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yasserrmd / caselaw-cpt-8b
Model's Last Updated: July 18 2025
text-generation
Introduction of caselaw-cpt-8b
Model Details of caselaw-cpt-8b
🏛️ Caselaw-CPT-7B
This model is a
Continued Pretrained Language Model (CPT)
based on a 7B parameter LLaMA architecture, further trained on the
common-pile/caselaw_access_project
dataset — a large corpus of U.S. court decisions.
The goal of this model is to adapt a general LLM to legal language , style, and reasoning patterns, enabling more accurate and fluent completions, clause generation, and preliminary legal Q&A.
🧠 Model Summary
| Feature | Value |
|---|---|
| Base model | LLaMA-3 7B / Unsloth (4bit) |
| Type | Continued Pretraining (CPT) |
| Domain | Legal (Caselaw, U.S.) |
| Dataset | Caselaw Access Project |
| Context length | 2048 |
| Quantization | 4-bit via Unsloth |
📜 Intended Use
This model is ideal for:
- 📄 Legal clause completions
- 🤖 Legal-style generation (opinions, summaries)
- ❓ Basic legal Q&A (zero-shot, surprisingly strong)
- 📚 Precursor to legal instruction tuning
🚀 Example Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")
prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=100,
temperature=0.7,
top_p=0.9,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🧪 Example Output
“The three conditions are that the defendant had exclusive control over the instrumentality that caused the plaintiff's injury, the plaintiff's injury was the type of injury that would not occur in the absence of negligence, and the plaintiff was not guilty of contributory negligence.”
📊 Evaluation
While not instruction-tuned, the model demonstrates:
- ✅ Strong fluency in legal domain terminology
- ✅ Structured legal logic in completions
- ✅ Promising zero-shot performance on legal questions
For downstream evaluation, consider testing on:
- LegalBench QA
- SODA Law
- Instruct-tuned legal tasks (summarization, classification)
📦 Files Included
-
config.json -
pytorch_model.binoradapter_model.bin(if LoRA) -
tokenizer_config.json -
tokenizer.model/tokenizer.json -
(Optional)
generation_config.json
📄 License & Use
The base model follows its original license (Meta AI or Unsloth), and the Caselaw Access Project is in the public domain. This checkpoint is provided for research and educational use only . Not intended for actual legal advice or decisions.
🙏 Acknowledgments
- 📚 Harvard Caselaw Access Project
- 🛠️ Unsloth.ai for efficient fine-tuning
- ☁️ Google Colab Pro for training infrastructure
- 🧠 Hugging Face for hosting & tooling
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Runs of yasserrmd caselaw-cpt-8b on huggingface.co
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More Information About caselaw-cpt-8b huggingface.co Model
Url of caselaw-cpt-8b
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Updated:July 30 2025
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