MIKE
MIKE is an India-focused legal research and triage adapter trained through
Adaption AutoScientist on top of
meta-llama/Llama-4-Scout-17B-16E-Instruct.
The model helps legal teams classify issues, identify missing facts and documents, plan research, handle source-grounded questions, and flag uncertainty. It is not a substitute for advice from a qualified Indian legal professional.
Release status
This repository contains the selected MIKE v4 controlled-fused LoRA adapter, which achieved a 92.16% Adaption held-out pairwise score. The adapter, tokenizer, and configuration files are included.
Access to the gated Meta Llama 4 Scout base model is required to load the adapter. The adapter configuration references the official base repository.
Selected training run
- Adaption dataset ID:
9b5b4f7d-f099-450e-a6b6-7c185864eacd - Source corpus: 12,673 rows
- Platform train/validation/test rows: 7,172 / 228 / 236
- Fine-tune job:
6fd6244e-46b6-46dc-93ec-97de856291ce - AutoScientist experiment:
8d2362dd-51d6-4445-b88f-62664090e3fe - Provider job:
ft-31293977-6cb2 - Base model:
meta-llama/Llama-4-Scout-17B-16E-Instruct - Training method: instruction SFT with LoRA
- Prompt/completion contract:
rephrased_prompttofused_generation - Epochs: 4
- LoRA rank/alpha/dropout: 64 / 128 / 0
- Learning rate: 1e-4
- Scheduler: cosine
Evaluation
The selected run received an Adaption in-house pairwise score of 92.16% against the Llama 4 Scout base model:
- fine-tuned-model wins: 210
- base-model wins: 11
- ties: 15
- prompts: 236
- judge:
google/gemini-3.1-pro-preview - judge failures: 0
- generation failures: 0
The score gives half credit to ties:
(210 + 0.5 * 15) / 236 = 0.9216
This is a pairwise preference score, not a claim that the model is 92.16% factually accurate. A separate 100-case Indian-law domain evaluation scored 65.66% (56 fine-tuned wins, 25 base wins, 18 ties, one judge failure).
Intended uses
- preliminary Indian legal issue triage;
- research planning and document collection;
- source-bounded statutory or judgment-excerpt analysis;
- uncertainty and unsupported-citation detection;
- legacy/current criminal-law transition screening involving IPC/CrPC/IEA and BNS/BNSS/BSA.
Out-of-scope uses
- final legal advice or representation;
- predicting guaranteed outcomes;
- filing without professional review;
- generating allegations or authorities unsupported by supplied facts and sources;
- use outside India without separate adaptation and evaluation.
Limitations
- Legal rules and procedural requirements change over time.
- A plausible answer can still be incorrect or incomplete.
- The 12,673-row controlled corpus preserves all 10,913 high-signal v2 rows, adds 860 quality-gated recovery examples, and adds 900 explicit no-unprovided-authority variants.
- Server-side previews found that some fused targets are overly long and may introduce assumptions or unnecessary authority references.
- Independent qualified Indian legal review and current official-source grounding remain required.
License
The adapter is distributed under the Llama 4 Community License because it is
a derivative of Llama 4. Users must also comply with Meta's Acceptable Use
Policy and the base model's access requirements. See NOTICE and
https://www.llama.com/llama4/license/.
The associated dataset is licensed separately in its public dataset repository.
Acknowledgements
Built with Adaption Adaptive Data and AutoScientist for the Legal track of the Adaption AutoScientist Challenge x HackIndia.
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Model tree for itsalloverig/MIKE
Base model
meta-llama/Llama-4-Scout-17B-16E
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct") model = PeftModel.from_pretrained(base_model, "itsalloverig/MIKE")