---
language:
- en
license: llama4
library_name: peft
pipeline_tag: text-generation
base_model: meta-llama/Llama-4-Scout-17B-16E-Instruct
datasets:
- itsalloverig/adaption-indian-legal-triage-samples-v4
tags:
- legal
- india
- indian-law
- adaption
- autoscientist
- instruction-tuning
- lora
- legal-research
model_name: MIKE
---

# 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_prompt` to `fused_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](https://huggingface.co/datasets/itsalloverig/adaption-indian-legal-triage-samples-v4).

## Acknowledgements

Built with Adaption Adaptive Data and AutoScientist for the Legal track of the
Adaption AutoScientist Challenge x HackIndia.