gemma-4-e2b-it-Q5_K_M-GGUF

Gemma 4 E2B it — Q5_K_M GGUF

5-bit medium quantized GGUF version of google/gemma-4-e2b-it.
High-fidelity quantization — outputs are very close to F16 with much lower KL divergence than Q4.

Other quantizations in this series:
Q2_K · Q3_K_S · Q3_K_M · Q4_K_S · Q4_K_M · Q5_K_S · 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: KL divergence drops dramatically from Q4_K_M (0.34 → 0.12) — the probability distributions are much closer to F16
• Speed: 22.0 tok/s — 3.9× faster than F16
• Size: 3.38 GB — only 190 MB more than Q4_K_M
• vs Q5_K_S: Essentially identical speed; Q5_K_M has lower KL (0.12 vs 0.15) while Q5_K_S has marginally better Top-1 (87.7 vs 86.9%). Both are excellent.
• Best for: Tasks where output fidelity matters more than raw speed — complex reasoning, multi-step math, detailed code generation

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Usage

# llama.cpp CLI
./llama-cli -m gemma-4-e2b-q5km.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-q5km.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