A fine-tuned Qwen3-1.7B model specialized for structured debate, argumentation, and logical reasoning. Trained on a curated dataset of 35,000+ debate examples spanning policy, ethics, economics, philosophy, and technology topics.

Built with Unsloth — 2x faster training, 70% less VRAM.

Model Details

Property	Value
Base Model	Qwen/Qwen3-1.7B
Fine-tune Method	QLoRA (4-bit, rank 16)
Training Framework	Unsloth + TRL SFTTrainer
Format	GGUF (Q4_K_M quantization)
Parameters	1.7 Billion
Context Length	2048 tokens
Language	English

What This Model Does

This model is purpose-trained to function as a debate agent — it can:

Construct arguments for PRO or CON positions on any topic
Rebut opponent arguments with logical counter-evidence
Perform cross-examination with sharp, targeted questions
Detect logical fallacies (ad hominem, straw man, false dichotomy, etc.)
Cite reasoning chains rather than asserting unsupported claims

It is part of the Debate-AI project: a multi-agent debate system with separate specialized models for debating, fact-checking, and moderation.

Training Data

Trained on a mixed dataset of 35,000+ examples from:

IBM Argument Quality corpus (30k human-annotated arguments)
IBM Debate Speeches (human-scored opening speeches)
args.me debate portal corpus (382k arguments, sampled)
Reddit r/ChangeMyView, r/DebateReligion, r/PoliticalDiscussion threads
Stanford Encyclopedia of Philosophy articles
Wikipedia policy and ethics articles (80+ topics)
FEVER fact-checking dataset

All data was filtered to quality score ≥ 3/5, deduplicated by content hash, and balanced across topic categories.

Quickstart

llama.cpp

llama-cli -hf karthik0098/debate-debater-1.7b --jinja

Ollama

An Modelfile is included in the repository. To load it:

bash

ollama create debate-debater -f Modelfile
ollama run debate-debater

Python (via llama-cpp-python)

python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="karthik0098/debate-debater-1.7b",
    filename="qwen3-1.7b.Q4_K_M.gguf",
    n_ctx=2048,
)

response = llm.create_chat_completion(
    messages=[
        {
            "role": "system",
            "content": "You are an expert PRO debater. Build logical, evidence-based arguments for your assigned position."
        },
        {
            "role": "user",
            "content": "Topic: Universal Basic Income should be implemented globally\nStance: PRO\nBuild your opening argument."
        }
    ],
    temperature=0.7,
    max_tokens=512,
)
print(response["choices"][0]["message"]["content"])

Recommended Prompting

The model responds best to structured prompts:

Opening argument: