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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.
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