Instructions to use Aquiles-ai/Athenea-4B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aquiles-ai/Athenea-4B-Thinking with Transformers:

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

pipe = pipeline("text-generation", model="Aquiles-ai/Athenea-4B-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Aquiles-ai/Athenea-4B-Thinking")
model = AutoModelForMultimodalLM.from_pretrained("Aquiles-ai/Athenea-4B-Thinking")
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 Aquiles-ai/Athenea-4B-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aquiles-ai/Athenea-4B-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aquiles-ai/Athenea-4B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aquiles-ai/Athenea-4B-Thinking

SGLang

How to use Aquiles-ai/Athenea-4B-Thinking 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 "Aquiles-ai/Athenea-4B-Thinking" \
    --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": "Aquiles-ai/Athenea-4B-Thinking",
		"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 "Aquiles-ai/Athenea-4B-Thinking" \
        --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": "Aquiles-ai/Athenea-4B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aquiles-ai/Athenea-4B-Thinking with Docker Model Runner:
```
docker model run hf.co/Aquiles-ai/Athenea-4B-Thinking
```

Fredtt3 commited on Nov 5, 2025

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@@ -24,6 +24,29 @@ tags:
 ![image](atheneamodel.png)
 ## 💻 Usage
 ### Installation

 ![image](atheneamodel.png)
+**Athenea-4B-Thinking** is a fine-tuned version of [huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated), designed as a **general-purpose reasoning model** capable of handling mathematical, multilingual, and conversational reasoning tasks.
+Trained on diverse, high-quality reasoning data with explicit `<think>` and `</think>` traces, this model represents the **core generalist** version of the Athenea family, intended as a foundation for specialized reasoning variants.
+> ⚠️ **Important Note:** This model uses an *abliterated (uncensored)* base version, providing full expressive freedom and unrestricted output generation. Users are fully responsible for any use or content produced by the model. It is intended exclusively for research and experimentation purposes.
+## 🎯 Model Description
+Athenea-4B-Thinking leverages the structured reasoning framework of Huihui-Qwen3 and expands it across multiple domains and languages. It serves as a **multidomain reasoning model**, performing well in both conversational and analytical contexts.
+Key features:
+* **Step-by-step reasoning** within `<think>` blocks
+* **General reasoning across math, language, and logic**
+* **Multilingual understanding and response generation**
+* **Uncensored reasoning output** for transparency
+* **Improved logical consistency** through focused fine-tuning
+* **Compatible with open inference frameworks** (Transformers, vLLM, etc.)
+The model was fine-tuned using the dataset [Aquiles-ai/Athenea-40k](https://huggingface.co/datasets/Aquiles-ai/Athenea-40k).
+> Note: Fine-tuning was performed using **Kronos**, Aquiles-ai’s proprietary enterprise fine-tuning system.
 ## 💻 Usage
 ### Installation