Instructions to use EpistemeAI/ReasoningCore-Llama-3B-R1-aligned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EpistemeAI/ReasoningCore-Llama-3B-R1-aligned with Transformers:

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

pipe = pipeline("text-generation", model="EpistemeAI/ReasoningCore-Llama-3B-R1-aligned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/ReasoningCore-Llama-3B-R1-aligned")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/ReasoningCore-Llama-3B-R1-aligned")
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-Llama-3B-R1-aligned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EpistemeAI/ReasoningCore-Llama-3B-R1-aligned"
# 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-Llama-3B-R1-aligned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EpistemeAI/ReasoningCore-Llama-3B-R1-aligned

SGLang

How to use EpistemeAI/ReasoningCore-Llama-3B-R1-aligned 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-Llama-3B-R1-aligned" \
    --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-Llama-3B-R1-aligned",
		"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-Llama-3B-R1-aligned" \
        --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-Llama-3B-R1-aligned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use EpistemeAI/ReasoningCore-Llama-3B-R1-aligned 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-Llama-3B-R1-aligned 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-Llama-3B-R1-aligned 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-Llama-3B-R1-aligned to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="EpistemeAI/ReasoningCore-Llama-3B-R1-aligned",
    max_seq_length=2048,
)

Docker Model Runner
How to use EpistemeAI/ReasoningCore-Llama-3B-R1-aligned with Docker Model Runner:
```
docker model run hf.co/EpistemeAI/ReasoningCore-Llama-3B-R1-aligned
```

legolasyiu commited on Apr 10, 2025

Commit

646a66e

verified ·

1 Parent(s): 0a85301

Update README.md

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README.md +5 -2

README.md CHANGED Viewed

@@ -6,9 +6,11 @@ tags:
 - unsloth
 - llama
 - trl
-license: llama3.3
 language:
 - en
 ---
 # ReasoningCore-Llama-3B-R1-aligned
@@ -112,6 +114,7 @@ Please use "Please reason step by step, and put your final answer within \boxed{
 ---
 ### Safety Fine‑Tuning & Data Strategy
 #### Objectives:
 - Provide a reliable tool for building secure and helpful reasoning systems.
@@ -164,7 +167,7 @@ Please use "Please reason step by step, and put your final answer within \boxed{
 ### Conclusion
-**ReasoningCore‑3B** represents a significant advancement in multilingual, reasoning‑enhanced language models. Optimized for tasks requiring deep reasoning, contextual understanding, and safe, helpful interactions, it offers a powerful tool for both commercial and research applications. We invite developers and researchers to explore its capabilities and contribute to building secure, innovative AI systems.
 For further details, questions, or feedback, please email episteme.ai@proton.me

 - unsloth
 - llama
 - trl
+license: llama3.2
 language:
 - en
+datasets:
+- gretelai/gretel-safety-alignment-en-v1
 ---
 # ReasoningCore-Llama-3B-R1-aligned
 ---
 ### Safety Fine‑Tuning & Data Strategy
+SFT fine tuning wit unsloth with dataset
 #### Objectives:
 - Provide a reliable tool for building secure and helpful reasoning systems.
 ### Conclusion
+**ReasoningCore-Llama-3B-R1-aligned** represents a significant advancement in multilingual, reasoning‑enhanced language models. Optimized for tasks requiring deep reasoning, contextual understanding, and safe, helpful interactions, it offers a powerful tool for both commercial and research applications. We invite developers and researchers to explore its capabilities and contribute to building secure, innovative AI systems.
 For further details, questions, or feedback, please email episteme.ai@proton.me