How Mixtral 8x7B Sets New Standards in Open-Source AI with Innovative Design
18 Oct 2024
The Mixtral 8x7B model sets a new standard in open-source AI performance, surpassing models like Claude-2.1, Gemini Pro, and GPT-3.5 Turbo in human evaluations.
Routing Analysis Reveals Expert Selection Patterns in Mixtral
18 Oct 2024
This analysis examines expert selection in Mixtral, focusing on whether specific experts specialize in domains like mathematics or biology.
How Instruction Fine-Tuning Elevates Mixtral – Instruct Above Competitors
18 Oct 2024
Mixtral–Instruct undergoes fine-tuning with supervised techniques and Direct Preference Optimization, achieving an impressive score of 8.30 on MT-bench.
Mixtral’s Multilingual Benchmarks, Long Range Performance, and Bias Benchmarks
18 Oct 2024
Mixtral 8x7B demonstrates outstanding performance in multilingual benchmarks, long-range context retrieval, and bias measurement.
Mixtral Outperforms Llama and GPT-3.5 Across Multiple Benchmarks
18 Oct 2024
Analyze the performance of Mixtral 8x7B against Llama 2 and GPT-3.5 across various benchmarks, including commonsense reasoning, math, and code generation.
Understanding the Mixture of Experts Layer in Mixtral
18 Oct 2024
Discover the architectural details of Mixtral, a transformer-based language model that employs SMoE layers, supporting a dense context length of 32k tokens.
Mixtral—a Multilingual Language Model Trained with a Context Size of 32k Tokens
18 Oct 2024
Discover Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model, trained with a context size of 32k tokens with access to 47B parameters.
Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Conclusion, References
3 Oct 2024
Apparate: A system that optimizes ML model inference by using adaptive early exits, reducing latency without sacrificing throughput or accuracy.
Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Additional Related Work
3 Oct 2024
Apparate: A system that optimizes ML model inference by using adaptive early exits, reducing latency without sacrificing throughput or accuracy.