---

license: apache-2.0

base_model: google/gemma-4-E2B-it

base_model_relation: quantized

library_name: gguf

pipeline_tag: text-generation

tags:

• gguf
• llama.cpp
• dashq
• quantized

---

!DASH-Q

gemma-4-E2B-it - DASH-Q 2-bit GGUF

llama.cpp GGUF files of google/gemma-4-E2B-it quantized with

DASH-Q, at several 2-bit-class sizes. Every file uses only standard

llama.cpp tensor types (no tensor above 4 bits) and loads in any recent llama.cpp build.

| File | Type | Size | Bits / weight |

| --- | --- | --- | --- |

| gemma-4-E2B-it-DASHQ-IQ2_XXS.gguf | IQ2_XXS | 2.11 GB | 3.63 |

| gemma-4-E2B-it-DASHQ-IQ2_M.gguf | IQ2_M | 2.23 GB | 3.84 |

| gemma-4-E2B-it-DASHQ-Q2_K_XL.gguf | Q2_K_XL | 2.29 GB | 3.94 |

Text only: the vision tower is not included.

Quality vs. the unquantized model (KL divergence)

Raw-text perplexity is not a meaningful quality metric for this instruction/reasoning model (the unquantized model itself scores

poorly on WikiText-2 while generating normally), so quality is reported as KL divergence of the next-token distribution to the

unquantized reference (llama-perplexity --kl-divergence, WikiText-2, 4 x 2048 tokens):

| Type | Model | Size | Mean KLD | Median KLD | Top-1 token agreement |

| --- | --- | --- | --- | --- | --- |

| IQ2_XXS | DASH-Q IQ2_XXS | 2.11 GB | 3.03 | 1.95 | 44.9% |

| IQ2_M | unsloth UD-IQ2_M | 2.29 GB | 1.29 | 0.67 | 57.7% |

| IQ2_M | DASH-Q IQ2_M | 2.23 GB | 1.10 | 0.42 | 62.3% |

| Q2_K_XL | llama.cpp Q2_K (imatrix) | 2.97 GB | 1.28 | 0.66 | 58.5% |

| Q2_K_XL | unsloth UD-Q2_K_XL | 2.40 GB | 0.96 | 0.45 | 62.0% |

| Q2_K_XL | DASH-Q Q2_K_XL | 2.29 GB | 1.12 | 0.42 | 62.8% |

Usage

llama-cli -m gemma-4-E2B-it-DASHQ-Q2_K_XL.gguf -ngl 99 -c 8192

License

Inherits the license of the base model (google/gemma-4-E2B-it).