gemma-4-E2B-it-GGUF

Choose a checkpoint

Other Gemma 4 sizes: E4B  ·  12B

Quickstart

llama-cli \
  --hf-repo TheStageAI/gemma-4-E2B-it-GGUF \
  --hf-file gemma-4-E2B-it-S-TS-Q4_K_S.gguf

Why we recommend S

At 2.40 GB, S matches the BF16 instruction-strict IFEval score within evaluation variation. It is 276 MB smaller than M and scores at least as high on both strict IFEval measurements.

P / I means prompt-strict / instruction-strict. IFEval uses deterministic non-thinking decoding; MMLU-Pro uses sampled long-form reasoning. Only complete model-level scores are reported. A dash means not reported.

Reasoning: XS prioritizes minimum footprint. Choose S, M, or L for long-form reasoning.

• IFEval: 541 prompts, native chat template, enable_thinking=false, temperature 0.
• MMLU-Pro: 12,032 questions, vLLM, 0-shot, native chat template tiger_cot_v1, enable_thinking=true; temperature=1, top_p=0.95, top_k=64, min_p=0, presence_penalty=0, frequency_penalty=0, repetition_penalty=1, seed=42; max_model_len=20480, max_new_tokens=16384; dataset revision b189ec765aa7ed75c8acfea42df31fdae71f97be.
• The headline BF16 comparison uses instruction-strict IFEval; the raw scores are shown in the table.

MMLU-Pro includes every row shown above, including the complete XS result.

How the checkpoints are built

All four checkpoints use the same production PTQ pipeline. The precision map is the only tier-specific part.

1. Fit native GGUF codes

Calibration activations produce a curvature objective weighted by true Fisher information for each quantized projection. We adapt the scale and minimum initialization from NeUQI to that objective, then solve integer codes on the target GGUF grid with a guarded cyclic coordinate-descent solver inspired by QuantEase. A final K-quant pass tunes stored scales and minima while keeping packed codes fixed.

2. Reconstruct the deployed trajectory

Layers are calibrated in execution order against activations from the already-quantized prefix. A dense reference path measures accumulated drift. Quantization Error Propagation (QEP) adds that drift to the next reconstruction target, so later layers optimize for the inputs they receive at inference time.

3. Allocate the byte budget

XS and S can choose Q2_K through Q8_0 for each quantizable group. The optimizer trades changes in the teacher distribution against the actual encoded byte cost, including scale and minimum metadata. ANNA provides constrained configuration search. RCO provides an exact-budget route (code). For this model, RCO selected XS and ANNA selected S. M and L keep the decoder qtypes at Q4_K_M and Q8_0, respectively, and use the same reconstruction and scale-tuning stages.

After schedule selection, PTQ runs again from the source weights. Each layer is then calibrated with the final upstream precision choices.

4. Align the full model

A short affine distillation pass tunes native FP16 scales and minima while qtypes, packed codes, dense weights, and tensor layouts stay fixed. The loss matches the teacher's next-token distribution without changing file size or runtime layout.

We load-test the shipping GGUF and evaluate it on a held-out set of 3,072 sequences with next-token KL. release-manifest.json records its SHA-256, downstream evaluation IDs, and tensor metadata. Final recommendations use complete-model benchmarks.

The Hub selector controls sidebar grouping and download discovery. For XS and S it approximates the whole-file size class; release-manifest.json contains the exact tensor mix. M and L keep their decoder qtypes at Q4_K_M and Q8_0 within the same production PTQ pipeline.

Runtime memory also includes KV cache and buffers, which grow with context length.

TheStageAI deployment stack

These GGUF files target llama.cpp-compatible runtimes. edge-lm runs compressed MLX models on Macs and iPhones. ANNA searches compression configurations under size or compute constraints. The TheStageAI Platform and documentation cover compression, compilation, and serving workflows.

For a specific device, latency target, or memory budget, talk to our team.

Reproducibility

• Release: July 21, 2026.
• Base model: google/gemma-4-E2B-it at revision 70af34e2.
• Manifest: release-manifest.json records the exact base revision, byte sizes, GGUF file types, whole-file BPW, tensor inventories, SHA-256 digests, held-out KL values, and evaluation IDs.
• Runtime gate: export and load checks used llama.cpp revision bec4772f.

Citation

If you use this checkpoint, cite this release and follow the upstream model's citation guidance:

@misc{thestageai2026gemma4e2bgguf,
  author       = {{TheStageAI}},
  title        = {Gemma 4 E2B IT: TheStageAI GGUF Release},
  year         = {2026},
  month        = {jul},
  howpublished = {Hugging Face model release},
  url          = {https://huggingface.co/TheStageAI/gemma-4-E2B-it-GGUF},
  note         = {XS, S, M, and L deployment tiers},
}

• ANNA, TheStageAI's constrained compression configuration search.
• RCO: Model Compression with Exact Budget Constraints via Riemannian Manifolds (code).
• NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs.
• QuantEase: Optimization-based Quantization for Language Models.
• Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization.

License

The checkpoint weights use the upstream model's Apache-2.0 license. llama.cpp and other runtime software keep their own licenses.