Model Type | |
Use Cases |
Areas: | |
Applications: | text generation, coding, summarization, agent/function calling, contextual question answering |
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Primary Use Cases: | Question answering, Coding assistance, Summarization, Agent/function calling |
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Limitations: | The model sometimes adds random extra tokens at the end of responses. It uses a closed-context formatting for more accurate outputs. |
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Considerations: | It is strongly advised to avoid commercial usage due to the OpenAI API usage. |
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Additional Notes | This model is experimental and its responses include some token randomness, requiring special prompt formatting for closed-context tasks. |
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Training Details |
Data Sources: | jondurbin/airoboros-3.2, bluemoon-fandom-1-1-rp-cleaned, boolq, LDJnr/Capybara, jondurbin/cinematika-v0.1, glaiveai/glaive-function-calling-v2, grimulkan/LimaRP-augmented, piqa, Vezora/Tested-22k-Python-Alpaca, mattpscott/airoboros-summarization, unalignment/toxic-dpo-v0.2, allenai/ultrafeedback_binarized_cleaned, argilla/distilabel-intel-orca-dpo-pairs, jondurbin/contextual-dpo-v0.1, jondurbin/gutenberg-dpo-v0.1, jondurbin/py-dpo-v0.1, jondurbin/truthy-dpo-v0.1, lmsys/lmsys-chat-1m |
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Methodology: | Fine-tuning through ChatML prompt formatting using DPO pass and synthetic data generated by Airoboros. |
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Input Output |
Input Format: | ChatML prompt template, using 'apply_chat_template' method for accuracy. |
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Accepted Modalities: | |
Output Format: | Text with structured response format using JSON/YAML for specific tasks. |
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Performance Tips: | Use a low temperature setting for contextually obedient responses. |
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