Qwen3-14B-Griffon

Developed by: Daemontatox
License: Apache-2.0
Finetuned from: unsloth/qwen3-14b-unsloth

Model Overview

This is a fine-tuned version of the Qwen3-14B model using the high-quality OpenThoughts2-1M dataset. Fine-tuned with Unsloth’s TRL-compatible framework and LoRA for efficient performance, this model is optimized for advanced reasoning tasks, especially in math, logic puzzles, code generation, and step-by-step problem solving.

Training Dataset

• Dataset: OpenThoughts2-1M
• Source: A synthetic dataset curated and expanded by the OpenThoughts team
• Volume: ~1.1M high-quality examples
• Content Type: Multi-turn reasoning, math proofs, algorithmic code generation, logical deduction, and structured conversations
• Tools Used: Curator Viewer

This dataset builds upon OpenThoughts-114k and integrates strong reasoning-centric data sources like OpenR1-Math and KodCode.

Intended Use

This model is particularly suited for:

• Chain-of-thought and step-by-step reasoning
• Code generation with logical structure
• Educational tools for math and programming
• AI agents requiring multi-turn problem-solving

Limitations

• English-only focus (does not generalize well to other languages)
• May hallucinate factual content despite reasoning depth
• Inherits possible biases from synthetic pretraining data

Example Usage

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

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Daemontatox/Qwen3_14B_Griffon")
pipe(messages)

Training Details

Framework: TRL + LoRA with Unsloth acceleration

Epochs/Steps: Custom fine-tuning on ~1M samples

Hardware: Single-node A100 80GB / similar high-VRAM setup

Objective: Enhance multi-domain reasoning under compute-efficient constraints

---