Instructions to use Kodjaoglanian/Athenas-Reasoning-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kodjaoglanian/Athenas-Reasoning-9B with Transformers:

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

pipe = pipeline("text-generation", model="Kodjaoglanian/Athenas-Reasoning-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kodjaoglanian/Athenas-Reasoning-9B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kodjaoglanian/Athenas-Reasoning-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kodjaoglanian/Athenas-Reasoning-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kodjaoglanian/Athenas-Reasoning-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kodjaoglanian/Athenas-Reasoning-9B

SGLang

How to use Kodjaoglanian/Athenas-Reasoning-9B 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 "Kodjaoglanian/Athenas-Reasoning-9B" \
    --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": "Kodjaoglanian/Athenas-Reasoning-9B",
		"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 "Kodjaoglanian/Athenas-Reasoning-9B" \
        --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": "Kodjaoglanian/Athenas-Reasoning-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Kodjaoglanian/Athenas-Reasoning-9B 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 Kodjaoglanian/Athenas-Reasoning-9B 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 Kodjaoglanian/Athenas-Reasoning-9B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kodjaoglanian/Athenas-Reasoning-9B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Kodjaoglanian/Athenas-Reasoning-9B",
    max_seq_length=2048,
)

Docker Model Runner
How to use Kodjaoglanian/Athenas-Reasoning-9B with Docker Model Runner:
```
docker model run hf.co/Kodjaoglanian/Athenas-Reasoning-9B
```

Kodjaoglanian commited on Jul 21

Commit

49fa28a

verified ·

1 Parent(s): 055fbf5

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -19

README.md CHANGED Viewed

@@ -1,40 +1,27 @@
-```yaml
 ---
 base_model: unsloth/Qwen3.5-9B-Base
 language:
 - pt
 license: apache-2.0
 tags:
-- unsloth
 - transformers
 - qwen
-- instruction-tuning
-- reasoning
 - gguf
 - pt-br
-- llama-cpp
 pipeline_tag: text-generation
-widget:
-- text: |
-    <|im_start|>system
-    Você é a Athenas, uma assistente virtual brasileira de código aberto, projetada para ser altamente inteligente, versátil e precisa nas suas respostas.<|im_end|>
-    <|im_start|>user
-    Explique a diferença entre dolo e culpa no direito penal brasileiro.<|im_end|>
-    <|im_start|>assistant
 ---
-```
 # Model Card: Athenas-Reasoning-9B
 ## 1. Model Summary
-**Athenas-Reasoning-9B** is an advanced, instruction-tuned Large Language Model (LLM) specifically engineered for the Brazilian Portuguese language. Built upon the robust Qwen 3.5 (9B) architecture, this model has been fine-tuned to excel in complex logical reasoning, academic synthesis, and high-fidelity text generation.
-The model was optimized using Low-Rank Adaptation (LoRA) on a heavily filtered, high-quality subset of conversational data, ensuring strict adherence to user instructions and professional output formatting.
 ### Model Identifiers
 * **Developer:** Kodjaoglanian
 * **Architecture:** Qwen 3.5 (9 Billion Parameters)
 * **Training Stage:** Supervised Fine-Tuning (SFT)
@@ -47,11 +34,9 @@ The model was optimized using Low-Rank Adaptation (LoRA) on a heavily filtered,
 To achieve optimal performance, inferences must strictly follow the **ChatML** formatting used during training. The model is highly responsive to its designated system prompt.
 ### Recommended System Prompt
 > *"Você é a Athenas, uma assistente virtual brasileira de código aberto, projetada para ser altamente inteligente, versátil e precisa nas suas respostas."*
 ### ChatML Template Structure
 ```text
 <|im_start|>system
 Você é a Athenas, uma assistente virtual brasileira de código aberto, projetada para ser altamente inteligente, versátil e precisa nas suas respostas.<|im_end|>

 ---
 base_model: unsloth/Qwen3.5-9B-Base
 language:
 - pt
 license: apache-2.0
 tags:
+- text-generation-inference
 - transformers
+- unsloth
 - qwen
 - gguf
 - pt-br
 pipeline_tag: text-generation
 ---
 # Model Card: Athenas-Reasoning-9B
 ## 1. Model Summary
+**Athenas-Reasoning-9B** is an advanced, instruction-tuned Large Language Model (LLM) specifically engineered for the Brazilian Portuguese language. Built upon the robust Qwen 3.5 (9B) architecture, this model has been fine-tuned to excel in complex logical reasoning, academic synthesis, and high-fidelity text generation.
+The model was optimized using Low-Rank Adaptation (LoRA) on a heavily filtered, high-quality subset of conversational data, ensuring strict adherence to user instructions and professional output formatting.
 ### Model Identifiers
 * **Developer:** Kodjaoglanian
 * **Architecture:** Qwen 3.5 (9 Billion Parameters)
 * **Training Stage:** Supervised Fine-Tuning (SFT)
 To achieve optimal performance, inferences must strictly follow the **ChatML** formatting used during training. The model is highly responsive to its designated system prompt.
 ### Recommended System Prompt
 > *"Você é a Athenas, uma assistente virtual brasileira de código aberto, projetada para ser altamente inteligente, versátil e precisa nas suas respostas."*
 ### ChatML Template Structure
 ```text
 <|im_start|>system
 Você é a Athenas, uma assistente virtual brasileira de código aberto, projetada para ser altamente inteligente, versátil e precisa nas suas respostas.<|im_end|>