Instructions to use EpistemeAI/ReasoningCore-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-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-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-3B-Instruct-r01-Reflect-Math")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-Math", device_map="auto")
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 Settings

vLLM

How to use EpistemeAI/ReasoningCore-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-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-3B-Instruct-r01-Reflect-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use EpistemeAI/ReasoningCore-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-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-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-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-3B-Instruct-r01-Reflect-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use EpistemeAI/ReasoningCore-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-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-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-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-3B-Instruct-r01-Reflect-Math",
    max_seq_length=2048,
)

Docker Model Runner
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-Math with Docker Model Runner:
```
docker model run hf.co/EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-Math
```

ReasoningCore-3B-Instruct-r01-Reflect-Math / README.md

legolasyiu

Update README.md

64e2d54 verified over 1 year ago

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	---
	base_model: EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- llama
	- trl
	license: llama3.2
	language:
	- en
	new_version: EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-Math
	---

	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`.

	```python
	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](https://github.com/tomtyiu/SuperTransformer-SHF)
	```python
	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()
	```


	# Uploaded model

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

	This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)