metadata
base_model: unsloth/qwen3-14b-unsloth
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- reasoning
- math
- code-generation
license: apache-2.0
language:
- en
datasets:
- open-thoughts/OpenThoughts2-1M
library_name: transformers
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)