Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+  - en
+  - fr
+  - de
+  - es
+  - pt
+  - it
+  - ja
+  - ko
+  - ru
+  - zh
+  - ar
+  - fa
+  - id
+  - ms
+  - ne
+  - pl
+  - ro
+  - sr
+  - sv
+  - tr
+  - uk
+  - vi
+  - hi
+  - bn
+base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
+library_name: vllm
+inference: false
+
+---
+
+# Aqui-VL 24B Mistral
+
+Aqui-VL 24B Mistral is an advanced language model based on Mistral Small 3.1, designed to deliver exceptional performance while remaining accessible on consumer-grade hardware. This is the first open weights model from Aqui Solutions, the company behind [AquiGPT](https://aquigpt.com.br). With 23.6 billion parameters, it can run efficiently on a single RTX 4090 GPU or a 32GB Mac, making cutting-edge AI capabilities available to researchers, developers, and enthusiasts.
+
+## Key Features
+
+- **Consumer Hardware Compatible**: Runs on single RTX 4090 or 32GB Mac
+- **Multimodal Capabilities**: Text, vision, chart analysis, and document understanding
+- **128K Context Window**: Handle long documents and complex conversations
+- **Strong Instruction Following**: Significantly improved over base Mistral Small 3.1
+- **Exceptional Code Generation**: Best-in-class coding performance
+
+## Hardware Requirements
+
+### Minimum Requirements
+- **GPU**: RTX 4090 (24GB VRAM) or equivalent
+- **Mac**: 32GB unified memory (Apple Silicon recommended)
+- **RAM**: 32GB system memory (for GPU setups)
+- **Storage**: 20GB available space (for model and overhead)
+
+### Recommended Setup
+- **GPU**: RTX 4090 with adequate cooling
+- **CPU**: Modern multi-core processor
+- **RAM**: 64GB+ for optimal performance
+- **Storage**: NVMe SSD for faster model loading
+
+## Performance Benchmarks
+
+Aqui-VL 24B Mistral demonstrates competitive performance across multiple domains:
+
+| Benchmark | Aqui-VL 24B Mistral | Mistral Small 3.1 | Llama 3.1 70B |
+|-----------|------------------|-------------------|----------------|
+| **IFEval** (Instruction Following) | 65.3% | 55.6% | **87.5%** |
+| **MMLU** (General Knowledge) | 80.9% | 80.5% | **86.0%** |
+| **GPQA** (Science Q&A) | 44.7% | 44.4% | **46.7%** |
+| **HumanEval** (Coding) | **92.5%** | 88.9% | 80.5% |
+| **MATH** (Mathematics) | 69.3% | **69.5%** | 68.0% |
+| **MMMU** (General Vision) | **64.0%** | 62.5% | N/A* |
+| **ChartQA** (Chart Analysis) | **87.6%** | 86.2% | N/A* |
+| **DocVQA** (Document Analysis) | **94.9%** | 94.1% | N/A* |
+| **Average Text Performance** | 70.5% | 67.8% | **73.7%** |
+| **Average Vision Performance** | **82.2%** | 80.9% | N/A* |
+
+*Llama 3.1 70B does not include vision capabilities
+
+## Model Specifications
+
+- **Parameters**: 23.6 billion
+- **Context Window**: 128,000 tokens
+- **Knowledge Cutoff**: December 2023
+- **Architecture**: mistral (transformer-based with vision)
+- **Languages**: Multilingual support with strong English, French and Portuguese performance
+
+## Installation & Usage
+
+### Quick Start with Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+model_name = "aquigpt/aqui-vl-24b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Generate text
+prompt = "Explain quantum computing in simple terms:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Using with Ollama
+
+```bash
+# Pull the model
+ollama pull aquiffoo/aqui-vl-24b
+
+# Run interactive chat
+ollama run aquiffoo/aqui-vl-24b
+```
+
+### Using with llama.cpp
+
+```bash
+# Download quantized model (Q4_K_M, 14.4GB)
+wget https://huggingface.co/aquigpt/aqui-vl-24b/resolve/main/aqui-vl-24b-q4_k_m.gguf
+
+# Run with llama.cpp
+./main -m aqui-vl-24b-q4_k_m.gguf -p "Your prompt here" -n 100
+```
+
+## Use Cases
+
+### Code Generation & Programming
+With an 88.9% score on HumanEval, Aqui-VL 24B Mistral excels at:
+- Writing clean, efficient code in multiple languages
+- Debugging and code review
+- Algorithm implementation
+- Technical documentation
+
+### Document & Chart Analysis
+Strong vision capabilities enable:
+- PDF document analysis and Q&A
+- Chart and graph interpretation
+- Scientific paper comprehension
+- Business report analysis
+
+### General Assistance
+- Research and information synthesis
+- Creative writing and content generation
+- Mathematical problem solving
+- Multilingual translation and communication
+
+## Quantization
+
+Aqui-VL 24B Mistral is available exclusively in Q4_K_M quantization, optimized for the best balance of performance and hardware compatibility:
+
+- **Format**: Q4_K_M quantization
+- **Size**: 14.4GB
+- **VRAM Usage**: ~16GB (with overhead)
+- **Compatible with**: RTX 4090, 32GB Mac, and similar hardware
+- **Performance**: Excellent quality retention with 4-bit quantization
+
+## Fine-tuning & Customization
+
+Aqui-VL 24B Mistral supports:
+- Parameter-efficient fine-tuning (LoRA, QLoRA)
+- Full fine-tuning for specialized domains
+- Custom tokenizer training
+- Multi-modal fine-tuning for specific vision tasks
+
+## Limitations
+
+- Knowledge cutoff at December 2023
+- May occasionally produce hallucinations
+- Performance varies with quantization level
+- Requires significant computational resources for optimal performance
+
+## License
+
+This model is released under the [Apache 2.0 License](LICENSE), making it suitable for both research and commercial applications.
+
+## Support
+
+For questions and support regarding Aqui-VL 24B Mistral, please visit the [Hugging Face repository](https://huggingface.co/aquigpt/aqui-vl-24b) and use the community discussions section.
+
+## Acknowledgments
+
+Built upon the excellent foundation of Mistral Small 3.1 by Mistral AI. Special thanks to the open-source community for tools and datasets that made this model possible.
+
+---
+
+*Copyright 2025 Aqui Solutions. All rights reserved*