An experimental model utilizes a unique, advanced form of supervised tuning. This training program loads the model and then loads the data from the dataset. It provides the data during inference time. Then, it trains the Large Language Model (LLM). During inference, it checks if the model reaches the desired answer or goal. If not, it continues training until the answer or solution is achieved.

Context Window: 128000

Installation

Update latest transformers

pip install -U transformers

System prompt suggested for math:

python

system_prompt="""
Please reason step by step, and put your final answer within \boxed{}
Respond in the following format:
<problem>
...
</problem>
<solution>
...
</solution>"""

Inference

python

from transformers import pipeline
model_id = "EpistemeAI/OpenReasoner-Llama-3.2-3B-rs1.01"
pipe = pipeline(
    "text-generation", 
    model=model_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)
print(pipe("What is larger 9.9 or 9.11?"))

Reference

Thank you so much to Hugging Face H4 and the dataset: Math-500

We use this as evaluator. It was not directly trained, it was used as a test

Uploaded model

Developed by: EpistemeAI
License: apache-2.0
Finetuned from model : EpistemeAI/OpenReasoner-Llama-3.2-3B-rs1.0

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

EpistemeAI/OpenReason-Llama-3.2-3B-rs1.01

Model Introduction

Installation

Reference

Uploaded model

EpistemeAI/
OpenReason-Llama-3.2-3B-rs1.01