cs-552-2026-Clanker-Scientists

coordinator-qwen3-14b-qlora-legal-v3

Training

Base: Qwen/Qwen3-14B
Method: QLoRA (rank 32, alpha 64) on chain-of-thought self-distillation
- Phase A: generate 1,000 reasoning traces with base Qwen3-14B (thinking mode, max_new_tokens=512)
- Phase B: filter to 752 traces where the parsed answer matched the gold label
- Phase C: train adapter for 3 epochs at LR 1e-5 on (prompt, reasoning + answer)
- Phase D: evaluate with enable_thinking=True (apples-to-apples vs Phase 1 baselines)

Evaluation

LegalBench, N=25 per task, seed=0:

Table with columns: Task, Best baseline, v3 FT, Δ
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.

Usage

python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from 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))

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

cs-552-2026-Clanker-Scientists

Model Tree

Base

Qwen/Qwen3-14B

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

Explore FriendliAI today

Get started Talk to an engineer

Training

Base: Qwen/Qwen3-14B
Method: QLoRA (rank 32, alpha 64) on chain-of-thought self-distillation
- Phase A: generate 1,000 reasoning traces with base Qwen3-14B (thinking mode, max_new_tokens=512)
- Phase B: filter to 752 traces where the parsed answer matched the gold label
- Phase C: train adapter for 3 epochs at LR 1e-5 on (prompt, reasoning + answer)
- Phase D: evaluate with enable_thinking=True (apples-to-apples vs Phase 1 baselines)

Evaluation

LegalBench, N=25 per task, seed=0:

Table with columns: Task, Best baseline, v3 FT, Δ
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

Usage

python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from 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))

coordinator-qwen3-14b-qlora-legal-v3

README

Training

Evaluation

Usage

Explore FriendliAI today

README

Training

Evaluation

Usage