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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-0.5B-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- CoT |
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- Reasoner |
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- Qwen |
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--- |
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# **FastThink-1.5B-Tiny** |
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# **Dataset Preparation** |
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This script is designed to load, process, and combine multiple datasets into a single, standardized format suitable for training conversational AI models. The script uses the `datasets` library to load and manipulate the datasets, and the `chat_templates` library to standardize the conversation format. |
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## Example |
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```python |
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# Load the initial three datasets |
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dataset1 = load_dataset("PowerInfer/LONGCOT-Refine-500K", split="train") |
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dataset2 = load_dataset("amphora/QwQ-LongCoT-130K", split="train") |
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dataset3 = load_dataset("AI-MO/NuminaMath-CoT", split="train") |
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# Map conversation columns for all datasets |
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dataset1 = dataset1.map(add_conversations_column, batched=False) |
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dataset2 = dataset2.map(add_conversations_column_prompt_qwq, batched=False) |
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dataset3 = dataset3.map(add_conversations_column_prompt_solution, batched=False) |
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# Combine all datasets |
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combined_dataset = concatenate_datasets([dataset1, dataset2, dataset3]) |
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# Standardize using the ShareGPT format |
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combined_dataset = standardize_sharegpt(combined_dataset) |
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# Initialize the tokenizer with a specific chat template |
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tokenizer = get_chat_template(tokenizer, chat_template="qwen-2.5") |
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# Apply formatting function to the combined dataset |
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combined_dataset = combined_dataset.map(formatting_prompts_func, batched=True) |
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# Print the first few examples to verify the output |
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print(combined_dataset[:50000]) |
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``` |
