Model Card for Gemma-3-1B-IT (Aria Quant Bundle, q326_channel)

Model Details

Model Description

• Developed by: Google
• Quantized and distributed by: Aria Compute
• Model type: Dense Transformer decoder-only (language, instruction-tuned)
• Language(s): English (primary), Chinese, and 30+ additional languages
• License: Apache 2.0
• Finetuned from model: google/gemma-3-1b-it

Model Sources

• Original Repository: google/gemma-3
• Original Paper: Gemma 3 Technical Report
• 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 multi-line code generation
• Instruction-following tasks for mobile and IoT applications
• Lightweight text embeddings for on-device retrieval and classification
• Short-to-medium form summarization of notifications, messages, and local content
• Local document analysis up to 32K context (chunked)

Target Devices

Note: KV cache is compact due to aggressive GQA (26 layers × 3 KV heads × head_dim=128), ~40% of an equivalent full-attention model's cache.

Out-of-Scope Use

• Long-form creative writing (>4K tokens per generation)
• Mathematical theorem proving or formal verification
• Full program/application synthesis
• Multimodal input (this model is text-only)
• Real-time audio/speech processing (use Aria speech models)
• Safety-critical decision systems without human oversight

How to Get Started with the Model

Download from Aria Compute

Quantization Recipe

• Bundle size: ~0.6 GB (BF16 original: ~2.2 GB)
• Generation quality: Awaiting gen_quant_eval audit. Per-channel quantization preserves 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-3-1B-IT: gemma-3-1b-it_q8_channel (per-channel 8-bit) and gemma-3-1b-it_q4_channel (per-channel 4-bit)

Model Architecture

• GQA (Grouped Query Attention): 3 KV heads serving 9 query heads (group 3) — KV Cache memory is 3× smaller than full attention
• GeGLU activation: GELU with tanh approximation gating, efficient for on-device inference
• RoPE position encoding: Standard 10K base frequency supporting 32K context length
• RMSNorm pre-normalization: Lightweight normalization before each sub-layer

• Half hidden width (1,152 vs 2,304) and half FFN width (4,608 vs 9,216) — the primary source of the ~1B parameter reduction from the 2B class
• 4× context length (32K vs 8K) — improved long-context capability despite smaller model size
• Smaller head_dim (128 vs 288) — lighter per-head computation with more query heads (9 vs 8)
• More aggressive GQA (group 3 vs group 2) — further KV cache compression to compensate for longer context
• Instruction-tuned variant — this bundle is based on the IT (instruction-tuned) checkpoint, not the base pre-trained model, providing better instruction-following capability out of the box

Bias, Risks, and Limitations

Limitations

• Reasoning depth: Multi-step logical reasoning (≥3 steps) is limited for a 1B-class model. Verify outputs in high-stakes scenarios.
• Mathematics: Simple arithmetic and GSM8K-level problems are partially supported; advanced quantitative reasoning is unreliable. Use larger models for mathematical precision.
• Code generation: Capable of single-function completions and basic snippets; unreliable for multi-file program synthesis or complex algorithms.
• Factual knowledge: Limited world knowledge due to ~1B parameter scale. Always verify factual claims against authoritative sources.
• Instruction following: Handles simple single-constraint instructions reliably. Complex multi-constraint prompts may cause degradation at longer contexts.
• Quantization drift: Per-channel quantization may exhibit generation drift versus FP16, especially on ambiguous or open-ended prompts. For minimal drift, use gemma-3-1b-it_q8_channel (per-channel 8-bit).

Bias and Risks

• Bias: As with all large language models trained on web-scale data, Gemma 3 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.
• 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