olaverse/legal-peace-v1.0

Legal-Peace-7B-v1.0 🏛️⚖️

⚠️ Beta Model: This model is experimental and intended for research purposes. Outputs must always be reviewed by a qualified legal professional. Not recommended for production use.

Legal-Peace-7B-v1.0 is a fine-tuned Mistral-7B-v0.3 model designed for contract analysis and legal reasoning tasks.

Part of the Olaverse Legal

Quick Start

Via the olaverse library (recommended)

pip install olaverse[legal]

from olaverse.llm import LegalPeace

model = LegalPeace()  # defaults to "olaverse/legal-peace-v1.0"
model.load()          # requires GPU + unsloth

prompt = "Analyze this clause: 'All disputes shall be resolved through binding arbitration in Delaware.' What are the key implications?"
response = model.generate(prompt, max_new_tokens=300, temperature=0.7)
print(response)

Via Hugging Face directly

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="olaverse/legal-peace-v1.0",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

FastLanguageModel.for_inference(model)

prompt = "Analyze this clause: 'All disputes shall be resolved through binding arbitration in Delaware.'"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Performance vs Base Mistral-7B

Benchmark	Improvement
Inference Speed	⚡ 10.3% faster (9.27s vs 10.33s)
Contract Analysis	📋 32.6% faster
Case Predictions	⚖️ 14.0% faster
Response Quality	📝 More structured, professional legal responses

Model Details

Property	Value
Base Model	Mistral-7B-v0.3
Parameters	7B
Quantization	4-bit (unsloth BnB)
Training	SFT (4,800 cases) + DPO (419 legal reasoning examples)
License	Apache 2.0

Use Cases

✅ Contract clause analysis and review ✅ Legal research assistance ✅ Evidence evaluation ✅ Case outcome prediction ✅ Legal Q&A

Limitations

⚠️ Beta model — not for production use ⚠️ Not a replacement for legal professionals ⚠️ Always verify outputs with qualified attorneys ⚠️ Trained primarily on U.S. legal data

Links

• GitHub
• Olaverse Library Docs
• Benchmarks

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