Mistral 7B
Mistral 7B is a 7-billion-parameter language model designed for exceptional performance and efficiency in Natural Language Processing (NLP).
It outperforms previous top models such as Llama 2 13B and Llama 1 34B across various benchmarks including reasoning, mathematics, and code generation.
Leveraging grouped-query attention (GQA) and sliding window attention (SWA), Mistral 7B achieves superior performance without sacrificing efficiency.
Mistral 7B models are released under the Apache 2.0 license, facilitating easy deployment and fine-tuning for diverse tasks.
Model Architecture Details
Mistral 7B utilizes a transformer architecture with innovative features like sliding window attention (SWA) and rolling buffer cache for efficient processing of long sequences.
SWA allows each token to attend to a limited window of previous tokens, enhancing computational efficiency without compromising performance.
The rolling buffer cache optimizes memory usage by storing keys and values in a fixed-size cache, significantly reducing memory overhead during inference.
Pre-fill and chunking strategies enable efficient sequence generation by pre-filling cache with prompts and chunking long sequences for attention computation.
Comparative evaluation against Llama models across various benchmarks demonstrates Mistral 7B's superior performance, particularly in code, mathematics, and reasoning tasks.
Detailed results show Mistral 7B outperforming Llama 2 13B consistently across multiple evaluation metrics, indicating its robustness and effectiveness.
Instruction Fine-tuning Mode
Mistral 7B offers an instruction fine-tuning mode, exemplified by Mistral 7B – Instruct, which surpasses other 7B models and rivals 13B models in chatbot performance.
Fine-tuning on instruction datasets showcases Mistral 7B's adaptability and performance, making it a versatile choice for various NLP tasks.
Independent human evaluations validate Mistral 7B's superiority over competitors in generating preferred responses across diverse scenarios.
Guardrails for Front-facing Applications
Integration of guardrails ensures responsible AI generation by enforcing output constraints, promoting ethical and safe content generation.
System prompts guide Mistral 7B to generate responses within specified guardrails, enhancing utility while ensuring safety and positivity.
Content moderation capabilities empower Mistral 7B to identify and filter out potentially harmful or inappropriate content, contributing to safer online environments.
Mixtral 8x7B
Mixtral 8x7B model, a Sparse Mixture of Experts (SMoE) language model. Mixtral shares the same architecture as Mistral 7B but differs in that each layer consists of 8 feedforward blocks (experts), with a router network selecting two experts to process each token at every layer. Despite having access to 47B parameters, Mixtral only utilizes 13B active parameters during inference. Mixtral outperforms or matches Llama 2 70B and GPT-3.5 across various benchmarks, especially excelling in mathematics, code generation, and multilingual tasks.
Model Architecture
Mixtral is based on a transformer architecture with modifications to support a fully dense context length of 32k tokens and Mixture-of-Expert layers instead of traditional feedforward blocks.
Performance
Mixtral outperforms or matches Llama 2 70B across most benchmarks while using significantly fewer active parameters, demonstrating its efficiency.
Multilingual Performance
Mixtral significantly outperforms Llama 2 70B in multilingual tasks, showcasing its ability to handle various languages effectively.
Long-Range Performance
Mixtral achieves 100% retrieval accuracy on tasks requiring long context, indicating its proficiency in handling extensive sequences.
Bias Benchmarks
Mixtral exhibits less bias and more positive sentiment compared to Llama 2 70B, as shown in benchmarks like BBQ and BOLD.
Instruction Fine-tuning
Mixtral - Instruct, fine-tuned to follow instructions, outperforms other models like GPT-3.5 Turbo and Claude-2.1 on human evaluation benchmarks.
Routing Analysis
Analysis of expert selection by the router reveals structured syntactic behavior, with certain experts often chosen for specific tokens regardless of domain..
Both are under Apache 2.0 License
Small Comparison
| Aspect | Mistral 7B | Mixtral 8x7B |
| Architecture | Transformer with sliding window attention (SWA) and rolling buffer cache | Transformer with sparse mixture of experts model (SMoE) layers and router network |
| Parameters | 7 billion | 47 billion (13 billion active during inference) |
| Performance | Outperforms Llama 2 13B | Matches or surpasses Llama 2 70B, GPT-3.5 |
| Multilingual Support | Not specifically mentioned | Significantly outperforms Llama 2 70B |
| Long-Range Processing | Efficient handling of long sequences | Achieves 100% retrieval accuracy on long contexts |
| Bias Benchmarks | Not specifically mentioned | Shows less bias and more positive sentiment compared to Llama 2 70B |
| Instruction Fine-tuning | Available (Mistral 7B - Instruct) | Available (Mixtral - Instruct) - – Instruct, a chat model fine-tuned instructions using supervised fine-tuning and Direct Preference Optimization |
References
Amazing Article from Mike Chambers : https://community.aws/content/2cZUf75V80QCs8dBAzeIANl0wzU/winds-of-change---deep-dive-into-mistral-ai-models
Whitepaper Mistral 7B: https://arxiv.org/abs/2310.06825
Whitepaper Mixtral of Experts: https://arxiv.org/pdf/2401.04088.pdf