Instructions to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath") model = AutoModelForCausalLM.from_pretrained("EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath") 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-3B-Instruct-r01-Reflect-ThinkMath with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath" # 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-3B-Instruct-r01-Reflect-ThinkMath", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath
- SGLang
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath 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-3B-Instruct-r01-Reflect-ThinkMath" \ --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-3B-Instruct-r01-Reflect-ThinkMath", "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-3B-Instruct-r01-Reflect-ThinkMath" \ --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-3B-Instruct-r01-Reflect-ThinkMath", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath 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-3B-Instruct-r01-Reflect-ThinkMath 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-3B-Instruct-r01-Reflect-ThinkMath 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-3B-Instruct-r01-Reflect-ThinkMath to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath", max_seq_length=2048, ) - Docker Model Runner
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath with Docker Model Runner:
docker model run hf.co/EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath
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-3B-Instruct-r01-Reflect-ThinkMath" \
--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-3B-Instruct-r01-Reflect-ThinkMath",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'This is a reasoning and reflect instruction-tuned generative model in 3B size (text in/text out).
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 Numina math dataset.
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-ThinkMath"
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-ThinkMath","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()
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.
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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-3B-Instruct-r01-Reflect-ThinkMath" \ --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-3B-Instruct-r01-Reflect-ThinkMath", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'