DuoNeural / Gemma-4-E4B-Q4_K_M

huggingface.co
Total runs: 1.6K
24-hour runs: 7
7-day runs: 135
30-day runs: 647
Model's Last Updated: April 29 2026

Introduction of Gemma-4-E4B-Q4_K_M

Model Details of Gemma-4-E4B-Q4_K_M

Gemma 4 E4B — Q4_K_M GGUF

A direct Q4_K_M quantization of Google's google/gemma-4-e4b-it — no fine-tuning, no persona, just the stock instruction-tuned model compressed to run on consumer hardware.

Performance
Hardware Speed
NVIDIA GTX 1070 (8GB VRAM) ~32 tok/s

Tested locally via LM Studio and Ollama. No parameter tweaks required.

Files
File Size Description
gemma-4-e4b-it.Q4_K_M.gguf 5.0 GB Main model — load this in Ollama/LM Studio
gemma-4-e4b-it.BF16-mmproj.gguf 946 MB Multimodal projector (vision/audio, optional)
Usage
Ollama
ollama pull hf.co/DuoNeural/Gemma-4-E4B-Q4_K_M
ollama run hf.co/DuoNeural/Gemma-4-E4B-Q4_K_M
LM Studio

Search DuoNeural/Gemma-4-E4B-Q4_K_M in the LM Studio model browser and download gemma-4-e4b-it.Q4_K_M.gguf .

llama.cpp
llama-cli -m gemma-4-e4b-it.Q4_K_M.gguf --chat-template gemma -ngl 99
About the Base Model

Gemma 4 E4B uses a Per-Layer Embeddings (PLE) architecture — it has ~8B total parameters but only ~4.5B are active during inference, giving it the reasoning depth of an 8B model at the compute cost of a 4B. The Q4_K_M format compresses weights to ~4.5 bits per parameter using mixed-precision block quantization, preserving attention layers at higher fidelity than feed-forward layers.

  • Context window: 128K tokens (recommended ≤8K for GTX 1070)
  • Architecture: Dense transformer + sliding window attention (512 token local window)
  • Modalities: Text, Image, Audio (multimodal projector file required for vision/audio)
  • License: Gemma Terms of Use
Related Models

Runs of DuoNeural Gemma-4-E4B-Q4_K_M on huggingface.co

1.6K
Total runs
7
24-hour runs
121
3-day runs
135
7-day runs
647
30-day runs

More Information About Gemma-4-E4B-Q4_K_M huggingface.co Model

Gemma-4-E4B-Q4_K_M huggingface.co

Gemma-4-E4B-Q4_K_M huggingface.co is an AI model on huggingface.co that provides Gemma-4-E4B-Q4_K_M's model effect (), which can be used instantly with this DuoNeural Gemma-4-E4B-Q4_K_M model. huggingface.co supports a free trial of the Gemma-4-E4B-Q4_K_M model, and also provides paid use of the Gemma-4-E4B-Q4_K_M. Support call Gemma-4-E4B-Q4_K_M model through api, including Node.js, Python, http.

Gemma-4-E4B-Q4_K_M huggingface.co Url

https://huggingface.co/DuoNeural/Gemma-4-E4B-Q4_K_M

DuoNeural Gemma-4-E4B-Q4_K_M online free

Gemma-4-E4B-Q4_K_M huggingface.co is an online trial and call api platform, which integrates Gemma-4-E4B-Q4_K_M's modeling effects, including api services, and provides a free online trial of Gemma-4-E4B-Q4_K_M, you can try Gemma-4-E4B-Q4_K_M online for free by clicking the link below.

DuoNeural Gemma-4-E4B-Q4_K_M online free url in huggingface.co:

https://huggingface.co/DuoNeural/Gemma-4-E4B-Q4_K_M

Gemma-4-E4B-Q4_K_M install

Gemma-4-E4B-Q4_K_M is an open source model from GitHub that offers a free installation service, and any user can find Gemma-4-E4B-Q4_K_M on GitHub to install. At the same time, huggingface.co provides the effect of Gemma-4-E4B-Q4_K_M install, users can directly use Gemma-4-E4B-Q4_K_M installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Gemma-4-E4B-Q4_K_M install url in huggingface.co:

https://huggingface.co/DuoNeural/Gemma-4-E4B-Q4_K_M

Url of Gemma-4-E4B-Q4_K_M

Gemma-4-E4B-Q4_K_M huggingface.co Url

Provider of Gemma-4-E4B-Q4_K_M huggingface.co

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