| Model Type | | storytelling, instruction-following |
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| Use Cases |
| Areas: | | storytelling, creative writing, role-playing |
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| Applications: | | novel writing, interactive fiction |
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| Primary Use Cases: | | >40K context, instruct-enhanced storytelling |
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| Additional Notes | | Tested for novel-style continuation, assistant-type responses, and long context analysis without refusals. Specific to certain configurations for performance of storytelling in longer contexts. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | DrNicefellow/ChatAllInOne-Yi-34B-200K-V1, migtissera/Tess-34B-v1.5b, cgato/Thespis-34b-v0.7, Doctor-Shotgun/limarpv3-yi-llama-34b-lora, adamo1139/yi-34b-200k-rawrr-dpo-2, migtissera/Tess-M-Creative-v1.0, NousResearch/Nous-Capybara-34B |
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| Methodology: | | Merge using DARE (Discrete Alignment-based Row Expansion) technique |
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| Context Length: | |
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| Input Output |
| Input Format: | | SYSTEM: {system_message} USER: {prompt} ASSISTANT: |
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| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Running Chinese models with large tokenizer vocabularies like Yi need careful parameter tuning due to large logit sampling tails. |
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