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cs-552-2026-Clanker-Scientists
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Model Details
Model Provider
cs-552-2026-Clanker-Scientists
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-14Benable_thinking=True (apples-to-apples vs Phase 1 baselines)LegalBench, N=25 per task, seed=0:
| Task | Best baseline | v3 FT | Δ |
|---|---|---|---|
contract_nli_explicit_identification | 0.840 (Qwen2.5-7B think) | 0.720 | -0.120 |
consumer_contracts_qa | 0.960 (Qwen3-14B think) | 0.960 | 0.000 (matches) |
proa | 1.000 (Qwen3-14B no-think) | 0.960 | -0.040 |
CoT distillation provides selective benefit by task type: it lifts free-form
QA tasks (consumer_contracts_qa) to baseline parity, slightly regresses
structurally-fixed entailment tasks (contract_nli), and preserves near-ceiling
performance on proa.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel BASE = "Qwen/Qwen3-14B"ADAPTER = "cs-552-2026-Clanker-Scientists/coordinator-qwen3-14b-qlora-legal-v3" tok = AutoTokenizer.from_pretrained(BASE)bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)base = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="auto")model = PeftModel.from_pretrained(base, ADAPTER)model.eval() msgs = [ {"role": "system", "content": "You are a legal contract analyst..."}, {"role": "user", "content": "Contract:\n[excerpt]\n\nQuestion: Does the contract explicitly identify confidential information?"},]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=True)inputs = tok(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(tok.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel BASE = "Qwen/Qwen3-14B"ADAPTER = "cs-552-2026-Clanker-Scientists/coordinator-qwen3-14b-qlora-legal-v3" tok = AutoTokenizer.from_pretrained(BASE)bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)base = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="auto")model = PeftModel.from_pretrained(base, ADAPTER)model.eval() msgs = [ {"role": "system", "content": "You are a legal contract analyst..."}, {"role": "user", "content": "Contract:\n[excerpt]\n\nQuestion: Does the contract explicitly identify confidential information?"},]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=True)inputs = tok(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(tok.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))