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DLCS
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Model Details
Model Provider
DLCS
Model Tree
microsoft/Phi-3-mini-4k-instruct
Input Modalities
Output Modalities
Supported Functionality
Evaluated on a held-out test set of 2,784 clauses, compared against the zero-shot base model:
| Metric | Zero-shot base model | This adapter |
|---|---|---|
| Accuracy | 21.6% | 62.5% |
| Invalid (unparseable) output rate | 58.5% | 0.5% |
Full per-category metrics, error analysis, and known limitations are in the eval notebook.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelbase_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct",quantization_config=BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.float16,),device_map="auto",)tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")model = PeftModel.from_pretrained(base_model, "DLCS/contract-clause-phi3-lora")model.eval()clause = "This Agreement shall be governed by and construed under the laws of the State of Delaware."prompt_messages = [{"role": "user","content": f'Classify the following contract clause into its category, or respond with "None" if it does not match any category. Respond with only the category name and nothing else.\n\nClause:\n{clause}',}]prompt = tokenizer.apply_chat_template(prompt_messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.inference_mode():output = model.generate(**inputs, max_new_tokens=18)print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))# "Governing Law"
qkv_proj, o_proj, gate_up_proj, down_proj — ~0.57% of total parameters trainablebitsandbytes/GradScaler bug forced this)Single-label classification only; several of the 41 categories have very few test examples, so per-category metrics for those are directional, not statistically reliable; the "None" class is heuristically constructed and carries some inherent label noise. See the eval notebook for a full, honest discussion.