PoSTMEDIA/Lux-V1-Pro

Lux-V1-Pro

Lux-V1-Pro is a fully fine-tuned LLM built on top of `google/gemma-4-31B-it` by PoSTMEDIA AI Lab.

It is trained with PoSTMEDIA's in-house Capability-Preserving Full Fine-Tuning recipe — a full-parameter SFT pipeline designed so that customization does not erode the reasoning, instruction-following, and multilingual abilities of the Gemma-4 base model.

Compared to Lux-V1, Lux-V1-Pro adapts a larger, dense 31B base with all parameters trainable, targeting maximum capability for demanding downstream tasks.

Highlights

• —Full-parameter fine-tuning of Gemma-4-31B (dense) — every weight is updated
• —Base capability preserved — pretraining knowledge and reasoning skills remain intact after SFT
• —Dataset-flexible — any combination of curated instruction / domain / persona datasets can be composed into a single full-FT run
• —Maximum capability tier of the Lux line, intended for the most demanding reasoning and generation workloads

Model Overview

Capability-Preserving Full Fine-Tuning

Naive full fine-tuning of large pretrained LLMs often damages the base model's general abilities — a well-known trade-off when SFT is pushed too far. PoSTMEDIA's recipe is built specifically to avoid this.

For Lux-V1-Pro, three design choices keep the Gemma-4 base intact while still allowing deep adaptation:

1. 1.All parameters trainable, conservatively. As a dense model, Lux-V1-Pro updates every weight — but under a tightly controlled optimization regime that keeps the model in the neighborhood of the pretrained distribution.
2. 2.Architecture-tuned learning rate. A lower LR is used for the 31B dense backbone, deliberately calibrated to avoid the catastrophic-forgetting regime that aggressive full-FT typically falls into.
3. 3.Continuous base-capability evaluation. Evaluation runs at the start of training and at every epoch, so any regression in base-model quality is caught early rather than discovered post-hoc.

This means Lux-V1-Pro can be re-trained from the same base with arbitrary mixtures of datasets — identity, domain knowledge, instruction-style, reasoning — without losing what Gemma-4 already knows.

Training Configuration

Quick Start

pip install transformers accelerate

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "PoSTMEDIA/Lux-V1-Pro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "Explain why preserving base-model capability matters during fine-tuning."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Use Cases

• —High-capability enterprise assistants and reasoning agents
• —Domain-specialized models that must retain strong general-purpose abilities
• —Persona / identity-aligned chat with deep instruction following
• —Downstream tasks where the larger dense backbone outperforms the MoE tier

Safety & Limitations

• —Inherits the safety characteristics of the Gemma-4 base; output guardrails are recommended for production.
• —Not intended for medical, legal, or financial decision-making.
• —May occasionally hallucinate — human review is recommended for critical outputs.

Citation

@misc{lux_v1_pro_2026,
  title  = {Lux-V1-Pro: Capability-Preserving Full Fine-Tuning of Gemma-4-31B},
  author = {PoSTMEDIA AI Lab},
  year   = {2026},
  publisher = {Hugging Face}
}

Contact

PoSTMEDIA AI Lab

• —Email: dev.postmedia@gmail.com
• —Web: https://postmedia.ai
• —Web: https://postmedia.co.kr