Model Card for Gemma-4-E4B-IT (Aria Quant Bundle, q8)

Model Details

Model Description

• Developed by: Google
• Quantized and distributed by: Aria Compute
• Model type: Dense Transformer decoder-only (multimodal base: image/audio + text inputs, text outputs; this bundle ships the text backbone)
• Language(s): English (primary), Chinese, and 30+ additional languages
• License: Apache 2.0
• Finetuned from model: google/gemma-4-e4b-it

Model Sources

• Original Repository: google-gemma/gemma-4
• Original Paper: Gemma 4 Technical Report
• Finetuned from: google/gemma-4-e4b-it
• Aria Compute Dashboard: ariacompute.com/dashboard/models
• Aria Engine: ariacompute.com

Uses

Direct Use

• On-device chat and conversational assistants
• Real-time text completion and basic code snippet generation
• Instruction-following tasks for mobile and IoT applications
• Lightweight text embeddings for on-device retrieval and classification
• Short-form summarization of notifications, messages, and local content
• Local document analysis up to 32K context (chunked)

Target Devices

Note: KV cache is compact thanks to GQA (2 KV heads for 8 query heads) and hybrid attention — 36 of 42 layers use sliding-window attention (window 512, KV bounded by the window), so only the 6 full-attention layers scale KV with context. Combined with tie-word-embeddings, this keeps 128K context practical on ~4.0 GB-class devices.

Out-of-Scope Use

• Long-form creative writing (>2K tokens per generation)
• Mathematical theorem proving or formal verification
• Full program/application synthesis
• Multimodal input (image/audio encoding pipeline is pending audit for this quantized bundle — text-only in this release)
• Real-time audio/speech processing (use Aria speech models)
• Safety-critical decision systems without human oversight
• Tasks requiring factual precision beyond the model's ~4B parameter capacity

How to Get Started with the Model

Download from Aria Compute

Quantization Recipe

• Bundle size: ~3.8 GB (BF16 text backbone: ~8.0 GB)
• Generation quality: Awaiting gen_quant_eval audit. The uniform 8-bit per-channel recipe is the highest-fidelity bundle in the Aria lineup — near-lossless versus FP16. Per-channel codebooks preserve per-output-channel distribution characteristics. Formal quality benchmarks against FP16 and other Aria quant recipes are pending
• Calibration-free: Hadamard pre-processing + per-channel quantization, no task-specific calibration data required
• Other precision options: Also available for Gemma-4-E4B-IT: gemma-4-e4b-it_q326_channel (mixed precision, attn 4-bit + FFN ~3-bit, recommended quality-size trade-off) and gemma-4-e4b-it_q4 (uniform per-channel 4-bit, smallest bundle)

Model Architecture

• Hybrid attention: 36/42 layers use sliding-window attention (window 512); 6/42 layers use standard full softmax attention — dense-attention KV cost is confined to a few layers
• GQA (Grouped Query Attention): 2 KV heads serving 8 query heads — KV Cache memory is 4× smaller than full attention, enabling 128K context on edge devices
• Per-layer input projections: a 256-dim layer-input embedding is projected to the 2,560-dim hidden state at each layer — compact embedding table with shared layer-input processing
• GeGLU activation: GELU with tanh approximation gating, efficient for on-device inference
• RoPE position encoding: 1M base frequency for full-attention layers (partial rotary factor 0.25) with 10K base for sliding-window layers, supporting 128K context
• RMSNorm pre-normalization: Lightweight normalization before each sub-layer
• Final logit softcapping: Output logits capped at ±30.0 for training stability
• Tied embeddings: tie_word_embeddings: true — input and output embeddings share weights, reducing footprint

Bias, Risks, and Limitations

Limitations

• Reasoning depth: Multi-step logical reasoning is limited for a 4B-class model. Verify outputs in high-stakes scenarios; consider larger Gemma 4 variants for reasoning tasks.
• Mathematics: Simple arithmetic may be attempted but is unreliable. Advanced quantitative reasoning is out of scope. Use larger models for mathematical tasks.
• Code generation: Capable of single-line completions and basic snippets; unreliable for multi-line code or structured programs.
• Factual knowledge: Moderate world knowledge due to ~4B parameter scale. Always verify factual claims against authoritative sources. This model is better suited for instruction-following and general text tasks than encyclopedic knowledge retrieval.
• Instruction following: Handles simple single-constraint instructions. Complex multi-constraint prompts may cause degradation, especially at longer contexts.
• Quantization drift: Uniform 8-bit per-channel quantization exhibits minimal generation drift versus FP16 thanks to near-lossless precision. For a smaller bundle, use gemma-4-e4b-it_q326_channel (mixed precision) or gemma-4-e4b-it_q4 (uniform 4-bit).

Bias and Risks

• Bias: As with all large language models trained on web-scale data, Gemma may reflect societal biases present in its training corpus. Evaluate outputs before deployment in sensitive domains (hiring, healthcare, law).
• Toxicity: The instruction-tuned model has been safety-aligned with RLHF. However, no safety filter is exhaustive. Consider an additional output classifier in high-risk environments.
• Hallucination: May generate plausible-sounding but factually incorrect information. Implement output verification for critical applications. Hallucination risk is elevated for smaller models due to limited memorization capacity.
• Dual-use risk: Text-generation capabilities could be misused for spam, disinformation, or impersonation. Deploy responsibly and in accordance with the Apache 2.0 license terms.

Recommendations

• Adding a lightweight output safety classifier for user-facing deployments
• Verifying factual claims with external knowledge bases
• Not using the model for high-stakes decisions without human review
• Considering larger Gemma 4 variants for tasks requiring stronger reasoning or factual recall (note: E4B is already a mid-size variant)