dahus/gemma-4-e2b-it-Q2_K-GGUF

Gemma 4 E2B it — Q2_K GGUF

2-bit quantized GGUF version of google/gemma-4-e2b-it. Smallest and fastest variant in the series — use only if RAM is the hard constraint.

Other quantizations in this series: Q3_K_S · Q3_K_M · Q4_K_S · Q4_K_M · Q5_K_S · Q5_K_M · Q6_K · Q8

File Info

Benchmark Results

Tested across 4 categories (Math, Logic, Code, Science), 3 prompts each. Greedy decoding, 200 max new tokens. Metrics compare logit distributions vs F16 baseline.

Results by Category

Quantization Comparison

Key Findings

• —Quality: Significant degradation — only 32% Top-1 agreement with F16; output can be incoherent (see sample below)
• —Speed: 31.6 tok/s — fastest in the series, 5.6× faster than F16
• —Size: 2.78 GB — fits in under 4 GB RAM
• —Best for: Extreme RAM-constrained environments where some output quality loss is acceptable; not recommended for reasoning or code tasks

⚠️ Warning: Q2K produces visibly broken outputs on this model. Sample response to a math prompt repeated token garbage (`skills skills skills...`). Consider Q3K_S or higher for usable results.

Usage

# llama.cpp CLI
./llama-cli -m gemma-4-e2b-q2k.gguf -p "Solve step by step: 2 + 2 = ?" -n 200

# llama-cpp-python
from llama_cpp import Llama

llm = Llama(model_path="gemma-4-e2b-q2k.gguf", n_ctx=2048)
output = llm("Solve step by step: 2 + 2 = ?", max_tokens=200)
print(output["choices"][0]["text"])

Hardware

Tested on: CPU inference (llama.cpp) Context: 2048 tokens | Greedy decoding