AtomicChat / gemma-4-31B-it-GGUF

huggingface.co
Total runs: 2.7K
24-hour runs: 41
7-day runs: 477
30-day runs: -1.3K
Model's Last Updated: July 24 2026
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Introduction of gemma-4-31B-it-GGUF

Model Details of gemma-4-31B-it-GGUF


Gemma 4 31B

Gemma 4 31B , self-quantized to GGUF by Atomic Chat . Built straight from Google's original weights with a per-tensor importance matrix. Runs fully offline.

Highlights
  • Multimodal — natively handles text and image input and generates text output.
  • Built-in reasoning — designed as a capable reasoner with a configurable thinking mode set via the system prompt.
  • 256K context window for long documents and codebases.
  • Multilingual — out-of-the-box support for 35+ languages, pre-trained on 140+ languages.
  • Coding & agentic — native function-calling support and notable coding-benchmark improvements.
  • Native system prompt — native support for the system role for more structured conversations.

These GGUFs are self-quantized from the original weights , not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

Always pass --jinja so the Gemma 4 31B chat template is applied. Without it the model can emit malformed turns.

Model Overview
Property Value
Base model google/gemma-4-31B-it
Total parameters 30.7B (Dense)
Layers 60
Context length 256K tokens
Vocabulary 262K
Architecture Dense decoder, hybrid local/global attention with Proportional RoPE
This repo GGUF quants (imatrix) + vision mmproj

Gemma 4 31B is multimodal. This repo ships the mmproj-gemma4-31b-it-f16.gguf vision projector. With -hf it is pulled automatically; otherwise pass --mmproj . Use llama-mtmd-cli or llama-server to feed images.

Gemma 4 31B benchmark scores

Scores are Google's published results for the base google/gemma-4-31B-it . Quantization preserves the large majority of this; Q4_K_M and up sit within a point or two of full precision.

Choosing a quant
Quant Size Notes
Q2_K 11.9 GB Smallest. Minimal RAM, clear quality drop.
IQ3_M 14.2 GB Beats Q3 at similar size thanks to imatrix. Best low-RAM pick.
Q3_K_M 15.3 GB Low quality but usable.
Q3_K_L 16.6 GB A step above Q3_K_M.
IQ4_XS 16.7 GB Excellent quality for size. Recommended low-bit.
Q4_K_S 17.8 GB Compact Q4, fast.
Q4_K_M 18.7 GB Recommended default. Best balance of size, speed and quality.
UD-Q4_K_XL 19.0 GB Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
Q5_K_S 19.6 GB Higher quality.
Q5_K_M 17.4 GB Higher quality, low loss.
Q6_K 7.6 GB Near lossless.
Q8_0 18.4 GB Effectively lossless, reference quality.

Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Gemma 4 31B locally with:

  • Atomic Chat : the easiest path. Open the app, search AtomicChat/gemma4-31b-it-GGUF , pick a quant, hit Use this model .
  • llama.cpp: llama-server -hf AtomicChat/gemma4-31b-it-GGUF:Q4_K_M --jinja -c 8192
  • Ollama: ollama run hf.co/AtomicChat/gemma4-31b-it-GGUF:Q4_K_M
  • LM Studio / Jan: search the repo id, download any quant.
Best practices
Parameter Value
temperature 1.0
top_p 0.95
top_k 64

Google's standardized sampling configuration recommended across all use cases.

Run in llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server
./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/gemma4-31b-it-GGUF:UD-Q4_K_XL \
    --jinja -ngl 99 -c 8192 -fa on
How these were made
  1. Download google/gemma-4-31B-it (original weights).
  2. Convert to f16 GGUF with llama.cpp .
  3. Build an importance matrix over calibration_datav3 (100 chunks).
  4. Quantize the full ladder with --imatrix .
  5. UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0 .
License

Original model by Google DeepMind, released under the Apache 2.0 license. Quantized by Atomic Chat.

Runs of AtomicChat gemma-4-31B-it-GGUF on huggingface.co

2.7K
Total runs
41
24-hour runs
469
3-day runs
477
7-day runs
-1.3K
30-day runs

More Information About gemma-4-31B-it-GGUF huggingface.co Model

More gemma-4-31B-it-GGUF license Visit here:

https://choosealicense.com/licenses/apache-2.0

gemma-4-31B-it-GGUF huggingface.co

gemma-4-31B-it-GGUF huggingface.co is an AI model on huggingface.co that provides gemma-4-31B-it-GGUF's model effect (), which can be used instantly with this AtomicChat gemma-4-31B-it-GGUF model. huggingface.co supports a free trial of the gemma-4-31B-it-GGUF model, and also provides paid use of the gemma-4-31B-it-GGUF. Support call gemma-4-31B-it-GGUF model through api, including Node.js, Python, http.

gemma-4-31B-it-GGUF huggingface.co Url

https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF

AtomicChat gemma-4-31B-it-GGUF online free

gemma-4-31B-it-GGUF huggingface.co is an online trial and call api platform, which integrates gemma-4-31B-it-GGUF's modeling effects, including api services, and provides a free online trial of gemma-4-31B-it-GGUF, you can try gemma-4-31B-it-GGUF online for free by clicking the link below.

AtomicChat gemma-4-31B-it-GGUF online free url in huggingface.co:

https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF

gemma-4-31B-it-GGUF install

gemma-4-31B-it-GGUF is an open source model from GitHub that offers a free installation service, and any user can find gemma-4-31B-it-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of gemma-4-31B-it-GGUF install, users can directly use gemma-4-31B-it-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

gemma-4-31B-it-GGUF install url in huggingface.co:

https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF

Url of gemma-4-31B-it-GGUF

gemma-4-31B-it-GGUF huggingface.co Url

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