Instructions to use tunedai/philosopher-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tunedai/philosopher-14b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tunedai/philosopher-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tunedai/philosopher-14b", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tunedai/philosopher-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tunedai/philosopher-14b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tunedai/philosopher-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tunedai/philosopher-14b

SGLang

How to use tunedai/philosopher-14b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tunedai/philosopher-14b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tunedai/philosopher-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tunedai/philosopher-14b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tunedai/philosopher-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tunedai/philosopher-14b with Docker Model Runner:
```
docker model run hf.co/tunedai/philosopher-14b
```

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. 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:

Lay out the full landscape — every relevant theory, position, and thinker, including dates and major works
Go deeper on each position — the underlying assumptions, where each argument holds and breaks
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

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 (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

@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.

Contact

Website: tunedailabs.com
Email: hello@tunedailabs.com
Live demo: 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.

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