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Model Details
Model Provider
xbill9
Model Tree
google/gemma-4-E4B-it-qat-q4_0-unquantized
Input Modalities
Output Modalities
Supported Functionality
google/gemma-4-E4B-it-qat-w4a16-ctGoogle publishes its own W4A16 QAT checkpoint for E4B. Two differences:
-q4_0-unquantized export exactly. Google's W4A16 export records llm-compressor's memoryless_minmax observer in its quantization_config: the QAT weights were re-quantized by round-to-nearest with each group's scale at max|w| / 7.5, which moves every weight to a shifted grid (measured at 6.67% relative error on the 12B and 31B exports; not measured tensor by tensor on E4B).lm_head.weight in bf16 (1.25 GiB), a byte-for-byte copy of the token embedding although the config ties them (tie_word_embeddings: true). This repack does not, so it is 1.25 GiB smaller on disk and on the chip.On a 3,880-record public suite read by label probability, paired record for record:
| E4B build | Where | Suite | This repack, difference (95% range) |
|---|---|---|---|
google/gemma-4-E4B-it bf16 | one v6e chip (E4B bf16 does not fit one v5e chip) | 73.1% | −0.2 points (−0.8 to +0.5) |
google/gemma-4-E4B-it-qat-w4a16-ct | the same v5e chip, same flags | 71.6% | +1.3 points (+0.7 to +2.0) |
| This repack | one v5e chip | 72.9% |
On one TPU v5e chip (v5litepod-1, 15.75 GiB HBM), vLLM TPU vllm/vllm-tpu@sha256:19a1a052… with the patches below:
| This repack | google/gemma-4-E4B-it-qat-w4a16-ct | |
|---|---|---|
| Weights resident | 8.90 GiB | 10.15 GiB |
| First-token latency, one request, median | 30.9 ms | 31.4 ms |
| Output tokens/s at 1 / 4 / 16 concurrent requests (256 tokens each) | 74.6 / 290.6 / 1,012.3 | not measured |
Logs, per-record outputs and scripts: https://github.com/xbill9/gemma4-dev/tree/main/jev-tpu-v5e1
pack-quantized, symmetric int4, group size 32, bf16 scales, activations unquantized (W4A16).per_layer_input_gate, per_layer_projection, per_layer_model_projection), which QAT also puts on the grid. This is the same set Google's export quantizes.The -qat-q4_0-unquantized export stores bf16 values that already lie on a 4-bit grid: within every group of 32 weights along the input dimension, each weight is step × level with level from −8 to 7. The repack recovers each group's step and writes the levels as packed int4. It does no new quantization. repack_q4_0.py finds, for each group, the step max|w| / m (m from 1 to 8) that reproduces all 32 values, refines it by least squares and stores it at bf16.
repack_q4_0.py verify rereads both checkpoints and checks every group: 124,149,760 groups of 32, none off the source grid, 89.9% of values bit-identical; the rest differ through the bf16 scale, by at most 1.09e-2 relative. All 1,733 unquantized tensors are byte-identical to the source, and every linear left bf16 is on the ignore list. repack_report.json and verify_report.json in this repository are that run's outputs.
Google Cloud TPU. vLLM's TPU backend needs tpu-inference #3653 (W4A16 linear layers) and #3299 (the KV-shared layers of the E models own no K/V parameters). On one v5e chip, leave HBM for the compiled model:
bash
vllm serve <this-repo> --max-model-len 2048 --gpu-memory-utilization 0.80 \ --limit-mm-per-prompt '{"image":0,"audio":0,"video":0}'NVIDIA GPUs. Untested with this checkpoint.
Gemma 4 is released by Google DeepMind under the Apache 2.0 license. This repository redistributes Google's weights in a changed container format, under the same license. The weights, training and the original model card are Google DeepMind's; the repack and this card are not affiliated with or endorsed by Google.
vllm serve <this-repo> --max-model-len 2048 --gpu-memory-utilization 0.80 \ --limit-mm-per-prompt '{"image":0,"audio":0,"video":0}'