PoSTMEDIA/Lux-V1-Pro

Lux-V1-Pro: Capability-Preserving Fine-Tuning

Lux-V1-Pro is a 31 billion parameter fully fine-tuned large language model from PoSTMEDIA AI Lab, based on google/gemma-4-31B-it. It stands out due to its Capability-Preserving Full Fine-Tuning recipe, a method designed to deeply customize the model without eroding the foundational reasoning, instruction-following, and multilingual skills of the Gemma-4 base.

Key Capabilities & Features

• Full-parameter fine-tuning: Every weight of the 31B dense Gemma-4 base is updated, targeting maximum capability.
• Base capability preserved: PoSTMEDIA's unique fine-tuning approach prevents catastrophic forgetting, maintaining the base model's pretraining knowledge and reasoning skills.
• Dataset-flexible: Supports arbitrary combinations of instruction, domain, and persona datasets for fine-tuning without compromising general abilities.
• Optimized for demanding workloads: Intended for complex reasoning and generation tasks where a larger, dense backbone is beneficial.

Differentiators

Unlike naive full fine-tuning that can degrade general abilities, Lux-V1-Pro employs specific design choices:

• Conservative parameter updates: All parameters are trainable but under a tightly controlled optimization regime.
• Architecture-tuned learning rate: A calibrated lower learning rate for the 31B backbone avoids the catastrophic-forgetting common in aggressive full fine-tuning.
• Continuous evaluation: Base-model quality is evaluated throughout training to catch any regression early.

Use Cases

This model is ideal for:

• Developing high-capability enterprise assistants and reasoning agents.
• Creating domain-specialized models that must retain strong general-purpose abilities.
• Implementing persona/identity-aligned chat with robust instruction following.