Before training our own models from scratch, Arcee AI was founded as a model post-training company. During this time, we focused on fine-tuning task specific models, taking open source models and enhancing them using training techniques pioneered by our research team.
This page serves as a repository of our previously released, and now retired, fine-tuned models.
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
Top Use Cases:
General-purpose task handling
Business communication
Automated document processing for mid-scale applications
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
Open-source and available on Hugging Face under the Apache-2.0 license: arcee-ai/Virtuoso-Large
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.
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
Open-source and available on Hugging Face under the Apache-2.0 license: arcee-ai/Virtuoso-Small
Top use cases:
General-purpose task handling
Business communication
Automated document processing for mid-scale applications
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.
Warm-up (SFT Phase): Quick supervised fine-tuning phase to prime the model with high-quality reasoning exemplars.
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
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
Open-source and available on Hugging Face under the Apache-2.0 license: arcee-ai/Coder-Large
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
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
Open-source and available on Hugging Face under the Apache-2.0 license: arcee-ai/Caller
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
