| Model Type | |
| Use Cases |
| Areas: | | Research, Instruction Following |
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| Applications: | | General Question Answering, Conversation, Education |
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| Primary Use Cases: | | Chatbot, Instruction following assistant |
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| Limitations: | | May produce problematic outputs, especially upon being prompted to do so, Not aligned with human preferences using techniques like RLHF |
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| Considerations: | | Prompts should be carefully crafted to avoid unintended outputs. |
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| Additional Notes | | The model is compatible with multiple UIs and libraries for easier accessibility. It has undergone quantization by TheBloke for enhanced deployment efficiency. The Blink's LLM work is funded by a grant from andreessen horowitz (a16z). |
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| Supported Languages | | English (Good proficiency) |
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| Training Details |
| Data Sources: | | WizardLM, QingyiSi/Alpaca-CoT, GPTeacher-General-Instruct, metaeval/ScienceQA_text_only, openai/summarize_from_feedback, camel-ai/math, camel-ai/physics, camel-ai/chemistry, camel-ai/biology, winglian/evals, ARC-Easy, ARC-Challenge, hellaswag, riddle_sense, gsm8k |
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| Data Volume: | |
| Methodology: | | Instruction fine-tuning on open datasets |
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| Context Length: | |
| Training Time: | | 7.5 hours on 6XA100 80GB GPUs for 1 epoch |
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| Hardware Used: | |
| Model Architecture: | | Based on LlaMA architecture |
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| Input Output |
| Input Format: | | A chat between a USER and ASSISTANT |
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| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Ensure correct prompt template for optimal response generation. |
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| Release Notes |
| Version: | |
| Notes: | | Fixes to datasets dropped during training. |
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