Instructions to use EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math with Transformers:

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

pipe = pipeline("text-generation", model="EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math

SGLang

How to use EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math",
    max_seq_length=2048,
)

Docker Model Runner
How to use EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math with Docker Model Runner:
```
docker model run hf.co/EpistemeAI/ReasoningCore-1.0-3B-Instruct-r01-Reflect-Math
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

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()