Cogito LLM 14B - On the Way to Superintelligence - Install and Test Locally
AI Summary
Overview
- Video presentation by Fahad Miza on Cojito AI models.
Key Points
- Introduction to Cojito AI Models
- Newly released models ranging from 3 billion to 70 billion parameters.
- Models available in various flavors, including Quen fine-tune and Llama 3.1.
- Quen flavor chosen for installation due to issues with Llama 4.
- Goals of Cojito
- Aiming towards general super intelligence, indicated as an ongoing process.
- Discussed the concept of alignment to ensure AI behaviors match human values.
- Introduced the alignment strategy IDA (Iterated Distillation and Amplification).
- Installation & Performance Testing
- Process of creating a conda environment and installing prerequisites (Torch and Transformers).
- Running inference using Hugging Face’s transformer pipeline.
- Various prompts tested, yielding coherent and creative responses (e.g., customer review of a fictitious cafe).
- Multilingual capabilities and coding tasks evaluated and found satisfactory.
- Emphasized cohesive and concise responses, maintaining engagement.
- Benchmark performance shown favorable against existing models.
- Technical Performance
- Model consumes approximately 28.5GB of VRAM during trials.
- Responses range from storytelling to logical reasoning and programming tasks.
- Addressed SQL query optimization and Rust program generation, yielding effective results.
- Concluding observations
- Despite strong performance in specific tasks, emphasized that true super intelligence remains unachieved.