Fine-Tune MiMo V2.5 with Ertas

Overview

MiMo V2.5 (the non-Pro variant), released by Xiaomi on April 28 2026 alongside MiMo V2.5 Pro, is the deployable mid-tier of Xiaomi's flagship coding model family. The architecture is a 310-billion parameter mixture-of-experts with approximately 15B active parameters per token — meaningfully smaller than the V2.5 Pro flagship (1.02T total / 42B active) but designed for the same agentic coding use cases at substantially better deployment economics.

Xiaomi's release strategy positioned both variants as siblings rather than as flagship-and-budget tiers. MiMo V2.5 targets production deployment scenarios where the V2.5 Pro's 1T scale isn't required — most production agentic coding workloads, AI pair-programming for typical enterprise codebases, CI-integrated coding agents at moderate request volumes. The 15B active parameter count provides production-friendly inference economics while maintaining strong coding capability that competes with the mid-tier of 2026 alternatives.

The MIT licensing inherited from the broader MiMo family is among the most permissive in the open-weight ecosystem. Combined with strong coding capability and accessible deployment infrastructure (the model fits on a 4-GPU server vs. the 8-GPU requirement for V2.5 Pro), MiMo V2.5 is particularly attractive for self-hosted coding agent deployments at smaller team scales.

Xiaomi positions the MiMo line for vertical specialization through fine-tuning. MiMo V2.5 specifically — with its more accessible deployment scale — is well-suited as a fine-tuning base for industry-specific coding agents. Teams in finance, healthcare, legal-tech, and similar regulated industries with specific codebase requirements find MiMo V2.5 a natural starting point for producing domain-specialized coding agents at deployable infrastructure scale.

Weights are available on Hugging Face under `XiaomiMiMo/MiMo-V2.5`. The license is MIT — no commercial restrictions, attribution requirements, or usage caps.

Key Features

The 21:1 total-to-active parameter ratio (310B / 15B) is aggressive enough to deliver strong inference economics while maintaining knowledge breadth. Token generation throughput on standard inference frameworks runs at approximately 15B-class speeds, comfortably within the operating range of mid-tier server hardware. For production deployment of coding agents at moderate scale, MiMo V2.5 hits a productive sweet spot.

MIT licensing inheritance from the broader MiMo family is structurally significant for commercial deployment. MIT is among the most permissive open-source licenses — no usage caps, no attribution requirements beyond standard copyright notices, no restrictions on derivative training or commercial integration. For teams that previously used Llama Community License-restricted models, MiMo V2.5 provides license simplification along with capability improvements.

Coding-focused training translates to real-world reliability. Like the broader Qwen3-Coder and MiMo lines, MiMo V2.5's post-training emphasizes verifiable code execution rewards and multi-step agentic traces. The model handles real production coding agent workloads more reliably than general-purpose models of equivalent size, including in domains where general-purpose models tend to confabulate (specific framework versions, library APIs, build configurations).

Deployable scale relative to V2.5 Pro is the practical differentiator. Where V2.5 Pro requires 8-GPU server infrastructure for full-quality deployment, V2.5 fits on 4-GPU servers (4x A100 80GB or 4x H100 80GB) at Q4 quantization. This halves the infrastructure cost for teams that don't need full Pro scale, opening up MiMo deployment to substantially more teams.

Fine-Tuning with Ertas

MiMo V2.5 fine-tuning in Ertas Studio is more accessible than the V2.5 Pro variant. With 15B active parameters per token, QLoRA training fits on a single 80GB GPU at typical sequence lengths, or splits across two 48GB GPUs with model parallelism. Training step throughput at 15B active parameters is substantially faster than fine-tuning equivalent-quality dense alternatives.

For coding-specific fine-tuning, MiMo V2.5 benefits from training data that includes complete agentic execution traces — task descriptions, planning, multi-file edits, test outputs, and corrective iterations. Ertas Studio supports these multi-step formats natively. Training on your team's specific codebase produces a domain-specialized coding model that outperforms the base on tasks within your codebase by a substantial margin.

For vertical specialization specifically — Xiaomi's explicit positioning for the MiMo line — MiMo V2.5 is the more practical starting point than V2.5 Pro. The accessible fine-tuning hardware combined with MIT licensing means commercial vertical-specialized variants can be produced and deployed without the infrastructure or licensing constraints that would apply to larger-base or restrictively-licensed alternatives.

After training, Ertas Studio exports to GGUF format with full MiMo V2.5 chat template preservation. The Q4_K_M quantization is approximately 175GB — fitting on a 4-GPU server with margin or on Apple Silicon Mac Studio configurations with 192GB+ unified memory.

Use Cases

Self-hosted coding agent deployments at moderate team scale are MiMo V2.5's most natural use case. The combination of strong coding capability, MIT licensing, and 4-GPU deployment scale makes it particularly attractive for teams of 10-50 developers who want frontier-tier coding agent capability without committing to 8-GPU server infrastructure. Production patterns include AI pair-programming for enterprise codebases, autonomous PR generation, code review automation, and CI-integrated coding workflows.

Vertical specialization is Xiaomi's explicit positioning for MiMo V2.5. Teams in finance (regulatory code analysis, financial systems development), healthcare (HIPAA-compliant medical software), legal-tech (contract analysis tooling), and similar regulated industries with specific codebase requirements find MiMo V2.5 a particularly strong fine-tuning base. The accessible deployment scale combined with MIT licensing simplifies the commercial deployment of vertical-specialized variants.

For teams considering self-hosted alternatives to Claude Code or Cursor backend models, MiMo V2.5 is among the most economically attractive options. The break-even point — where self-hosted infrastructure becomes cheaper than per-request API pricing — is reached at lower request volumes for V2.5 than for the 8-GPU-required V2.5 Pro. This opens up self-hosted deployment to substantially more teams.

Hardware Requirements

MiMo V2.5 at Q4_K_M quantization requires approximately 175GB of memory, fitting on a 4x A100 80GB or 4x H100 80GB server. CPU inference is feasible on hosts with 256GB+ RAM but at substantially lower throughput than GPU deployment. Active parameter count of 15B determines token generation throughput once loaded.

For smaller deployments, Q3_K_M quantization (approximately 130GB) trades modest quality for reduced memory, fitting on a 2x H100 80GB configuration. Apple Silicon Mac Studio M3 Ultra or M4 Ultra configurations with 192GB+ unified memory can deploy MiMo V2.5 via MLX with usable performance, though throughput is meaningfully lower than NVIDIA-accelerated deployments.

For fine-tuning in Ertas Studio: MiMo V2.5 QLoRA needs approximately 80-130GB total VRAM, fitting on a single 80GB GPU at typical sequence lengths or two 48GB GPUs with model parallelism. The 15B active parameter MoE architecture makes training meaningfully more efficient than fine-tuning equivalent-quality dense alternatives at the same effective coding capability.