Add plain text prompt support and sample limiting to generate-responses.py

- Add --prompt-column option to accept plain text prompts, automatically converting to chat format
- Add --max-samples option to limit dataset processing for testing and development
- Update README.md with examples showing both chat message and plain text prompt usage
- Enhance dataset card generation to reflect input column type (chat vs plain text)
- Improve validation logic to handle both input modes

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

README.md

@@ -52,32 +52,43 @@ hf jobs uv run \
 
 ### generate-responses.py
 
-Generate responses for chat-formatted prompts using generative LLMs (e.g., Llama, Qwen, Mistral) with vLLM's high-performance inference engine.
+Generate responses for prompts using generative LLMs (e.g., Llama, Qwen, Mistral) with vLLM's high-performance inference engine.
 
 **Features:**
 
 - 💬 Automatic chat template application
+- 📝 Support for both chat messages and plain text prompts
 - 🔀 Multi-GPU tensor parallelism support
 - 📏 Smart filtering for prompts exceeding context length
 - 📊 Comprehensive dataset cards with generation metadata
 - ⚡ HF Transfer enabled for fast model downloads
 - 🎛️ Full control over sampling parameters
+- 🎯 Sample limiting with `--max-samples` for testing
 
 **Usage:**
 
 ```bash
-# Local execution with default Qwen model
+# With chat-formatted messages (default)
 uv run generate-responses.py \
     username/input-dataset \
     username/output-dataset \
     --messages-column messages \
     --max-tokens 1024
 
+# With plain text prompts (NEW!)
+uv run generate-responses.py \
+    username/input-dataset \
+    username/output-dataset \
+    --prompt-column question \
+    --max-tokens 1024 \
+    --max-samples 100
+
 # With custom model and parameters
 uv run generate-responses.py \
     username/input-dataset \
     username/output-dataset \
     --model-id meta-llama/Llama-3.1-8B-Instruct \
+    --prompt-column text \
     --temperature 0.9 \
     --top-p 0.95 \
     --max-model-len 8192

generate-responses.py

@@ -82,6 +82,7 @@ def create_dataset_card(
     source_dataset: str,
     model_id: str,
     messages_column: str,
+    prompt_column: Optional[str],
     sampling_params: SamplingParams,
     tensor_parallel_size: int,
     num_examples: int,
@@ -119,7 +120,7 @@ This dataset contains generated responses for prompts from [{source_dataset}](ht
 ## Generation Details
 
 - **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
-- **Messages Column**: `{messages_column}`
+- **Input Column**: `{prompt_column if prompt_column else messages_column}` ({'plain text prompts' if prompt_column else 'chat messages'})
 - **Model**: [{model_id}](https://huggingface.co/{model_id})
 - **Number of Examples**: {num_examples:,}
 - **Generation Date**: {generation_time}{filtering_section}
@@ -154,7 +155,7 @@ uv run https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-respons
     {source_dataset} \\
     <output-dataset> \\
     --model-id {model_id} \\
-    --messages-column {messages_column} \\
+    {'--prompt-column ' + prompt_column if prompt_column else '--messages-column ' + messages_column} \\
     --temperature {sampling_params.temperature} \\
     --top-p {sampling_params.top_p} \\
     --top-k {sampling_params.top_k} \\
@@ -168,6 +169,7 @@ def main(
     output_dataset_hub_id: str,
     model_id: str = "Qwen/Qwen3-30B-A3B-Instruct-2507",
     messages_column: str = "messages",
+    prompt_column: Optional[str] = None,
     output_column: str = "response",
     temperature: float = 0.7,
     top_p: float = 0.8,
@@ -179,6 +181,7 @@ def main(
     max_model_len: Optional[int] = None,
     tensor_parallel_size: Optional[int] = None,
     skip_long_prompts: bool = True,
+    max_samples: Optional[int] = None,
     hf_token: Optional[str] = None,
 ):
     """
@@ -189,6 +192,7 @@ def main(
         output_dataset_hub_id: Where to save results on Hugging Face Hub
         model_id: Hugging Face model ID for generation
         messages_column: Column name containing chat messages
+        prompt_column: Column name containing plain text prompts (alternative to messages_column)
         output_column: Column name for generated responses
         temperature: Sampling temperature
         top_p: Top-p sampling parameter
@@ -200,6 +204,7 @@ def main(
         max_model_len: Maximum model context length (None uses model default)
         tensor_parallel_size: Number of GPUs to use (auto-detect if None)
         skip_long_prompts: Skip prompts exceeding max_model_len instead of failing
+        max_samples: Maximum number of samples to process (None for all)
         hf_token: Hugging Face authentication token
     """
     generation_start_time = datetime.now().isoformat()
@@ -261,15 +266,34 @@ def main(
     # Load dataset
     logger.info(f"Loading dataset: {src_dataset_hub_id}")
     dataset = load_dataset(src_dataset_hub_id, split="train")
+    
+    # Apply max_samples if specified
+    if max_samples is not None and max_samples < len(dataset):
+        logger.info(f"Limiting dataset to {max_samples} samples")
+        dataset = dataset.select(range(max_samples))
+    
     total_examples = len(dataset)
     logger.info(f"Dataset loaded with {total_examples:,} examples")
 
-    # Validate messages column
-    if messages_column not in dataset.column_names:
-        logger.error(
-            f"Column '{messages_column}' not found. Available columns: {dataset.column_names}"
-        )
-        sys.exit(1)
+    # Determine which column to use and validate
+    if prompt_column:
+        # Use prompt column mode
+        if prompt_column not in dataset.column_names:
+            logger.error(
+                f"Column '{prompt_column}' not found. Available columns: {dataset.column_names}"
+            )
+            sys.exit(1)
+        logger.info(f"Using prompt column mode with column: '{prompt_column}'")
+        use_messages = False
+    else:
+        # Use messages column mode
+        if messages_column not in dataset.column_names:
+            logger.error(
+                f"Column '{messages_column}' not found. Available columns: {dataset.column_names}"
+            )
+            sys.exit(1)
+        logger.info(f"Using messages column mode with column: '{messages_column}'")
+        use_messages = True
 
     # Get effective max length for filtering
     if max_model_len is not None:
@@ -280,18 +304,29 @@ def main(
     logger.info(f"Using effective max model length: {effective_max_len}")
 
     # Process messages and apply chat template
-    logger.info("Applying chat template to messages...")
+    logger.info("Preparing prompts...")
     all_prompts = []
     valid_prompts = []
     valid_indices = []
     skipped_info = []
 
-    for i, example in enumerate(tqdm(dataset, desc="Processing messages")):
-        messages = example[messages_column]
-        # Apply chat template
-        prompt = tokenizer.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
-        )
+    for i, example in enumerate(tqdm(dataset, desc="Processing prompts")):
+        if use_messages:
+            # Messages mode: use existing chat messages
+            messages = example[messages_column]
+            # Apply chat template
+            prompt = tokenizer.apply_chat_template(
+                messages, tokenize=False, add_generation_prompt=True
+            )
+        else:
+            # Prompt mode: convert plain text to messages format
+            user_prompt = example[prompt_column]
+            messages = [{"role": "user", "content": user_prompt}]
+            # Apply chat template
+            prompt = tokenizer.apply_chat_template(
+                messages, tokenize=False, add_generation_prompt=True
+            )
+        
         all_prompts.append(prompt)
 
         # Count tokens if filtering is enabled
@@ -352,6 +387,7 @@ def main(
         source_dataset=src_dataset_hub_id,
         model_id=model_id,
         messages_column=messages_column,
+        prompt_column=prompt_column,
         sampling_params=sampling_params,
         tensor_parallel_size=tensor_parallel_size,
         num_examples=total_examples,
@@ -419,12 +455,22 @@ Examples:
             default="messages",
             help="Column containing chat messages (default: messages)",
         )
+        parser.add_argument(
+            "--prompt-column",
+            type=str,
+            help="Column containing plain text prompts (alternative to --messages-column)",
+        )
         parser.add_argument(
             "--output-column",
             type=str,
             default="response",
             help="Column name for generated responses (default: response)",
         )
+        parser.add_argument(
+            "--max-samples",
+            type=int,
+            help="Maximum number of samples to process (default: all)",
+        )
         parser.add_argument(
             "--temperature",
             type=float,
@@ -502,6 +548,7 @@ Examples:
             output_dataset_hub_id=args.output_dataset_hub_id,
             model_id=args.model_id,
             messages_column=args.messages_column,
+            prompt_column=args.prompt_column,
             output_column=args.output_column,
             temperature=args.temperature,
             top_p=args.top_p,
@@ -513,6 +560,7 @@ Examples:
             max_model_len=args.max_model_len,
             tensor_parallel_size=args.tensor_parallel_size,
             skip_long_prompts=args.skip_long_prompts,
+            max_samples=args.max_samples,
             hf_token=args.hf_token,
         )
     else: