Atlas-1B: Lightweight Fine-tuned LLM for Edge and Low-Memory Devices
🚀 Atlas-1B is a 1.2-billion parameter model fine-tuned from BaseLLM-1B to deliver improved accuracy, reasoning, and efficiency on low-power inference devices (e.g., Jetson, Ryzen APU, and mobile-based LLM frameworks).
This version introduces quantization-aware finetuning, dataset specialization, and token efficiency optimization, making it a solid drop-in model for on-device AI use cases.
🧠 Model Overview
- Base model: BaseLLM-1B v1.3 (transformer-based autoregressive)
- Architecture: Decoder-only transformer
- Parameters: 1.2B
- Precision support: FP16 / INT8 / INT4
- Context length: 16K tokens
- Tokenizer: SentencePiece (32K vocab)
- Frameworks supported: PyTorch, vLLM, and sglang
This model was optimized specifically for edge inference and multi-request throughput, providing ~30% lower memory bandwidth usage at batch=4 compared to the base model.
🧩 Use Cases
- On-device chat assistants
- Smart IoT response systems
- Embedded analytics (offline summarization, intent detection, etc.)
- Lightweight reasoning for robotics
🔧 Fine-tuning Details
| Attribute | Description |
|---|---|
| Dataset | Blend of 50M tokens curated for code, chat, and reasoning |
| Training framework | PyTorch + DeepSpeed ZeRO-2 |
| Optimizer | AdamW |
| Learning rate | 2e-5 (cosine decay) |
| Batch size | 512 tokens per GPU |
| Epochs | 3 |
| Loss function | Cross-entropy (token-level) |
| Special techniques | LoRA adapters (rank=8), QLoRA-aware finetuning, FlashAttention-2 integration |
🧪 Performance Benchmarks
| Metric | BaseLLM-1B | Atlas-1B |
|---|---|---|
| MMLU (Subset) | 30.2 | 38.7 |
| CodeEval (Python) | 22.4 | 29.1 |
| Average latency (Jetson Orin, INT4) | 213ms | 158ms |
| Memory usage (FP16) | 7.9GB | 5.4GB |
Benchmarks measured with vLLM 0.4.2 and sglang backend on an RTX 3060 (12GB) and Jetson Orin AGX.