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tools/test_meta_fp16.py

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+#!/usr/bin/env python3
+"""
+Minimal loader test for Meta's FP16/BF16 Llama models (no GPTQ)
+"""
+import os
+import traceback
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+def test_meta_fp16_loader():
+    """Test loading Meta's Llama model with device_map=auto"""
+    MODEL_ID = "meta-llama/Meta-Llama-3.1-8B"  # Meta's repo
+    
+    print(f"=== Testing Meta FP16 Loader: {MODEL_ID} ===")
+    
+    try:
+        # Load tokenizer
+        print("Loading tokenizer...")
+        tok = AutoTokenizer.from_pretrained(
+            MODEL_ID, 
+            use_fast=True,
+            token=os.getenv("HF_TOKEN")
+        )
+        print("✓ Tokenizer loaded")
+        
+        # Load model with device_map="auto"
+        print("Loading model...")
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID,
+            torch_dtype="auto",
+            device_map="auto",          # let accelerate place it
+            trust_remote_code=True,
+            token=os.getenv("HF_TOKEN")
+        )
+        
+        # Check device placement
+        device = next(model.parameters()).device
+        print(f"✓ Model loaded on device: {device}")
+        
+        # Test generation
+        prompt = "The benefits of GPU inference are"
+        inputs = tok(prompt, return_tensors="pt").to(device)
+        
+        print("Testing generation...")
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=16,
+                do_sample=False,
+                pad_token_id=tok.eos_token_id
+            )
+        
+        result = tok.decode(outputs[0], skip_special_tokens=True)
+        print(f"✓ Generation test passed:")
+        print(f"Output: {result}")
+        
+        return True
+        
+    except Exception as e:
+        print(f"✗ Error: {type(e).__name__} - {e}")
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    import torch
+    print("torch:", torch.__version__)
+    print("cuda available:", torch.cuda.is_available())
+    print()
+    
+    test_meta_fp16_loader()