gemma-4-e2b-it-Q5_K_S-GGUF

Gemma 4 E2B it — Q5_K_S GGUF

5-bit small quantized GGUF version of google/gemma-4-e2b-it.
High-fidelity quantization — slightly smaller than Q5_K_M with nearly identical quality.

Other quantizations in this series:
Q2_K · Q3_K_S · Q3_K_M · Q4_K_S · Q4_K_M · Q5_K_M · Q6_K · Q8

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File Info

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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

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Quantization Comparison

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Key Findings

• Quality: 87.7% Top-1 agreement — slightly better than Q5_K_M on this metric; exceptional code quality (92.3% on code tasks)
• Speed: 21.9 tok/s — identical to Q5_K_M in practice
• Size: 3.35 GB — 30 MB smaller than Q5_K_M (negligible difference)
• vs Q5_K_M: Q5_K_S has marginally higher Top-1 but higher KL divergence; pick either one — the practical difference is minimal
• Best for: Same as Q5_K_M; particularly strong on code generation tasks

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Usage

# llama.cpp CLI
./llama-cli -m gemma-4-e2b-q5ks.gguf -p "Explain how a transformer neural network works." -n 200

# llama-cpp-python
from llama_cpp import Llama

llm = Llama(model_path="gemma-4-e2b-q5ks.gguf", n_ctx=2048)
output = llm("Explain how a transformer neural network works.", max_tokens=200)
print(output["choices"][0]["text"])

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Hardware

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