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AK04-IXR
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Model Details
Model Provider
AK04-IXR
Model Tree
sarvamai/sarvam-1
Input Modalities
Output Modalities
Supported Functionality
The base sarvam-1 is a base (non-instruct) model and cannot be reliably
prompted into TN (12-shot ICL scores 49.9% WER — worse than rules). This
adapter is fine-tuned on a synthetic, correct-by-construction code-mixed corpus.
| System | WER ↓ | CER ↓ | Exact-Match ↑ |
|---|---|---|---|
naive rules (indic-numtowords) | 43.6% | 43.7% | 0% |
| competitive rule engine | 20.9% | 17.5% | 27.5% |
| Sarvam-1 base (12-shot ICL) | 49.9% | 35.4% | 5% |
| this adapter | 7.96% | 6.37% | 62.5% |
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("sarvamai/sarvam-1")m = AutoModelForCausalLM.from_pretrained("sarvamai/sarvam-1")m = PeftModel.from_pretrained(m, "AK04-IXR/sarvam1-hinglish-tn-lora") prompt = "Input: Mera flight ticket PNR-8392 hai, aur departure 4:30 PM ko hai.\nOutput:"ids = tok(prompt, return_tensors="pt").to(m.device)out = m.generate(**ids, max_new_tokens=96, do_sample=False)print(tok.decode(out[0][ids['input_ids'].shape[1]:], skip_special_tokens=True))# -> Mera flight ticket pee-en-aar eight three nine two hai, aur departure four thirty pee-em ko hai.LoRA (r=16, α=32, all attn+MLP projections; 0.94% of params) on ~8k synthetic pairs, 3 epochs, bf16, on a single A100. See the GitHub repo for the data generator, trainer, and evaluation harness.
Trained on synthetic data, so it follows the project's normalization conventions; the held-out test set is small (40 sentences) — treat the headline number as indicative and see the per-category breakdown in the repo.