Model Card — BanglaBridge-Instruct

Model summary

• Model: BanglaBridge-Instruct — a LoRA adapter for Llama 3.3 70B Instruct.
• Base model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference (Meta Llama 3.3 70B Instruct). Fine-tuned via Adaption AutoScientist; platform run adaption_llama_3_3_70b_instru_banglish_helpful_pairs_959f9e3a.
• Adaptation method: Adaption's two-stage pipeline — (1) Adaptive Data quality-lift of the raw instruction pairs, (2) AutoScientist self-learning fine-tuning loop (data ↔ recipe co-optimization) on the adapted set.
• Language(s): Bengali — code-mixed / romanized "Banglish", native script, and mixed code-switch — plus English.
• License: Llama 3.3 Community License (inherited from the base model). Dataset released separately under CC-BY-4.0.
• Developed by: Team MAHATA — Adaption AutoScientist Challenge × HackIndia
• Repo: https://github.com/HackIndiaXYZ/adaption-autoscientist-challenge-50000-prize-pool-mahata

Why this model exists

100M+ Bengali speakers type in romanized "Banglish" ("kal ki plan? ami free achi") — a register with no standard orthography that off-the-shelf models routinely garble. BanglaBridge-Instruct is instruction-tuned to understand all three real-world registers (romanized / native script / code-switch) and the spelling chaos within them (ache/ase/achhe, kivabe/kemne/kmne).

Results (the headline)

Metric	Result
Win rate vs. baseline (Adaption held-out evaluation, Language category)	65%

The adapted model's responses beat the baseline model's in 65% of head-to-head judgments on Adaption's in-house held-out test set — a measurable improvement over baseline, satisfying the challenge's eligibility gate.

Intended use

Instruction-following, Q&A, translation, rewriting, and generation in code-mixed / romanized / native Bengali — chat assistants, content tools, and support bots serving Bengali speakers who type the way people actually type.

Out of scope: high-stakes medical/legal/financial advice; safety-critical decisions.

Training data

Original, hand-authored Banglish instruction dataset (0% scraped), processed through Adaption's Adaptive Data pipeline and released openly alongside the model. Sourcing, register distribution, augmentation design, and licensing are documented in DATASET_CARD.md and data/.

Key dataset design choices:

• 3-register hedge: romanized + native-script + mixed code-switch pairs.
• Spelling-variation augmentation (train split only): deterministic, meaning-preserving orthographic variants so the model learns the meaning, not the surface form.
• 21 task types including safety/refusal behavior in-register.

Training procedure

• Platform: Adaption AutoScientist (managed fine-tuning of Llama 3.3 70B Instruct)
• Method: LoRA (PEFT), bf16. peft_type=LORA, task_type=CAUSAL_LM.
• Hyperparameters (from adapter_config.json / trainer_state.json):
  • rank r = 64, lora_alpha = 128, lora_dropout = 0.05, bias = none
  • target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
  • 4 epochs, 516 steps, per-device batch size 1
  • train loss 1.52 → 0.38 (smooth convergence, no divergence)
• Data pipeline: data/adaptive_pipeline.py (upload → estimate → adapt → export; resumable, spend-gated)
• Artifacts: LoRA adapter (adapter_model.safetensors, ~3.3 GB), tokenizer, chat template, and trainer_state.json released in training/.

How to use (load the adapter on the base model)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "meta-llama/Llama-3.3-70B-Instruct"   # or the Together reference build
tok  = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "MAHATA/BanglaBridge-Instruct")  # this adapter

Limitations & risks

• Register-specialized: tuned toward conversational Bengali registers; may not beat generic models on formal literary Bengali.
• Inherits Llama 3.3 base-model biases; informal registers may carry social-media-style biases.
• Evaluated via win-rate preference judgments — not a factual-accuracy benchmark; verify factual outputs independently.

Citation

@misc{banglabridge2026,
  title  = {BanglaBridge-Instruct: a domain-adapted LLM for code-mixed Bengali},
  author = {Team MAHATA},
  year   = {2026},
  note   = {Adaption AutoScientist Challenge x HackIndia},
  url    = {https://github.com/HackIndiaXYZ/adaption-autoscientist-challenge-50000-prize-pool-mahata}
}