Update dependencies

gpt_oss_minimal.py

@@ -2,8 +2,9 @@
 # requires-python = ">=3.10"
 # dependencies = [
 #     "torch",
-#     "transformers>=4.45.0",
+#     "transformers>=4.55.0",
 #     "datasets",
+#     "hf-xet >= 1.1.7",
 #     "huggingface-hub[hf_transfer]",
 #     "accelerate",
 # ]
@@ -44,38 +45,52 @@ os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Minimal GPT OSS generation for HF Jobs")
-    parser.add_argument("--input-dataset", required=True, help="Input dataset on HF Hub")
-    parser.add_argument("--output-dataset", required=True, help="Output dataset on HF Hub")
-    parser.add_argument("--prompt-column", default="prompt", help="Column containing prompts")
+    parser = argparse.ArgumentParser(
+        description="Minimal GPT OSS generation for HF Jobs"
+    )
+    parser.add_argument(
+        "--input-dataset", required=True, help="Input dataset on HF Hub"
+    )
+    parser.add_argument(
+        "--output-dataset", required=True, help="Output dataset on HF Hub"
+    )
+    parser.add_argument(
+        "--prompt-column", default="prompt", help="Column containing prompts"
+    )
     parser.add_argument("--model-id", default="openai/gpt-oss-20b", help="Model to use")
     parser.add_argument("--max-samples", type=int, help="Limit number of samples")
-    parser.add_argument("--max-new-tokens", type=int, default=1024, help="Max tokens to generate")
+    parser.add_argument(
+        "--max-new-tokens", type=int, default=1024, help="Max tokens to generate"
+    )
     args = parser.parse_args()
 
     # Check GPU availability
     if not torch.cuda.is_available():
-        print("ERROR: GPU required. Use HF Jobs with --flavor a10g-small or run on GPU machine")
+        print(
+            "ERROR: GPU required. Use HF Jobs with --flavor a10g-small or run on GPU machine"
+        )
         sys.exit(1)
-    
+
     print(f"GPU: {torch.cuda.get_device_name(0)}")
     print(f"Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f}GB")
 
     # Authenticate
     token = os.environ.get("HF_TOKEN") or get_token()
     if not token:
-        print("ERROR: HF_TOKEN required. Set HF_TOKEN env var or run: huggingface-cli login")
+        print(
+            "ERROR: HF_TOKEN required. Set HF_TOKEN env var or run: huggingface-cli login"
+        )
         sys.exit(1)
     login(token=token, add_to_git_credential=False)
 
     # Load tokenizer (following official blog)
     print(f"Loading tokenizer: {args.model_id}")
     tokenizer = AutoTokenizer.from_pretrained(args.model_id)
-    
+
     # Load model (without Flash Attention 3 for compatibility)
     print(f"Loading model: {args.model_id}")
     print("Note: MXFP4 will auto-dequantize to bf16 on non-Hopper GPUs")
-    
+
     model = AutoModelForCausalLM.from_pretrained(
         args.model_id,
         device_map="auto",
@@ -88,60 +103,57 @@ def main():
     # Load dataset
     print(f"Loading dataset: {args.input_dataset}")
     dataset = load_dataset(args.input_dataset, split="train")
-    
+
     if args.prompt_column not in dataset.column_names:
         print(f"ERROR: Column '{args.prompt_column}' not found")
         print(f"Available columns: {dataset.column_names}")
         sys.exit(1)
-    
+
     # Limit samples if requested
     if args.max_samples:
         dataset = dataset.select(range(min(args.max_samples, len(dataset))))
-    
+
     print(f"Processing {len(dataset)} examples")
 
     # Process each example
     results = []
     for i, example in enumerate(dataset):
-        print(f"[{i+1}/{len(dataset)}] Processing...")
-        
+        print(f"[{i + 1}/{len(dataset)}] Processing...")
+
         prompt_text = example[args.prompt_column]
-        
+
         # Create messages (user message only, as per official examples)
-        messages = [
-            {"role": "user", "content": prompt_text}
-        ]
-        
+        messages = [{"role": "user", "content": prompt_text}]
+
         # Apply chat template (following official blog)
         inputs = tokenizer.apply_chat_template(
-            messages,
-            add_generation_prompt=True,
-            return_tensors="pt",
-            return_dict=True
+            messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
         ).to(model.device)
-        
+
         # Generate
         with torch.no_grad():
             generated = model.generate(
-                **inputs, 
+                **inputs,
                 max_new_tokens=args.max_new_tokens,
                 do_sample=True,
                 temperature=0.7,
             )
-        
+
         # Decode only the generated part (excluding input)
         response = tokenizer.decode(
-            generated[0][inputs["input_ids"].shape[-1]:],
-            skip_special_tokens=False  # Keep channel markers
+            generated[0][inputs["input_ids"].shape[-1] :],
+            skip_special_tokens=False,  # Keep channel markers
         )
-        
+
         # Store raw output with channel markers
-        results.append({
-            "prompt": prompt_text,
-            "raw_output": response,
-            "model": args.model_id,
-        })
-        
+        results.append(
+            {
+                "prompt": prompt_text,
+                "raw_output": response,
+                "model": args.model_id,
+            }
+        )
+
         # Show preview of output structure
         if i == 0:
             print(f"Sample output preview (first 200 chars):")
@@ -151,18 +163,20 @@ def main():
     # Create and push dataset
     print("\nCreating output dataset...")
     output_dataset = Dataset.from_list(results)
-    
+
     print(f"Pushing to {args.output_dataset}...")
     output_dataset.push_to_hub(args.output_dataset, token=token)
-    
+
     print(f"\n✅ Complete!")
     print(f"Dataset: https://huggingface.co/datasets/{args.output_dataset}")
     print(f"\nOutput format:")
     print("- prompt: Original prompt")
     print("- raw_output: Full model response with channel markers")
     print("- model: Model ID used")
-    print("\nTo extract final response, look for text after '<|channel|>final<|message|>'")
+    print(
+        "\nTo extract final response, look for text after '<|channel|>final<|message|>'"
+    )
 
 
 if __name__ == "__main__":
-    main()
+    main()