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Glint-1

⚠️ IMPORTANT NOTICE

1. This model is experimental. Glint-1 is a 1M parameter research model designed for architectural experimentation.
2. Performance characteristics: The model exhibits behavioral patterns comparable to ~2M parameter models despite its compact size.
3. Not production-ready: This release demonstrates functional capability, not optimal performance.

Overview

Glint-1 is an ultra-compact language model developed by CompactAI following our rebrand initiative. This 1M parameter model demonstrates that efficient architectural design can yield behavioral characteristics typically associated with larger models (~2M parameters).

This release includes both Pretrained Weights (base language modeling) and Instruction-Tuned Weights (fine-tuned for conversational tasks).

Model Specifications

Parameter	Value
Architecture	Transformer Decoder
Parameters	~1M
Effective Behavior	~2M parameter equivalent
Context Length	2,048 tokens
Vocabulary	Standard
Normalization	RMSNorm
Activation	SwiGLU

Benchmarks

Glint-1 has been evaluated on standard language modeling and reasoning benchmarks:

BLiMP Benchmark

Grammaticality minimal pairs across 67 paradigms. Accuracy measured as % grammatical < ungrammatical perplexity.

ARC-Easy Benchmark

Multiple-choice science QA (~2.4K questions) using perplexity-based answer selection.

WikiText-2 Benchmark

Language modeling perplexity on Wikipedia test split. Lower is better.

Training Details

Parameter	Value
Batch Size	48
Learning Rate	8e-4 (pretrain), 2e-4 (SFT)
Warmup	300 steps
Weight Decay	0.02
Max Grad Norm	1.0

Limitations

• Repetition: May exhibit repetitive generation patterns
• Knowledge: Limited world knowledge due to parameter constraints
• Reliability: Not suitable for production applications or critical tasks
• Purpose: Intended for research, educational purposes, and architectural benchmarking

Usage

This model is released for research purposes. While functional, users should not expect state-of-the-art performance. The model demonstrates that compact architectures can achieve reasonable behavioral characteristics, making it suitable for:

• Architectural research
• Edge deployment experiments
• Educational purposes
• Baseline comparisons

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Generated by CompactAI for research purposes. Use responsibly.