--- license: apache-2.0 tags: - adam - curious-architecture - instruction-tuned - conversational-ai - 2b-parameters library_name: transformers pipeline_tag: text-generation --- # Adam: Instruction-Tuned Conversational AI
2B Parameters Curious Architecture Instruction Tuned 8K Context
## 🚀 Model Overview **Adam** is a powerful 2 billion parameter language model built with the Curious architecture, specifically instruction-tuned for high-quality conversational AI and task completion. This model represents the next generation of efficient, instruction-tuned language models optimized for natural conversations. ## ✨ Key Features - **🏗️ Native Curious Architecture**: Custom `CuriousForCausalLM` architecture with Curious-specific optimizations - **🎯 Instruction-Tuned**: Fine-tuned for conversational AI and task completion - **⚡ Efficient**: 2B parameters with optimized inference - **💬 Conversational**: Specialized for natural dialogue and helpful responses - **🔧 Advanced Features**: Sliding window attention, logit softcapping, and enhanced activations ## 📊 Model Specifications | Parameter | Value | |-----------|-------| | **Architecture** | CuriousForCausalLM | | **Model Type** | curious_text | | **Parameters** | ~2.6B | | **Context Length** | 8,192 tokens | | **Vocabulary** | 256,000 tokens | | **Training** | Instruction-tuned | | **Curious Version** | 2.0 | ## 🎯 Capabilities - **Natural Conversations**: Engaging and contextually aware dialogue - **Question Answering**: Accurate responses to diverse queries - **Creative Writing**: Poetry, stories, and creative content generation - **Code Assistance**: Programming help and code generation - **Mathematical Reasoning**: Problem-solving and calculations - **Instruction Following**: Precise task execution and completion ## 🚀 Quick Start ### Interactive Chat ```python pip install requirements.txt ``` ```python # Use the included chat interface python chat_with_adam.py to talk to adam. ``` ## 🏗️ Curious Architecture Features - **Enhanced Attention**: Advanced attention mechanisms for better context understanding - **Sliding Window**: Efficient processing of long sequences - **Logit Softcapping**: Improved generation stability - **Optimized Activations**: GELU with PyTorch tanh for better performance - **Instruction Tuning**: Specialized for conversational AI tasks ## 📈 Performance - **Quality**: High-quality instruction-tuned responses - **Speed**: Optimized for efficient inference - **Memory**: ~5GB model size - **Hardware**: GPU recommended for best performance - **Context**: 8K token context window ## 🔧 Technical Details ### Model Configuration ```json { "architectures": ["CuriousForCausalLM"], "model_type": "curious_text", "hidden_size": 2304, "num_attention_heads": 8, "num_hidden_layers": 26, "max_position_embeddings": 8192, "curious_version": "2.0", "curious_instruction_tuned": true } ``` ### Generation Parameters ## 🎨 Use Cases - **Chatbots**: Conversational AI applications - **Assistants**: Task-oriented AI helpers - **Creative Writing**: Content generation and editing - **Education**: Tutoring and explanation - **Coding**: Programming assistance - **Research**: Information synthesis and analysis ## ⚠️ Limitations - **Context Length**: Limited to 8K tokens - **Training Data**: Cutoff date applies to training data - **Bias**: May reflect biases in training data - **Factual Accuracy**: Should be verified for critical applications ## 🙏 Acknowledgments - Built with the Curious Architecture Framework - Instruction-tuned for conversational AI - Powered by the Curious Architecture Framework v2.0 ---
Adam: The Future of Conversational AI
Built with ❤️ using the Curious Architecture Framework