xet

generate-responses.py

@@ -4,6 +4,7 @@
 #     "datasets",
 #     "flashinfer-python",
 #     "huggingface-hub[hf_transfer]",
+#     "hf-xet",
 #     "torch",
 #     "transformers",
 #     "vllm>=0.8.5",
@@ -120,7 +121,7 @@ This dataset contains generated responses for prompts from [{source_dataset}](ht
 ## Generation Details
 
 - **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
-- **Input Column**: `{prompt_column if prompt_column else messages_column}` ({'plain text prompts' if prompt_column else 'chat messages'})
+- **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}
@@ -155,7 +156,7 @@ uv run https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-respons
     {source_dataset} \\
     <output-dataset> \\
     --model-id {model_id} \\
-    {'--prompt-column ' + prompt_column if prompt_column else '--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} \\
@@ -266,12 +267,12 @@ 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")
 
@@ -326,7 +327,7 @@ def main(
             prompt = tokenizer.apply_chat_template(
                 messages, tokenize=False, add_generation_prompt=True
             )
-        
+
         all_prompts.append(prompt)
 
         # Count tokens if filtering is enabled