uyu-2-28B

Introduction

uyu-2-28B is a language model specialized for role-playing. It is derived from google/gemma-4-31B-it, structurally pruned with Global Iterative Structured Pruning (GISP), and then fine-tuned on English and Korean question-answering data.

vllm serve mente-ai/uyu-2-28B \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 2048 \
  --enforce-eager \
  --served-model-name uyu-2-28b

Model details

Property	Value
Model name	uyu-2-28b
Parameters	28,181,549,312
Weight format	BF16 Safetensors, 6 shards
Weight size	56,363,098,744 bytes
Layers	60
Hidden size	5,376
Configured maximum position length	262,144 tokens
Validated serving length	2,048 tokens
Modalities	Text only
Languages	Korean and English
Primary use	Conversation and role-play
Base checkpoint	google/gemma-4-31B-it
Pruning	GISP structured pruning
Recovery fine-tuning	LoRA on Korean and English question-answering data, merged into the weights

Pruning method: GISP

This checkpoint was produced through a staged sequence of globally ranked structured pruning passes. Each pass used the same Korean role-playing calibration set, BF16 inference, a fixed maximum sequence length, and teacher-forced language-model loss restricted to assistant response tokens. The model weights were frozen during calibration; only temporary scalar pruning gates received gradients.

1. MLP group calibration and pruning

• Each layer's high-dimensional MLP intermediate space was divided into equal-sized contiguous channel groups.
• A scalar gate initialized to one was inserted after the gated MLP activation for every group. A backward pass for each calibration sample produced each gate's sensitivity score.
• Group importance was accumulated as the sample mean of |gate * d(loss)/d(gate)|.
• All eligible groups were ranked globally rather than layer by layer. The first and final decoder layers were protected.
• A small fraction of the lowest-scoring groups was selected. The global ranking concentrated most of these groups in a narrow range of later decoder layers.

2. Attention recalibration after MLP pruning

The selected MLP mask was applied before attention importance was measured, so the second pass estimated attention sensitivity on the already reduced MLP structure rather than on the original model.

• In a sliding-window layer, one pruning set contains a paired group of Q heads, their associated K/V head, and the corresponding input channels of o_proj.
• In a full-attention layer, one set contains a paired group of Q heads and the corresponding o_proj input channels. The shared global K/V heads were retained.
• Each set received a scalar gate and was scored with the same assistant-only |gate * d(loss)/d(gate)| criterion.
• A globally selected fraction of the lowest-scoring attention sets was removed across a subset of layers. Most selected sets came from sliding-window attention, with a smaller number coming from full-attention layers. This removed a substantial number of Q heads together with their paired sliding-window K/V heads.

3. Physical structural conversion and recovery

The masks were converted into smaller tensors rather than left as runtime zeros. Selected MLP rows were removed from gate_proj and up_proj, with the matching columns removed from down_proj. Selected attention rows were removed from Q/K/V projections and the matching columns from o_proj. All decoder layers and the full residual stream were retained, which leaves different MLP widths and attention head counts in different layers.

The conversion also removed the multimodal weight keys, making this a text-only checkpoint. A recovery LoRA was then trained on Korean and English question-answering data and merged into the pruned weights. Exact per-layer removed groups, heads, and retained tensor shapes are recorded in pruning_config.json and duplicated under pruned_shapes in config.json.

Standalone Transformers status

The included modeling_uyu2.py is required by the vLLM Transformers modeling backend. Direct AutoModelForCausalLM.generate() is not supported in this release: the checkpoint loads without missing or mismatched tensors, but direct Transformers generation did not match the validated vLLM output in the current test environment. Do not use the snippet below for inference until this section is updated in a later release.

pip install "transformers>=5.13.0,<5.14" accelerate safetensors torch

Evaluation status

The merged checkpoint has been smoke-tested for coherent Korean and English generation with vLLM main and the bundled integration matching vllm-uyu2 0.3.2. Validation included a three-conversation batch and a 1,983-token prompt. With a 2,048-token model length and --gpu-memory-utilization 0.80, vLLM reported 60,800 GPU KV cache tokens and 29.69x maximum concurrency on the validation system. These capacity figures are hardware- and configuration-dependent.

No standardized benchmark results are published yet. The reported parameter and pruning statistics are derived directly from the distributed checkpoint and pruning metadata, not from an estimated model label. The checkpoint also contains 60 non-parameter BF16 buffer elements.

Limitations

• This is a role-playing model and may generate fictional, inaccurate, biased, unsafe, or otherwise undesirable content.
• It has not received a comprehensive safety evaluation. Applications should add safeguards appropriate to their use case.
• Pruning and task fine-tuning can reduce capabilities relative to the base checkpoint, especially outside Korean/English conversation and role-play.
• The configured maximum context is inherited from the base architecture, but this release has only been locally validated at short context lengths.
• This release is text-only despite retaining some tokenizer special tokens for base-tokenizer compatibility.
• Standalone Transformers generation is not supported in this release. Use the vLLM plugin path.

License and attribution

The model weights and repository code are released under Apache License 2.0. This is a modified derivative of google/gemma-4-31B-it; Google DeepMind did not create or endorse uyu-2-28B. See NOTICE for modification and attribution details.