This fine tune model is inspired by Nathan Lambert's talk "Traits of Next Generation Reasoning Models". It introduces a structured multi-phase reasoning cycle for large language models (LLMs).
The fine tune model extends beyond simple question-answer pairs by adding explicit reasoning phases:
- Planning – The model outlines a step-by-step plan before attempting a solution.
- Answering – The model provides its initial solution.
- Double-Checking – The model revisits its answer, verifying correctness and coherence.
- Confidence – The model assigns a confidence score or justification for its final response. This structure encourages models to reason more transparently, self-correct, and calibrate their confidence.
Uploaded model
- Developed by: EpistemeAI
- License: apache-2.0
- Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Base model
openai/gpt-oss-20b Quantized
unsloth/gpt-oss-20b-unsloth-bnb-4bit
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EpistemeAI/gpt-oss-deepplan", dtype="auto")