| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - small-language-model |
| - slm |
| - pytorch |
| - edge-ai |
| - logistics |
| - customer-support |
| - delivery-assistant |
| pipeline_tag: text-generation |
| widget: |
| - text: "<|user|> Customer is asking to change delivery address after order pickup. <|assistant|>" |
| --- |
| |
| # Delivery-Rider-Assistant-SLM (30.0M Parameters) |
|
|
| ## Overview |
| Delivery-Rider-Assistant-SLM is a lightweight Small Language Model (SLM) trained from scratch, intended for offline use on low-power mobile devices by delivery partners and drivers (e.g., Swiggy, Zomato, Porter). |
|
|
| ## Intended Use Case |
| - Offline edge execution in areas with poor connectivity. |
| - Quick responses for address changes, payment status checks, COD handling, and order issues. |
|
|
| ## Model Details & Architecture |
| - Architecture: Decoder-only causal transformer (nanoGPT-style, flash attention) |
| - Parameters: ~30.0 million |
| - Layers: 6 |
| - Attention heads: 8 |
| - Embedding dimension: 384 |
| - Context length: 256 tokens |
| - Tokenizer: BPE (GPT-2 encoding via tiktoken) |
|
|
| ## Training Setup |
| - Hardware: Single NVIDIA Tesla T4 GPU (Google Colab Free Tier) |
| - Training time: ~23 minutes |
| - Precision: Mixed precision (float16 AMP) |
| - Optimizer: AdamW (lr=6e-4, weight_decay=0.1, cosine schedule with warmup) |
| - Best validation loss: 0.7974 |
| - Data: Bitext customer-support instruction dataset |
| |
| ## How to Load in PyTorch |
| ```python |
| import torch |
| from huggingface_hub import hf_hub_download |
|
|
| weights_path = hf_hub_download(repo_id="learner-subodh/Delivery-Rider-Assistant-SLM", filename="pytorch_model.bin") |
| # Re-create the GPT class and GPTConfig from the training notebook, then: |
| # model.load_state_dict(torch.load(weights_path, map_location="cpu")) |
| ``` |
| |
