Instructions to use EpistemeAI/ReasoningCore-3B-R01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EpistemeAI/ReasoningCore-3B-R01 with Transformers:

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

pipe = pipeline("text-generation", model="EpistemeAI/ReasoningCore-3B-R01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/ReasoningCore-3B-R01")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/ReasoningCore-3B-R01", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EpistemeAI/ReasoningCore-3B-R01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EpistemeAI/ReasoningCore-3B-R01"
# 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-R01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EpistemeAI/ReasoningCore-3B-R01

SGLang

How to use EpistemeAI/ReasoningCore-3B-R01 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-R01" \
    --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-R01",
		"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-R01" \
        --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-R01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use EpistemeAI/ReasoningCore-3B-R01 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-R01 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-R01 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-R01 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="EpistemeAI/ReasoningCore-3B-R01",
    max_seq_length=2048,
)

Docker Model Runner
How to use EpistemeAI/ReasoningCore-3B-R01 with Docker Model Runner:
```
docker model run hf.co/EpistemeAI/ReasoningCore-3B-R01
```

legolasyiu commited on Feb 9, 2025

Commit

b979cec

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@@ -23,7 +23,7 @@ datasets:
 - **Model Developer:** EpitemeAI
 - **Model Architecture:**
-  ReasoningCore‑3B is an auto‑regressive language model built on an optimized transformer architecture. It incorporates specialized reasoning pathways and has been fine‑tuned using both supervised learning and reinforcement learning with human feedback (RLHF) to align with human expectations for clarity, accuracy, and safety in complex tasks.
 |                                | Training Data                                    | Params | Input Modalities      | Output Modalities            | Context Length | GQA | Shared Embeddings | Token Count    | Knowledge Cutoff  |
 |--------------------------------|--------------------------------------------------|--------|-----------------------|------------------------------|----------------|-----|-------------------|----------------|-------------------|

 - **Model Developer:** EpitemeAI
 - **Model Architecture:**
+  ReasoningCore‑3B is an auto‑regressive language model built on an optimized transformer architecture. It incorporates specialized reasoning pathways and has been fine‑tuned using Group Robust Preference Optimization(GRPO), and both supervised learning and reinforcement learning with human feedback (RLHF) to align with human expectations for clarity, accuracy, and safety in complex tasks.
 |                                | Training Data                                    | Params | Input Modalities      | Output Modalities            | Context Length | GQA | Shared Embeddings | Token Count    | Knowledge Cutoff  |
 |--------------------------------|--------------------------------------------------|--------|-----------------------|------------------------------|----------------|-----|-------------------|----------------|-------------------|