| --- |
| language: ["en", "hi", "bn", "mr", "ta"] |
| license: other |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: [small-language-model, slm, indic, multilingual, edge-ai, logistics, customer-support, llama] |
| --- |
| |
| # Indic-Delivery-Rider-SLM (40M parameters) |
|
|
| A small, from-scratch, Llama-style language model for **offline** assistance of delivery partners in India. |
| Languages: English, Hindi, Bengali, Marathi, Tamil. |
|
|
| > **Experimental research model.** Trained for ~28 minutes on one T4 GPU on a small dataset. Non-English |
| > domain data is **machine-translated** and has not been reviewed by native speakers. Not for payments, safety or legal advice. |
|
|
| ## Architecture |
| Decoder-only transformer, 10 layers, hidden 512, 8 attention heads / 4 KV heads (GQA), SwiGLU MLP (1408), |
| RMSNorm, RoPE, tied embeddings, context 384. Tokenizer: SentencePiece BPE, 20000 tokens, byte fallback. |
| Exports as `LlamaForCausalLM` (logits verified against the original implementation). |
|
|
| Tokenizer fertility on held-out text: |
| - English: 1.28 tokens/word (GPT-2 BPE: 1.30) |
| - Hindi: 1.26 tokens/word (GPT-2 BPE: 7.76) |
| - Bengali: 1.45 tokens/word (GPT-2 BPE: 12.79) |
| - Marathi: 1.57 tokens/word (GPT-2 BPE: 10.50) |
| - Tamil: 1.81 tokens/word (GPT-2 BPE: 23.44) |
|
|
| ## Prompt format |
| `<s><|user|>{question}<|assistant|><|lang|>` then the model writes the answer and `</s>`. |
| `<|lang|>` is one of `<|en|>`, `<|hi|>`, `<|bn|>`, `<|mr|>`, `<|ta|>`; prefill it to force the reply language, or omit it to let the model choose. |
|
|
| ## Usage |
| ```python |
| import torch, sentencepiece as spm |
| from huggingface_hub import snapshot_download |
| from transformers import AutoModelForCausalLM |
| |
| path = snapshot_download("learner-subodh/Indic-Delivery-Rider-SLM") |
| sp = spm.SentencePieceProcessor(model_file=f"{path}/tokenizer.model") |
| model = AutoModelForCausalLM.from_pretrained(path).eval() |
| |
| q = "Customer wants to change the delivery address after pickup. What should I tell them?" |
| ids = [sp.bos_id(), sp.piece_to_id("<|user|>")] + sp.encode(q) + [sp.piece_to_id("<|assistant|>"), sp.piece_to_id("<|en|>")] |
| out = model.generate(torch.tensor([ids]), max_new_tokens=160, do_sample=True, temperature=0.6, top_k=40, top_p=0.9, |
| repetition_penalty=1.15, eos_token_id=sp.eos_id(), pad_token_id=sp.pad_id()) |
| print(sp.decode(out[0][len(ids):].tolist())) |
| ``` |
|
|
| ## Training data |
| - Bitext customer-support dataset (English, `bitext/Bitext-customer-support-llm-chatbot-training-dataset`) |
| - Sentence-level machine translation of a stratified subset into the other languages with NLLB-200 (600M distilled), filtered for placeholders, script, repetition and length |
| - Native-script instruction data from the Aya dataset (`CohereForAI/aya_dataset`, Apache-2.0) |
|
|
| ## Evaluation (held-out) |
| Bits per byte of the answer text (lower is better; comparable across scripts). `domain` = support-style data, `general` = Aya. |
|
|
| | lang | data | bits_per_byte | ppl | resp_tokens | |
| |---|---|---|---|---| |
| | bn | domain | 0.204 | 6.614 | 10821 | |
| | bn | general | 0.825 | 193.146 | 5206 | |
| | en | domain | 0.258 | 2.297 | 52493 | |
| | hi | domain | 0.199 | 4.506 | 12750 | |
| | hi | general | 0.694 | 51.689 | 3170 | |
| | mr | domain | 0.217 | 7.363 | 11822 | |
| | mr | general | 0.668 | 36.486 | 4682 | |
| | ta | domain | 0.177 | 7.704 | 12050 | |
| | ta | general | 0.731 | 119.382 | 9661 | |
| |
| Generation checks on hand-written rider prompts (3 per language; tiny sample, indicative only): `lang_fidelity` = reply is in the expected |
| script (and Hindi/Marathi marker words), `auto_lang_id` = model picks the right language tag unprompted, `distinct2` = 1 - repetition. |
|
|
| | lang | lang_fidelity | auto_lang_id | distinct2 | stop_rate | avg_words | |
| |---|---|---|---|---|---| |
| | bn | 1.0 | 1.0 | 0.95 | 0.67 | 80.0 | |
| | en | 1.0 | 1.0 | 0.98 | 0.67 | 75.0 | |
| | hi | 1.0 | 1.0 | 0.94 | 1.0 | 91.67 | |
| | mr | 0.67 | 1.0 | 0.65 | 0.67 | 57.67 | |
| | ta | 1.0 | 1.0 | 0.99 | 0.67 | 89.67 | |
| |
| ## Limitations |
| Small capacity, small data: it can state wrong policies fluently, blur Hindi and Marathi (same script), and repeat itself. |
| Machine-translated training text carries translation errors. Route anything involving money, injuries or disputes to a human or a fixed template. |
| |
| ## Licensing note |
| Mixed provenance: Bitext (CDLA-Sharing-1.0), Aya (Apache-2.0), and translations produced by NLLB-200 (model weights CC-BY-NC-4.0). |
| Check these terms with your own counsel before any commercial use. Weights are released for research and evaluation. |
| |
