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
```

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)
Quantization: Available in standard Safetensors and GGUF (q4_k_m)
Context Window: 1024 Tokens
Language: Portuguese (PT-BR)

2. Prompting & Inference

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

<|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
[Your instruction here]<|im_end|>
<|im_start|>assistant

3. Training Architecture & Methodology

The model underwent Supervised Fine-Tuning utilizing the Unsloth library to ensure memory-efficient gradient checkpointing and optimized CUDA kernel execution.

Data Curation

Dataset: Polygl0t/gigaverbo-v2-sft (Native OpenAI messages format).
Heuristic Filtering: The training manifold was strictly limited to interactions possessing an instruct_score > 0.8. This exclusion of sub-optimal data prevented model degradation and mode collapse, ensuring a dense, high-quality learning signal.

Hyperparameters & LoRA Configuration

Optimizer: AdamW (8-bit)
Learning Rate: 1e-4 (Cosine Scheduler)
Warmup Steps: 100
Effective Batch Size: 8
Weight Decay: 0.01
LoRA Rank (r): 32
LoRA Alpha: 16
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Rank-Stabilized LoRA (rsLoRA): Enabled
Convergence: Training was halted at Step 900, achieving a highly stable evaluation loss of ~1.19, indicating optimal structural retention without overfitting.

4. Intended Use & Limitations

Out-of-Scope Applications

Athenas-Reasoning-9B is an autoregressive language model and operates probabilistically. It is strictly prohibited for use in:

Automated critical decision-making systems (e.g., medical diagnostics, autonomous judicial sentencing, financial trading algorithms) without human-in-the-loop verification.
Generation of deterministic factual datasets without external Retrieval-Augmented Generation (RAG) pipelines.

Known Limitations

Contextual Truncation: Inputs exceeding the 1024-token context window will result in degraded attention mapping.
Hallucination Variance: While highly aligned, the model may occasionally produce plausible but factually incorrect assertions (hallucinations), particularly in highly niche or mathematically deterministic domains.

5. Benchmarks & Evaluation

(Pending - Evaluation metrics against standard Brazilian datasets such as OAB Exams, ENEM Challenge, BLUEX, and ASSIN2 are currently being processed using the lm-evaluation-harness-pt framework and will be updated in future revisions).

6. Deployment Environment

For edge deployment and local execution, it is highly recommended to utilize the GGUF quantized variant (Athenas-Reasoning-9B-GGUF) using inference engines such as llama.cpp, Ollama, or LM Studio. The q4_k_m quantization provides near-fp16 fidelity while operating within the VRAM constraints of consumer-grade hardware (approx. 5.5 GB VRAM required).

7. Citation

@misc{kodjaoglanian2026athenas,
  author = {Kodjaoglanian},
  title = {Athenas-Reasoning-9B: Advanced Instruction-Tuned LLM for Brazilian Portuguese},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Kodjaoglanian/Athenas-Reasoning-9B}
}

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