Add HF reference test script

hf_reference_test.py

@@ -0,0 +1,28 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = '/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4'
+print('Loading tokenizer...', flush=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+print('Loading model...', flush=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, torch_dtype=torch.bfloat16,
+    device_map='auto', trust_remote_code=True, low_cpu_mem_usage=True
+)
+model.eval()
+print('Model loaded!', flush=True)
+
+msg = [{'role':'user','content':'The capital of France is'}]
+ids = tokenizer.apply_chat_template(msg, add_generation_prompt=True, return_tensors='pt').cuda()
+print(f'Input: {ids.shape} tokens: {repr(tokenizer.decode(ids[0]))}', flush=True)
+
+with torch.no_grad():
+    logits = model(ids).logits[0, -1]
+    top10 = torch.topk(logits, 10)
+    print('HF Top-10:', flush=True)
+    for i, (tid, val) in enumerate(zip(top10.indices, top10.values)):
+        print(f'  {i+1}. {repr(tokenizer.decode([tid.item()]))} (id={tid.item()}, logit={val.item():.3f})', flush=True)
+
+    # Generate 10 tokens
+    out = model.generate(ids, max_new_tokens=10, do_sample=False)
+    print(f'Generated: {repr(tokenizer.decode(out[0]))}', flush=True)