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Model: olaverse/legal-peace-v1.0 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- legal
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- contract-analysis
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- legal-reasoning
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- mistral
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- olaverse
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pipeline_tag: text-generation
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library_name: transformers
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base_model: unsloth/mistral-7b-v0.3-bnb-4bit
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---
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# Legal-Peace-7B-v1.0 🏛️⚖️
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> ⚠️ **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.
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**Legal-Peace-7B-v1.0** is a fine-tuned **Mistral-7B-v0.3** model designed for contract analysis and legal reasoning tasks.
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Part of the [Olaverse Legal](https://github.com/Olaverse-Labs/olaverse)
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## Quick Start
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### Via the `olaverse` library (recommended)
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```bash
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pip install olaverse[legal]
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```
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```python
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from olaverse.llm import LegalPeace
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model = LegalPeace() # defaults to "olaverse/legal-peace-v1.0"
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model.load() # requires GPU + unsloth
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prompt = "Analyze this clause: 'All disputes shall be resolved through binding arbitration in Delaware.' What are the key implications?"
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response = model.generate(prompt, max_new_tokens=300, temperature=0.7)
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print(response)
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```
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### Via Hugging Face directly
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="olaverse/legal-peace-v1.0",
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max_seq_length=2048,
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dtype=None,
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load_in_4bit=True,
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)
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FastLanguageModel.for_inference(model)
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prompt = "Analyze this clause: 'All disputes shall be resolved through binding arbitration in Delaware.'"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Performance vs Base Mistral-7B
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| Benchmark | Improvement |
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|---|---|
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| Inference Speed | ⚡ 10.3% faster (9.27s vs 10.33s) |
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| Contract Analysis | 📋 32.6% faster |
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| Case Predictions | ⚖️ 14.0% faster |
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| Response Quality | 📝 More structured, professional legal responses |
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## Model Details
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| Property | Value |
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|---|---|
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| Base Model | Mistral-7B-v0.3 |
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| Parameters | 7B |
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| Quantization | 4-bit (unsloth BnB) |
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| Training | SFT (4,800 cases) + DPO (419 legal reasoning examples) |
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| License | Apache 2.0 |
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## Use Cases
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✅ Contract clause analysis and review
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✅ Legal research assistance
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✅ Evidence evaluation
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✅ Case outcome prediction
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✅ Legal Q&A
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## Limitations
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⚠️ **Beta model — not for production use**
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⚠️ Not a replacement for legal professionals
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⚠️ Always verify outputs with qualified attorneys
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⚠️ Trained primarily on U.S. legal data
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## Links
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- [GitHub](https://github.com/Olaverse-Labs/olaverse)
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- [Olaverse Library Docs](https://Olaverse-Labs.github.io/olaverse/)
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- [Benchmarks](https://github.com/Olaverse-Labs/olaverse)
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---
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**Made with ❤️ by Olaverse AI**
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