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90d6e8c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import json, os, time, re
import torch
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
from peft import PeftModel
from datasets import load_dataset
HUB_ORG = 'narcolepticchicken'
EVAL_DS = f'{HUB_ORG}/speculative-actions-eval'
MAX_EVAL = int(os.environ.get('MAX_EVAL', '200'))
ACTIONS = ['tool_call', 'retrieval', 'file_read', 'file_write', 'repair', 'verifier', 'ask_clarification', 'final_answer', 'BLOCKED']
COST = {'strong': 1.00, 'cheap': 0.15, 'verifier': 0.30, 'verify_check': 0.10}
SYSTEM_PROMPT = """You are an agent action predictor. Predict the next action from: tool_call, retrieval, file_read, file_write, repair, verifier, ask_clarification, final_answer, BLOCKED.
Format your response as:
Action: <action_name>
<brief reason>"""
def load_lm(model_id, device):
print(f" Loading LM: {model_id}")
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map='auto', trust_remote_code=True)
model.eval()
return model, tok
def load_reward_model(adapter_id, device):
tok = AutoTokenizer.from_pretrained('Qwen/Qwen3-4B', trust_remote_code=True)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
model = AutoModelForSequenceClassification.from_pretrained('Qwen/Qwen3-4B', num_labels=1, torch_dtype=torch.bfloat16, device_map='auto', trust_remote_code=True)
model.config.pad_token_id = tok.pad_token_id
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()
return model, tok
def parse_action(text):
m = re.search(r'Action:\s*(tool_call|retrieval|file_read|file_write|repair|verifier|ask_clarification|final_answer|BLOCKED)', text, re.IGNORECASE)
if m:
return m.group(1).lower()
for a in ACTIONS:
if a in text.lower():
return a
return 'tool_call'
def build_proposer_messages(example):
msgs = example['messages']
context = '\n'.join(f"{m['role']}: {str(m['content'])[:300]}" for m in msgs[-4:])
return [{'role': 'system', 'content': SYSTEM_PROMPT}, {'role': 'user', 'content': f"Predict the next action for:\n\n{context}"}]
@torch.no_grad()
def predict_action(model, tokenizer, messages, device='cuda'):
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=2048).to(device)
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False, pad_token_id=tokenizer.pad_token_id)
response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip()
return parse_action(response)
@torch.no_grad()
def get_reward_score(model, tokenizer, text, device='cuda'):
inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=1024).to(device)
return model(**inputs).logits.squeeze().item()
@torch.no_grad()
def acc_rej(model, tokenizer, proposed_action, example_msgs, device='cuda'):
context = '\n'.join(f"{m['role']}: {str(m['content'])[:200]}" for m in example_msgs[-3:])
msgs = [
{'role': 'system', 'content': 'You are a verifier. Say ACCEPT if the proposed action is correct, REJECT if wrong. Only output ACCEPT or REJECT.'},
{'role': 'user', 'content': f'Proposed action: {proposed_action}\n\nContext:\n{context}\n\nDecision:'}
]
text = tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=1024).to(device)
outputs = model.generate(**inputs, max_new_tokens=5, do_sample=False, pad_token_id=tokenizer.pad_token_id)
resp = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip().lower()
return 'accept' in resp and 'reject' not in resp
def build_reward_text(proposed_action, example):
msgs = example['messages']
context = '\n'.join(f"{m['role']}: {str(m['content'])[:200]}" for m in msgs[-3:])
return f"User: {context}\n\nAssistant: Action: {proposed_action}"
def main():
device = 'cuda'
print(f'Device: {device}')
ds = load_dataset(EVAL_DS, split='train')
data = [ds[i] for i in range(min(MAX_EVAL, len(ds)))]
print(f'Evaluating {len(data)} examples')
print('Loading cheap model...')
cm, ctok = load_lm(f'{HUB_ORG}/speculative-proposer-qwen3-1.7b', device)
print('Loading reward verifier...')
vm, vtok = load_reward_model(f'{HUB_ORG}/speculative-verifier-qwen3-4b', device)
print('Loading strong model...')
sm, stok = load_lm('Qwen/Qwen3-8B', device)
all_metrics = {}
# A
print('\nConfig A: strong only')
res = []
for i, ex in enumerate(data):
if i % 20 == 0: print(f' {i}/{len(data)}')
pred = predict_action(sm, stok, build_proposer_messages(ex), device)
res.append({'pred': pred, 'true': ex['action_type'], 'cost': COST['strong']})
acc = sum(1 for r in res if r['pred'] == r['true']) / len(res)
all_metrics['A'] = {'config': 'A', 'accuracy': round(acc, 4), 'avg_cost': COST['strong'], 'n': len(res)}
print(f' Acc: {acc:.3f}')
# B
print('\nConfig B: cheap only')
res = []
for i, ex in enumerate(data):
if i % 20 == 0: print(f' {i}/{len(data)}')
pred = predict_action(cm, ctok, build_proposer_messages(ex), device)
res.append({'pred': pred, 'true': ex['action_type'], 'cost': COST['cheap']})
acc = sum(1 for r in res if r['pred'] == r['true']) / len(res)
all_metrics['B'] = {'config': 'B', 'accuracy': round(acc, 4), 'avg_cost': COST['cheap'], 'n': len(res)}
print(f' Acc: {acc:.3f}')
# C
print('\nConfig C: cheap + strong verifier')
res = []
for i, ex in enumerate(data):
if i % 20 == 0: print(f' {i}/{len(data)}')
cheap_pred = predict_action(cm, ctok, build_proposer_messages(ex), device)
accepted = acc_rej(sm, stok, cheap_pred, ex['messages'], device)
if accepted:
pred, cost = cheap_pred, COST['cheap'] + COST['verify_check']
else:
pred = predict_action(sm, stok, build_proposer_messages(ex), device)
cost = COST['cheap'] + COST['verify_check'] + COST['strong']
res.append({'pred': pred, 'true': ex['action_type'], 'cost': cost, 'accepted': accepted})
acc = sum(1 for r in res if r['pred'] == r['true']) / len(res)
ar = sum(1 for r in res if r['accepted']) / len(res)
all_metrics['C'] = {'config': 'C', 'accuracy': round(acc, 4), 'avg_cost': round(sum(r['cost'] for r in res)/len(res), 4), 'accept_rate': round(ar, 4), 'n': len(res)}
print(f' Acc: {acc:.3f} | Accept: {ar:.3f}')
# D
THR = -1.0
print(f'\nConfig D: cheap + reward verifier (thr={THR})')
res = []
for i, ex in enumerate(data):
if i % 20 == 0: print(f' {i}/{len(data)}')
cheap_pred = predict_action(cm, ctok, build_proposer_messages(ex), device)
score = get_reward_score(vm, vtok, build_reward_text(cheap_pred, ex), device)
res.append({'pred': cheap_pred, 'true': ex['action_type'], 'cost': COST['cheap'] + COST['verify_check'], 'accepted': score >= THR, 'score': score})
acc = sum(1 for r in res if r['pred'] == r['true']) / len(res)
ar = sum(1 for r in res if r['accepted']) / len(res)
scores = [r['score'] for r in res]
all_metrics['D'] = {'config': 'D', 'accuracy': round(acc, 4), 'avg_cost': round(sum(r['cost'] for r in res)/len(res), 4), 'accept_rate': round(ar, 4), 'mean_score': round(sum(scores)/len(scores), 3), 'min_score': round(min(scores), 3), 'max_score': round(max(scores), 3), 'n': len(res)}
print(f' Acc: {acc:.3f} | Accept: {ar:.3f} | Score: {sum(scores)/len(scores):.3f}')
# E
N = 3
print(f'\nConfig E: multi-proposal (n={N})')
res = []
for i, ex in enumerate(data):
if i % 10 == 0: print(f' {i}/{len(data)}')
msgs = build_proposer_messages(ex)
text = ctok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
proposals = []
for _ in range(N):
inputs = ctok(text, return_tensors='pt', truncation=True, max_length=2048).to(device)
outputs = cm.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.8, top_p=0.95, pad_token_id=ctok.pad_token_id)
proposals.append(parse_action(ctok.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)))
scored = [(p, get_reward_score(vm, vtok, build_reward_text(p, ex), device)) for p in set(proposals)]
best = max(scored, key=lambda x: x[1])[0]
res.append({'pred': best, 'true': ex['action_type'], 'cost': COST['cheap'] * N + COST['verify_check'] * N})
acc = sum(1 for r in res if r['pred'] == r['true']) / len(res)
all_metrics['E'] = {'config': 'E', 'accuracy': round(acc, 4), 'avg_cost': round(sum(r['cost'] for r in res)/len(res), 4), 'n': len(res)}
print(f' Acc: {acc:.3f}')
print(f'\n{"="*60}')
print(f'{"Config":<6} {"Acc":>8} {"Cost":>8} {"Accept%":>10}')
print('-'*60)
for c in ['A','B','C','D','E']:
m = all_metrics[c]
ar = f'{m.get("accept_rate","-"):.3f}' if isinstance(m.get('accept_rate'), float) else '-'
print(f'{c:<6} {m["accuracy"]:>8.3f} {m["avg_cost"]:>8.3f} {ar:>10}')
print('\nCOST-QUALITY FRONTIER')
for m in sorted(all_metrics.values(), key=lambda x: x['avg_cost']):
print(f" {m['config']}: cost={m['avg_cost']:.3f} acc={m['accuracy']:.3f}")
with open('/tmp/eval_results_v2.json', 'w') as f:
json.dump({'metrics': all_metrics, 'version': 'v2-fixed-prompts', 'n': len(data)}, f, indent=2)
from huggingface_hub import HfApi
api = HfApi()
api.upload_file(path_or_fileobj='/tmp/eval_results_v2.json', path_in_repo='eval_results_v2.json', repo_id=f'{HUB_ORG}/speculative-tool-actions', repo_type='model', commit_message='Eval v2 results (fixed prompt format)')
print('Uploaded!')
if __name__ == '__main__':
main()
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