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What is Span-01?

Span-01 represents a cutting-edge hyper-parallel reasoning classifier designed to detect complex patterns within large production traces in a single forward pass. Rather than generating tokens sequentially, it evaluates natural-language behavior definitions and outputs direct probabilities that indicate whether a specific behavior is present, absent, or unobservable. The model begins with a foundational training phase focused on general classification reasoning through Reinforcement Learning with Human Feedback (RLAIF), which is followed by fine-tuning aimed explicitly at behavior detection. This training methodology empowers Span-01 to effectively leverage previously unseen definitions for both new behaviors and diverse domains, thereby circumventing the constraints of a fixed taxonomy. During inference, its hyper-parallel definition branches are capable of assessing multiple behaviors at once against the same trace context. Additionally, the use of hybrid attention enhances its ability to reason over lengthy and complex inputs. Engineered for the scrutiny of production environments, Span-01 emphasizes safety, reliability, grounding, agent behavior, and response quality while tackling challenges like jailbreak and prompt injection detection alongside privacy and confidentiality issues. This innovative methodology not only makes Span-01 a noteworthy milestone in the domain but also paves the way for more efficient and thorough investigations into intricate behaviors, ultimately leading to improved decision-making processes. As a result, its deployment could significantly enhance operational standards across various industries.

What is MiMo-V2-Flash?

MiMo-V2-Flash is an advanced language model developed by Xiaomi that employs a Mixture-of-Experts (MoE) architecture, achieving a remarkable synergy between high performance and efficient inference. With an extensive 309 billion parameters, it activates only 15 billion during each inference, striking a balance between reasoning capabilities and computational efficiency. This model excels at processing lengthy contexts, making it particularly effective for tasks like long-document analysis, code generation, and complex workflows. Its unique hybrid attention mechanism combines sliding-window and global attention layers, which reduces memory usage while maintaining the capacity to grasp long-range dependencies. Moreover, the Multi-Token Prediction (MTP) feature significantly boosts inference speed by allowing multiple tokens to be processed in parallel. With the ability to generate around 150 tokens per second, MiMo-V2-Flash is specifically designed for scenarios requiring ongoing reasoning and multi-turn exchanges. The cutting-edge architecture of this model marks a noteworthy leap forward in language processing technology, demonstrating its potential applications across various domains. As such, it stands out as a formidable tool for developers and researchers alike.

Media

Media

Integrations Supported

AssemblyAI
Cognee
Google GenAI SDK
JavaScript
LangChain
LinkUp
LlamaIndex
Mastra AI
Mem0
OpenAI
OpenAI Agents API
PostHog
Python
TypeScript
Vercel

Integrations Supported

Claude Code
Hugging Face
Xiaomi MiMo
Xiaomi MiMo Studio

API Availability

Has API

API Availability

Has API

Pricing Information

$199 per month
Free Version
Free Trial Offered?

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Respan

Date Founded

2023

Company Location

United States

Company Website

www.respan.ai/blog/introducing-span-1

Company Facts

Organization Name

Xiaomi Technology

Date Founded

2010

Company Location

China

Company Website

mimo.xiaomi.com/blog/mimo-v2-flash

Categories and Features

AI Decision Models

Not specified

Categories and Features

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

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