Athenas Symbiote 9B GGUF

--- license: apache-2.0 language: - pt - en pipeline_tag: text-generation tags: - qwen - lora - peft - legal - portuguese - instruct - brazilian-portuguese - lm-evaluation-harness - oab - enem base_model: unsloth/Qwen3.5-9B-Base widget: - text: "Explique o princípio da presunção de inocência no direito brasileiro." example_title: "Legal Reasoning" --- # Athenas-Symbiote-9B <div align="center"> <img src="image.png" alt="Athenas-Symbiote Logo" width="300"/> </div> ## Model Details **Athenas-Symbiote-9B** is a large language model (LLM) fine-tuned for advanced reasoning, complex text interpretation, and legal/academic domain expertise, primarily optimized for Brazilian Portuguese (PT-BR). Built upon the `Qwen3.5-9B` architecture, this model leverages Parameter-Efficient Fine-Tuning (PEFT) to adapt its robust multilingual baseline specifically to the nuances, terminology, and logical structures required by the Brazilian legal and educational systems. 📄 **Technical Report**: [Available on Zenodo](https://zenodo.org/records/21418168) — full methodology, evaluation protocol, and per-edition analysis. ### Model Description - **Developed by:** Bruno Kodjaoglanian Cardoso Tulux (Independent AI Researcher) - **Model Type:** Causal Language Model (Transformer) - **Base Model:** `unsloth/Qwen3.5-9B-Base` - **Parameter Count:** 9 Billion - **Context Length:** 32,768 tokens - **Language(s) (NLP):** Portuguese (Primary), English - **License:** Apache 2.0 - **Finetuning Approach:** LoRA (Low-Rank Adaptation) --- ## Evaluation & Benchmarks The model was comprehensively evaluated using the `lm-evaluation-harness` framework. Inference was conducted on high-performance infrastructure (1x NVIDIA H100 80GB) utilizing native `bfloat16` precision to ensure metric fidelity and prevent quantization degradation. *[Insert your consolidated performance bar/radar chart here]* ### National Benchmarks (Brazil) Evaluations targeting the Brazilian cultural, legal, and educational framework. | Benchmark | Metric | Score (Zero/Few-Shot) | | :--- | :--- | :---: | | **OAB Exams** (Brazilian Bar Association) | Accuracy | **63.23%** (3-shot) | | **ENEM Challenge** (National High School Exam) | Accuracy | **80.90%** (3-shot) | > **Benchmark Datasets:** > - OAB Exams: [`maritaca-ai/oab-bench`](https://huggingface.co/datasets/maritaca-ai/oab-bench) > - ENEM Challenge:...