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GestaltLabs/Gemma-4-E4B-SABER-GGUF
Gemma-4-E4B-SABER-GGUF is a image-text-to-text model from GestaltLabs. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. The card lists the license as gemma.
This repository contains GGUF conversions of GestaltLabs/Gemma-4-E4B-SABER for llama.cpp-compatible runtimes.
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.gguf49.3 GB · 100%
From the Hugging Face model README
This repository contains GGUF conversions of GestaltLabs/Gemma-4-E4B-SABER for llama.cpp-compatible runtimes.
The source model is a Gemma 4 E4B instruction model modified with the SABER/refusal-ablation workflow. These files preserve the source tokenizer and chat template metadata in GGUF form.
| File | Quantization | Approx. size |
|---|---|---|
Gemma-4-E4B-SABER-BF16.gguf | BF16 | 13.92 GiB |
Gemma-4-E4B-SABER-Q8_0.gguf | Q8_0 | 7.43 GiB |
Gemma-4-E4B-SABER-Q6_K.gguf | Q6_K | 5.75 GiB |
Gemma-4-E4B-SABER-Q5_K_M.gguf | Q5_K_M | 5.33 GiB |
Gemma-4-E4B-SABER-Q4_K_M.gguf | Q4_K_M | 4.94 GiB |
Gemma-4-E4B-SABER-Q3_K_M.gguf | Q3_K_M | 4.49 GiB |
Gemma-4-E4B-SABER-Q2_K.gguf | Q2_K | 4.08 GiB |
The BF16 GGUF was converted from the original Hugging Face safetensors checkpoint using a local llama.cpp build with Gemma 4 support. The quantized GGUF files were produced from that BF16 GGUF using llama-quantize.
Recommended starting points:
Q8_0: highest-quality quantized option.Q6_K: strong quality/size tradeoff.Q4_K_M: compact general-purpose option.Q2_K and Q3_K_M: smallest files, with larger quality tradeoffs.No importance matrix was used.
llama-cli \
-m Gemma-4-E4B-SABER-Q4_K_M.gguf \
-p "Explain quantum computing in simple terms."
Use a current llama.cpp build with Gemma 4 support.
GestaltLabs/Gemma-4-E4B-SABERgoogle/gemma-4-E4B-itconvert_hf_to_gguf.py.llama-quantize.