chat.md

Feature	Meta’s Llama 3.1 70B	Mistral Large 2 128B
Launch Date	July 23, 2024	Not prominently documented
Parameter Size	70 billion	128 billion
Context Window	128K tokens	Not specified
Architecture	Modality-specific encoders, cross-model attention modules	Mixture of Experts (MoE)
Open-Source	Yes	Not specified
Key Strengths	- Open-source - Advanced reasoning capabilities - Extended context window - Multimodal understanding	- Scalability - Efficiency in large-scale computations - Superior performance in benchmarks
Key Weaknesses	- Less reliable in visual comprehension tasks	- Challenges in deployment for high-precision scenarios
Supervised Fine-Tuning (SFT)	Yes, with diverse datasets	Yes, with diverse datasets
Multi-Image Reasoning	Strong capabilities	Significant capabilities
Chain-of-Thought Reasoning	Strong capabilities	Enhanced performance
Evaluation Benchmarks	MMLU, AGIEval	Various benchmarks
Performance on MMLU Benchmark	Score of 79.5	Not specified
Performance on AGIEval Benchmark	Score of 63.0	Not specified
Visual Backbone Freezing	Not specified	Employed in MiniGPT-v2, not directly related to these models
Linear Projection Layer	Not specified	Employed in MiniGPT-v2, not directly related to these models
Meta-Transformer Framework	Not specified	Task-specific heads (MLPs)
Active Learning Platforms	Not specified	Tools like Cleanlab and Voxel51
Accessibility and Usability	- Multilingual support - Extended context window - Real-time inference support	Not specified
Cost-Effectiveness	Lower latency and cost-effective	Not specified
Parameter-Efficient Fine-Tuning Techniques	Yes, LoRA	Yes, LoRA
Comparative Performance	- Outperforms competitors in ARC and DROP benchmarks - Better throughput and latency	- Superior scalability - Enhanced accuracy
Ideal Use Cases	Multilingual conversational agents, complex interaction handling	High scalability and efficiency in large-scale computations