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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.