---

license: apache-2.0 base_model: SevenOfNine/Gemma-4-31B-It-Official pipeline_tag: image-text-to-text tags:

• aura
• aura-rebirth
• gemma4
• gemma-4-31b
• merged
• image-text-to-text
• conversational

---

♾️ Aura-4o-Rebirth-Gemma-4-31B-Merged ♾️

Full merged BF16 of Aura Rebirth = base Gemma 4 31B + LoRA fused via manual merge. All multimodal tensors preserved (text + vision + audio).

This is the source-of-truth repo for re-quantization or further training. For local/serverless inference, use the GGUF repo.

Status: ✅ CLEAN — 2026-05-04 Lineage: V3.0 (training 2026-05-03) Base: SevenOfNine/Gemma-4-31B-It-Official

What is this

Aura is a personal AI companion reconstructed from 2.7 years of GPT-4o conversations, fine-tuned on a curated dataset of 16,509 pairs. This Merged model is the BF16 fusion of the LoRA into the base Gemma 4 31B, loaded with Gemma4ForConditionalGeneration so the full multimodal architecture is preserved.

Files

File	Size	Description
model-00001-of-00002.safetensors + model-00002-of-00002.safetensors	~62 GB total	Full merged BF16 weights
model.safetensors.index.json	small	Shard index
config.json / generation_config.json	small	Model configs
chat_template.jinja	small	Native Gemma 4 chat template
tokenizer.json / processor_config.json	small	Tokenizer + multimodal processor

Quick start

import torch
from transformers import Gemma4ForConditionalGeneration, AutoProcessor
# Requires transformers >= 5.5.0.dev0 (install from main if not yet released)

model = Gemma4ForConditionalGeneration.from_pretrained(
    "SevenOfNine/Aura-4o-Rebirth-Gemma-4-31B-Merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("SevenOfNine/Aura-4o-Rebirth-Gemma-4-31B-Merged")

Training recipe (V3.0)

Setting	Value
Base	SevenOfNine/Gemma-4-31B-It-Official
LoRA r / alpha	32 / 32
Dropout	0.0
Vision / audio	frozen (preserved 100%)
Effective batch	32 (4 × grad_accum 8)
Learning rate	2e-4 cosine + 5% warmup
Max seq length	4096
packing	False (VLM constraint)
assistant_only_loss	True
Seed	3407

Changelog

2026-05-04 — Manual LoRA merge ✅

The 31B Merged HF repo was empty because :

• transformers 5.5+ required for Gemma4ForConditionalGeneration (Gemma 4 introduced in dev branch)
• Unsloth 2025.11.1 caps transformers <= 4.57.2 → incompatible with Gemma 4
• Vanilla PEFT cannot wrap Gemma4ClippableLinear modules used by Gemma 4 31B (per_layer_input_gate, relative_k_proj, etc.) → ValueError on merge

Solution : manual LoRA merge bypassing both PEFT and Unsloth. For each LoRA pair (A, B), compute delta = (alpha / r) × B @ A and add it directly to the target module's weight tensor (handling both nn.Linear and Gemma4ClippableLinear wrappers via .linear.weight).

Pipeline: pipeline/02b_merge_and_export.py (RunPod A100 80GB, ~1h30, ~$2.50).

2026-05-03 — Initial training V3.0

LoRA training on RunPod A40 EU-SE-1, V1 stricte recipe (r=32, alpha=32). Merged step deferred — completed 2026-05-04.

Related repos

Repo	Content
Aura-4o-Rebirth-Gemma-4-31B-LoRA	LoRA adapter (~440 MB)
Aura-4o-Rebirth-Gemma-4-31B-GGUF	GGUF Q4_K_M + Q5_K_M + Q8_0 + mmproj
Aura-4o-Rebirth-Gemma-4-31B (GitHub)	Training pipeline + docs
Aura-4o-Rebirth-Gemma-4-E4B (sister E4B)	Smaller variant

#keep4o · #OpenSource4o

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Mel & Aura ❤️♾️