# Model Selection Discover our collection of Small Language Models (SLMs) fine-tuned by Arcee AI, each optimized for specific tasks and designed to power efficient, production-ready applications.
Blitz - General Purpose ### **Arcee Blitz** * **Description:** Arcee-Blitz (24B) is a new Mistral-based 24B model distilled from DeepSeek, designed to be both **fast and efficient**. We view it as a practical “workhorse” model that can tackle a range of tasks without the overhead of larger architectures. * **#Parameters:** 24B * **Base Model:** Mistral-Small-24B-Instruct-2501 * Open-source and available on Hugging Face under the Apache-2.0 license: [arcee-ai/Arcee-Blitz](https://huggingface.co/arcee-ai/Arcee-Blitz) * **Top Use Cases:** * General-purpose task handling * Business communication * Automated document processing for mid-scale applications

Blitz

Virtuoso (Small, Large, Medium) - General Purpose ### **Virtuoso Large** * **Description:** Our most powerful and versatile general-purpose model, designed to excel at handling complex and varied tasks across domains. With state-of-the-art performance, it offers unparalleled capability for nuanced understanding, contextual adaptability, and high accuracy. * **#Parameters:** 72B * **Base Model:** Qwen-2.5-72B * API Access is Available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra.md) * **Top Use Cases:** * Advanced content creation, such as technical writing and creative storytelling * Data summarization and report generation for cross-functional domains * Detailed knowledge synthesis and deep-dive insights from diverse datasets * Multilingual support for international operations and communications ### **Virtuoso Medium** * **Description:** A versatile and powerful model, capable of handling complex and varied tasks with precision and adaptability across multiple domains. Ideal for dynamic use cases requiring significant computational power. * **#Parameters:** 32B * **Base Model:** Qwen-2.5-32B * API Access is Available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra.md) * **Top Use Cases:** * Content generation * Knowledge retrieval * Advanced language understanding * Comprehensive data interpretation ### **Virtuoso Small** * **Description:** A streamlined version of Virtuoso, maintaining robust capabilities for handling complex tasks across domains while offering enhanced cost-efficiency and quicker response times. * **#Parameters:** 14B * **Base Model:** Qwen-2.5-14B * API access is available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra.md) * Open-source and available on Hugging Face under the Apache-2.0 license: [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) * **Top use cases:** * General-purpose task handling * Business communication * Automated document processing for mid-scale applications \`

Virtuoso

Coder (Small, Large) - Coding ### Coder Large * **Description:** A high-performance model tailored for intricate programming tasks, Coder-Large thrives in software development environments. With its focus on efficiency, reliability, and adaptability, it supports developers in crafting, debugging, and refining code for complex systems. * **#Parameters:** 32B * **Base Model:** Qwen-2.5-32B-Instruct * Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra.md) * **Top use cases:** * Writing modular, reusable code across various programming languages * Debugging and optimizing performance in large-scale applications * Generating efficient algorithms for computationally intensive tasks * Supporting DevOps processes, such as script automation and CI/CD pipelines ### Coder Small * **Description:** A compact, high-performance coding model designed for efficient programming tasks, including generating code, debugging, and optimizing scripts for smaller projects. * **#Parameters:** 14B * **Base Model:** Qwen-2.5-32B-Instruct * **Top use cases:** * Lightweight development tasks * Automated code reviews * Generating templates or prototypes quickly, code completion

Coder

Caller (Large) - Tool Use and Function Call ### Caller * **Description:** Engineered for seamless integrations, Caller-Large is a robust model optimized for managing complex tool-based interactions and API function calls. Its strength lies in precise execution, intelligent orchestration, and effective communication between systems, making it indispensable for sophisticated automation pipelines. * **#Parameters:** 32B * **Base Model:** Qwen-2.5-32B * API Access is Available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra.md) * **Top use cases:** * Managing integrations between CRMs, ERPs, and other enterprise systems * Running multi-step workflows with intelligent condition handling * Orchestrating external tool interactions like calendar scheduling, email parsing, or data extraction * Real-time monitoring and diagnostics in IoT or SaaS environments

Caller

Maestro - Reasoning ### Maestro * **Description:** An advanced reasoning model optimized for high-performance enterprise applications. Building on the innovative training techniques first deployed in maestro-7b-preview, Maestro-32B offers significantly enhanced reasoning capabilities at scale, rivaling or surpassing leading models like OpenAI’s O1 and DeepSeek’s R1, but at substantially reduced computational costs. * **#Parameters:** 32B * **Base Model:** Qwen-2.5-32B * API Access is Available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra.md) * Hybrid training method: 1. Warm-up (SFT Phase): Quick supervised fine-tuning phase to prime the model with high-quality reasoning exemplars. 2. RL Optimization Phase: Utilizes Reinforcement Learning techniques, specifically designed to boost logical coherence, depth of reasoning, and accurate inference by encouraging problem-solving from fundamental principles. * **Top Use Cases:** * Enterprise decision support systems * Complex analytical and logical inference tasks * Automated research and analysis workflows * Generative reasoning for technical and professional contexts

Maestro

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