2.8 KiB
2.8 KiB
license, language, tags, pipeline_tag, library_name, base_model
| license | language | tags | pipeline_tag | library_name | base_model | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers | unsloth/mistral-7b-v0.3-bnb-4bit |
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
Made with ❤️ by Olaverse AI