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Sponsored by
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AtomicChat / gemma-4-31B-it-NVFP4
Model's Last Updated: July 23 2026
text-generation
Introduction of gemma-4-31B-it-NVFP4
Model Details of gemma-4-31B-it-NVFP4
Gemma 4 31B , self-quantized to NVFP4 by Atomic Chat . Built straight from Google's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.
Highlights
- 30.7B parameters : the weights this repo quantizes.
- Context length : 256K tokens, as published by Google.
- 60 layers : Dense decoder, hybrid sliding-window (1024) and global attention.
- Modalities : Text, Image.
- Full imatrix ladder : every quant is calibrated with an importance matrix.
- Reasoning : All models in the family are designed as highly capable reasoners, with configurable thinking modes.
- Diverse & Efficient Architectures : Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.
These NVFP4s are self-quantized from the original weights , not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.
Model Overview
| Property | Value |
|---|---|
| Base model |
google/gemma-4-31B-it
|
| Parameters | 30.7B |
| Layers | 60 |
| Sliding window | 1024 tokens |
| Context length | 256K tokens |
| Vocabulary | 262K |
| Modalities | Text, Image |
| Architecture |
Dense decoder, hybrid sliding-window (1024) and global attention, 32 attention heads over 16 KV heads,
Gemma4ForConditionalGeneration
|
| This repo | NVFP4 weights |
Benchmarks
| Benchmark | Score |
|---|---|
| MMLU Pro | 85.2% |
| AIME 2026 no tools | 89.2% |
| LiveCodeBench v6 | 80.0% |
| Codeforces ELO | 2150 |
| GPQA Diamond | 84.3% |
| Tau2 (average over 3) | 76.9% |
| HLE no tools | 19.5% |
| HLE with search | 26.5% |
| BigBench Extra Hard | 74.4% |
| MMMLU | 88.4% |
| MMMU Pro | 76.9% |
| OmniDocBench 1.5 (average edit distance, lower is better) | 0.131 |
| MATH-Vision | 85.6% |
| MedXPertQA MM | 61.3% |
| MRCR v2 8 needle 128k (average) | 66.4% |
Scores are Google's published results for the base
google/gemma-4-31B-it
, not our own measurements. Quantization preserves the large majority of this;
Q4_K_M
and up stay close to full precision.
Get started
-
Atomic Chat
:
search
AtomicChat/gemma-4-31B-it-NVFP4and hit Use this model . -
vLLM:
vllm serve AtomicChat/gemma-4-31B-it-NVFP4 --max-model-len 8192
Best practices
| Parameter | Value |
|---|---|
| temperature | 1.0 |
| top_p | 0.95 |
| top_k | 64 |
Google's recommended sampling configuration for
google/gemma-4-31B-it
.
How these were made
-
Download
google/gemma-4-31B-it(original weights). -
Quantize to NVFP4 with
llm-compressorover a calibration corpus.
License
Original model by Google, released under the Apache 2.0 license. Full terms: Apache 2.0 . Quantized by Atomic Chat.
Runs of AtomicChat gemma-4-31B-it-NVFP4 on huggingface.co
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More Information About gemma-4-31B-it-NVFP4 huggingface.co Model
Url of gemma-4-31B-it-NVFP4
gemma-4-31B-it-NVFP4 huggingface.co Url
Provider of gemma-4-31B-it-NVFP4 huggingface.co
Other API from AtomicChat
Updated:August 09 2026
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