tokenizer = AutoTokenizer.from_pretrained("ayjays132/NeuroReasoner-PlanningHead-1") model.set_tokenizer(tokenizer) model.eval()
<reasoning> This requires a systematic approach to ensure all aspects are considered. </reasoning>
The solution involves: """
# Check plan vector extraction
if outputs.get('plan_vector') is not None:
plan_vec = outputs['plan_vector'][0]
print(f"✓ Plan vector extracted: shape {plan_vec.shape}, norm {torch.norm(plan_vec).item():.4f}")
# Generate text
generated = model.generate( inputs['input_ids'], attention_mask=inputs['attention_mask'], max_length=inputs['input_ids'].shape[1] + 300, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.2, )
# Decode
if hasattr(generated, 'sequences'): generated_ids = generated.sequences[0].cpu().tolist() else: generated_ids = generated[0].cpu().tolist()
result = tokenizer.decode(generated_ids, skip_special_tokens=False)
print(result)</code></pre>