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Model: olaverse/legal-peace-v1.0
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- legal
+- contract-analysis
+- legal-reasoning
+- mistral
+- olaverse
+pipeline_tag: text-generation
+library_name: transformers
+base_model: 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](https://github.com/Olaverse-Labs/olaverse)
+
+## Quick Start
+
+### Via the `olaverse` library (recommended)
+
+```bash
+pip install olaverse[legal]
+```
+
+```python
+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
+
+```python
+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](https://github.com/Olaverse-Labs/olaverse)
+- [Olaverse Library Docs](https://Olaverse-Labs.github.io/olaverse/)
+- [Benchmarks](https://github.com/Olaverse-Labs/olaverse)
+
+---
+
+**Made with ❤️ by Olaverse AI**

config.json

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+{
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+        "MistralForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 2,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "model_type": "mistral",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "pad_token_id": 770,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "unsloth_version": "2026.2.1",
+    "use_cache": true,
+    "vocab_size": 32768
+}

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