EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math

This is a Reasoning Core 1.0 reasoning and reflect instruction-tuned generative model in 3B size (text in/text out).
This is next 1.0 version of the orignal ReasoningCore-3B-Instruct-r01-Reflect-Math

Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) with GRPO fine tuning using unsloth, to align with human preferences for helpfulness and safety. Fine tune with s1 dataset from /simplescaling

Use with transformers

Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.

Make sure to update your transformers installation via pip install --upgrade transformers.

import torch
from transformers import pipeline

model_id = "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-Math"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are a powerful assistant Respond in the following format:
<reasoning>
...
</reasoning>
<reflecting>
...
</reflecting>
<answer>
...
</answer>"},
    {"role": "user", "content": "Which is bigger? 9.11 or 9.9?"},
]
outputs = pipe(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Using SuperTransformer

import SuperTransformer
# Load SuperTransformer Class,  (1) Loads Huggingface model, (2) System Prompt (3) Text/prompt (4)Max tokens
SuperTransformers = SuperTransformers("EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-Math","You are a highly knowledgeable assistant with expertise in mathematics. <reasoning>...</reasoning><reflecting>...</reflecting><answer>...</answer>","What is the area of a circle, radius=16, reason step by step", 2026)
# 8-bit quantization
SuperTransformers.HuggingFaceTransformer8bit()
# or 4-bit quantization
SuperTransformers.HuggingFaceTransformer4bit()

Thank you

Thank you for simplescaling

Uploaded model

• Developed by: EpistemeAI
• License: apache-2.0
• Finetuned from model : EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-Math

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