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nyaya-7b/README.md
ModelHub XC 2b77647a38 初始化项目，由ModelHub XC社区提供模型 
Model: MrRoyaleAce/nyaya-7b
Source: Original Platform
2026-07-17 18:37:12 +08:00

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language, license, base_model, tags, datasets, pipeline_tag, library_name, model-index
language license base_model tags datasets pipeline_tag library_name model-index
en
hi
apache-2.0 mistralai/Mistral-7B-Instruct-v0.3
legal
indian-legal
information-extraction
nlp
qlora
peft
finetuned
mistral
json-extraction
legal-nlp
d0r1h/ILSum
law-ai/InLegalNLP
text-generation transformers
name results
nyaya-7b
task metrics
type name
text-generation Structured Legal Information Extraction
type value name
f1 0.425 Statute F1
type value name
accuracy 0.64 Outcome Accuracy
type value name
other 0.427 Hallucination Rate (lower is better)
type value name
other 0.86 JSON Validity Rate

🏛️ Nyaya-7B — Indian Legal Judgment Parser

Nyaya-7B is a domain-adapted, instruction-finetuned version of Mistral-7B-Instruct-v0.3, trained on 10,000+ Indian Supreme Court and High Court judgments to extract structured legal information into clean, validated JSON — at zero API cost, fully offline.

"Nyaya" (न्याय) means justice in Sanskrit and Hindi.

🎯 What it does

Given raw Indian court judgment text, Nyaya-7B extracts a full structured JSON covering:

Field Description
case_name Petitioner v. Respondent
citation AIR / SCC / SCR citation
court Full court name (Supreme Court, High Court, etc.)
year Year of judgment
petitioner / respondent Party names
subject_matter Criminal / Civil / Constitutional / Tax / ...
statutes_cited List of Acts + Sections + descriptions
precedents_cited AIR/SCC citations with case names
legal_issues Issues framed by the court
holding Court's decision and reasoning (1–3 sentences)
outcome dismissed / allowed / disposed / remanded / modified

📊 Benchmark Results

Evaluated on a 50-case held-out test set of Indian SC/HC judgments, benchmarked head-to-head against Gemini 2.5 Flash:

Metric Gemini 2.5 Flash Nyaya-7B Winner
Statute F1 0.227 0.425 🏆 Nyaya-7B (+87%)
Outcome Accuracy 0.20 0.64 🏆 Nyaya-7B (+220%)
Hallucination Rate ↓ 0.775 0.427 🏆 Nyaya-7B (−45%)
JSON Validity 0.98 0.86 Gemini Flash
Field Coverage 0.893 0.855 Gemini Flash
Cost per Judgment ~$0.0001 $0.00 🏆 Nyaya-7B

Nyaya-7B achieves 3.2× higher outcome classification accuracy and 45% lower hallucination rate than Gemini 2.5 Flash, while running entirely offline at zero cost.

🚀 Quick Start

Installation

pip install transformers torch accelerate bitsandbytes

Load & Run (GPU, 4-bit quantized)

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
import torch, json

MODEL_ID = "mrroyaleace/nyaya-7b"   # replace with your HuggingFace repo

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    quantization_config=bnb_config,
    device_map="auto",
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

Extract structured data from a judgment

judgment_text = """
IN THE SUPREME COURT OF INDIA
Criminal Appeal No. 1234 of 2022

State of Punjab                         ...Appellant
Versus
Gurpreet Singh                          ...Respondent

JUDGMENT

The appellant challenges the High Court's order acquitting the respondent 
of charges under Section 302 IPC read with Section 34 IPC...
"""

messages = [
    {"role": "system", "content": "You are Nyaya, a specialized Indian legal extraction model."},
    {"role": "user", "content": f"Extract structured data from this judgment and return JSON:\n\n{judgment_text}"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

raw_output = pipe(
    prompt,
    max_new_tokens=512,
    do_sample=False,
    return_full_text=False,
    pad_token_id=tokenizer.eos_token_id,
)[0]["generated_text"]

result = json.loads(raw_output.strip())
print(result)

Expected Output

{
  "case_name": "State of Punjab v. Gurpreet Singh",
  "citation": null,
  "court": "Supreme Court of India",
  "year": 2022,
  "petitioner": "State of Punjab",
  "respondent": "Gurpreet Singh",
  "subject_matter": "Criminal",
  "statutes_cited": [
    {"act": "Indian Penal Code", "section": "302", "description": "Punishment for murder"},
    {"act": "Indian Penal Code", "section": "34",  "description": "Acts done by several persons in furtherance of common intention"}
  ],
  "precedents_cited": [],
  "legal_issues": [
    "Whether the High Court was justified in acquitting the respondent under Section 302 IPC?"
  ],
  "holding": "The Supreme Court examined the evidence and found the High Court's reasoning sound...",
  "outcome": "dismissed"
}

CPU Inference (no GPU required)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.float32,
    device_map="cpu",
    low_cpu_mem_usage=True,
)
# Note: CPU inference is significantly slower (~5–15 min per judgment)

🔧 Training Details

Parameter Value
Base model mistralai/Mistral-7B-Instruct-v0.3
Fine-tuning method QLoRA (Quantized Low-Rank Adaptation)
Quantization 4-bit NF4 with double quantization
LoRA rank 16
LoRA alpha 32
LoRA dropout 0.05
Target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training samples ~10,000 Indian SC/HC judgment pairs
Epochs 3
Effective batch size 16 (batch 2 × grad_accum 8)
Learning rate 2e-4
LR scheduler Cosine
Optimizer paged_adamw_8bit
Max sequence length 2048 tokens
Hardware Kaggle T4 × 2 (32 GB VRAM total)
Training time ~8 hours
Compute dtype float16

Training Infrastructure

  • Fine-tuned on Kaggle T4 × 2 GPU notebooks
  • Monitored with Weights & Biases (JSON validity rate logged per epoch)
  • Adapter merged into base model weights with merge_and_unload() for zero inference overhead

📚 Training Data

The model was trained on ~2,000 Indian court judgment pairs curated and labeled from:

  • ILSum — Indian Legal Summarization dataset (Supreme Court judgments)
  • InLegalNLP — Indian Legal NLP benchmark corpus

Labels were auto-generated using Gemini 2.5 Flash as a labelling oracle on the raw judgment texts, following the canonical extraction schema, then validated for JSON structure and field completeness.

⚠️ Limitations

  • Not for legal advice: This model extracts structured information only. It does not provide legal opinions or advice. Always consult a qualified lawyer for legal matters.
  • Pre-1950 judgments: May perform poorly on archaic legal language from older judgments.
  • Hindi/regional language text: Primarily trained on English-language judgments; performance degrades on mixed-language or vernacular text.
  • Scanned/handwritten PDFs: Model accepts only clean text input — OCR preprocessing is required for scanned documents.
  • Citation hallucination: Significantly reduced (42.7% vs 77.5% baseline), but the model can still occasionally generate plausible-but-incorrect section numbers. Always validate critical citations against primary sources.
  • Novel statutes: Statutes not well-represented in training data (e.g., recent 2023–24 acts) may have lower extraction accuracy.

✅ Intended Use

  • ⚖️ Legal research and document processing automation
  • 🤖 Paralegal workflow tools and legal analytics dashboards
  • 📖 Academic research on Indian legal NLP
  • 🔍 Building legal search and knowledge graph systems
  • 📊 Bulk digitization of case records

❌ Out-of-Scope Use

  • Providing legal advice to individuals
  • Making or influencing judicial decisions
  • Use in actual legal proceedings without qualified human review
  • Any high-stakes decision-making without validation

📄 Output Schema

{
  "case_name":         str,                          # "Petitioner v. Respondent"
  "citation":          str | None,                   # "AIR 1997 SC 3986" or null
  "court":             str,                          # Full court name
  "year":              int | None,                   # 4-digit year
  "petitioner":        str,
  "respondent":        str,
  "subject_matter":    str | None,                   # Criminal | Civil | Constitutional | ...
  "statutes_cited":    [{"act": str, "section": str, "description": str}],
  "precedents_cited":  [{"citation": str, "case_name": str | None}],
  "legal_issues":      [str],
  "holding":           str,                          # 1-3 sentence summary
  "outcome":           str                           # dismissed | allowed | disposed | remanded | modified
}

🔗 Related Resources

📝 Citation

If you use Nyaya-7B in your research or applications, please cite:

@misc{nyaya7b2026,
  title   = {Nyaya-7B: A QLoRA Fine-tuned LLM for Indian Legal Judgment Parsing},
  author  = {Shubham Suman},
  year    = {2026},
  url     = {https://huggingface.co/mrroyaleace/nyaya-7b},
  note    = {Fine-tuned from mistralai/Mistral-7B-Instruct-v0.3 on 2,000+ Indian SC/HC judgments}
}

Built with ❤️ for the Indian legal research community. Nyaya-7B is open-source and free to use under the Apache 2.0 license.