wright), Williamson on knowability, then goes back through each at depth — what each position actually rules out, where the moves break, and what survives. The depth is the point.

## How to use it

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-14B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("tunedailabs/philosopher-14b")
model = PeftModel.from_pretrained(base_model, "tunedailabs/philosopher-14b")
model.eval()

messages = [{"role": "user", "content": "Do we have free will?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=2000, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

**Recommended generation settings:** `temperature=0.7`, `max_new_tokens=2000–4000` for depth. Lower temperature (0.3–0.5) for more focused outputs.

**Note on `<think>` tokens:** Qwen3-14B's reasoning mode is preserved. Responses begin with a `<think>...</think>` block showing the model's internal reasoning trace. We left this on intentionally — for philosophy, the *how* of the reasoning is part of the value. If you prefer answers without thinking blocks, pass `enable_thinking=False` to the chat template.

## Model details

| | |
|---|---|
| **Base model** | `Qwen/Qwen3-14B` |
| **Training method** | Direct Preference Optimization (DPO) |
| **Adapter type** | LoRA (provided as PEFT adapters; merge optional) |
| **Parameters** | 14B (base) + small LoRA delta |
| **Precision** | bf16 |
| **Context length** | 32K tokens (inherited from base) |
| **Languages** | English (training data was English-only) |

The training pipeline is part of TunedAI Labs' proprietary methodology and is not described in detail here. **What's released is the model behavior**; what's retained is the methodology that produced it.

## Comparison: depth-over-size

A 14B specialist vs. a 235B generalist on the same prompt is the headline result. In our internal evaluations, Philosopher-14B produces measurably more thorough, deeper, and more historically-grounded answers on philosophical questions than `Qwen/Qwen3-235B-A22B-Instruct-2507` — a frontier-class generalist roughly 17× the parameter count.

**A live side-by-side comparison demo is available at:**
[`https://tunedailabs-philosopher-demo.hf.space`](https://tunedailabs-philosopher-demo.hf.space) (private demo, contact for access)

We're releasing the weights so others can verify this directly. We invite the community to construct formal philosophical-depth evaluations against this and other specialist models — we believe small + specialized > large + general for any well-bounded knowledge domain, and this model is offered as one piece of evidence.

## What this model is *not* good at

- General-purpose chat (use a general model)
- Coding (use a coder model)
- Math, science, current events (use a frontier model)
- Recent history (knowledge cutoff matches base model)
- Languages other than English

We trained for depth on a single domain. That depth comes at the cost of breadth.

## Limitations & caveats

- **Hallucinations.** Like all LLMs, this model can confidently produce incorrect philosophical claims, misattributions, or fabricated quotations. Verify any claim before citing in academic work.
- **Western-canon bias.** Training data over-represents Western analytic and continental traditions; non-Western philosophy is included but less deeply.
- **Not a replacement for primary texts.** Use this model to orient yourself in a question, not as a substitute for reading Hume / Kant / Wittgenstein / etc. directly.
- **No moral or psychological claims about the user.** This is an academic-philosophy tool, not a counselor.

## License

**Creative Commons Attribution-NonCommercial 4.0 (CC-BY-NC-4.0).**

You may use, modify, and redistribute this model for **research, education, and personal use**. **Commercial use requires a separate license from TunedAI Labs** — contact us at hello@tunedailabs.com.

The base model `Qwen/Qwen3-14B` is licensed separately by Alibaba; please respect their terms in addition to this license.

## Citation

```bibtex
@misc{philosopher14b2026,
  title  = {Philosopher-14B: A Domain-Specialized Small Model for Philosophical Reasoning},
  author = {Mark Gentry and TunedAI Labs},
  year   = {2026},
  url    = {https://huggingface.co/tunedailabs/philosopher-14b},
  note   = {Fine-tuned via DPO from Qwen3-14B}
}
```

## Acknowledgments

Built on `Qwen/Qwen3-14B` from the Qwen team at Alibaba. Training infrastructure on [Modal](https://modal.com).

## Contact

- **Website:** [tunedailabs.com](https://tunedailabs.com)
- **Email:** hello@tunedailabs.com
- **Live demo:** [tunedailabs-philosopher-demo.hf.space](https://tunedailabs-philosopher-demo.hf.space)
- **Author:** Mark Gentry, founder TunedAI Labs

---

*Philosopher-14B is the first public release in TunedAI Labs' specialist-model program. We're applying the same methodology to causal reasoning across other domains — civil rights, intent detection, decision support. More results forthcoming.*