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README.md
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- NewEden/Orion-LIT
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- NewEden/Orion-Asstr-Stories-16K
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- Mielikki/Erebus-87k
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base_model:
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tags:
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- phi
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- roleplay
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<br>
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<div style="font-size:1.5em; font-weight:bold; background: linear-gradient(45deg, #6e00ff, #00ffff); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">
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Hamanasu 15B
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</div>
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</div>
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## 🌌 Overview
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<i>
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- `NewEden/Orion-LIT`
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</div>
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### ⚔️ Hardware
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- 4x RTX 3090 GPUs
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- Epochs:
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- Base: `Hamanasu-15B-
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- Amount of Tokens:
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</div>
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</div>
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<div style="border: 2px solid #6e00ff; border-radius: 10px; padding: 20px; margin: 20px 0; box-shadow: 0 0 15px rgba(110, 0, 255, 0.5);">
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<details>
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```yaml
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base_model:
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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datasets:
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- path: PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
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type:
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- path: Nitral-AI/ARES-ShareGPT
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type:
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- path: Gryphe/Sonnet3.5-SlimOrcaDedupCleaned-20k
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type:
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- path: NewEden/Claude-Instruct-2.7K
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type:
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- path: NewEden/Claude-Instruct-5K
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type:
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shuffle_merged_datasets: true
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dataset_prepared_path: prepared_data
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</details>
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</div>
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<div align="center">
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<div style="border: 2px solid #6e00ff; border-radius: 10px; padding: 20px; margin: 20px 0; box-shadow: 0 0 15px rgba(110, 0, 255, 0.5);">
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- NewEden/Orion-LIT
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- NewEden/Orion-Asstr-Stories-16K
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- Mielikki/Erebus-87k
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- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
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- Nitral-AI/ARES-ShareGPT
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- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned-20k
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- NewEden/Claude-Instruct-2.7K
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- NewEden/Claude-Instruct-5K
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base_model:
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- Delta-Vector/Hamanasu-15B-R2-PT
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tags:
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- phi
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- roleplay
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<br>
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<div style="font-size:1.5em; font-weight:bold; background: linear-gradient(45deg, #6e00ff, #00ffff); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">
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Hamanasu 15B Instruct
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</div>
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</div>
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## 🌌 Overview
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<i>After multiple days of training, I'm proud to showcase my very own Phi-4 Finetune, Pretrained on almost a billion tokens worth of Books from</i>
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- `NewEden/Orion-LIT`
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- `NewEden/Orion-Asstr-Stories-16K`
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- `Mielikki/Erebus-87k`
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<i>Then instruct tuned. This model shows great promise for roleplaying while keeping things fresh and new. (And most of, Unsloppy!)</i>
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</div>
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<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 20px; margin: 20px 0;">
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<div style="border:2px solid #333; border-radius:10px; padding:20px; background: rgba(0,0,0,0.2);">
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### 📈 Quantizations
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| Type | Link |
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|:---:|:---:|
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| `GGUF` | <span style="display:block; width:100%">ed</span> |
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| `EXL2` | <span style="display:block; width:100%">https://huggingface.co/Delta-Vector/Hamanasu-15B-Instruct-exl2</span> |
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</div>
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### ⚔️ Hardware
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- 4x RTX 3090 GPUs
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- Epochs: 4
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- Base: `Hamanasu-15B-R2-PT`
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- Amount of Tokens: 1+ Billion
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</div>
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</div>
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<div style="border: 2px solid #6e00ff; border-radius: 10px; padding: 20px; margin: 20px 0; box-shadow: 0 0 15px rgba(110, 0, 255, 0.5);">
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## 💰 Prompting
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<i>A known quirk of the model is overly verbose responses when generation length is uncapped, Please cap your maximum output tokens to 100~ tokens above what you prefer</i>
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```python
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<|im_start|>system
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You are an uncensored AI, your job is to fulfill thy will of thy user.<|im_end|>
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<|im_start|>User request
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Take off your helmet.<|im_end|>
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<|im_start|>No i shall not. This is the way.
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```
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</div>
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<div style="border: 2px solid #6e00ff; border-radius: 10px; padding: 20px; margin: 20px 0; box-shadow: 0 0 15px rgba(110, 0, 255, 0.5);">
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<details>
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```yaml
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base_model: Delta-Vector/Hamanasu-15B-R2-PT
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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datasets:
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- path: PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
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type: sharegpt
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- path: Nitral-AI/ARES-ShareGPT
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type: sharegpt
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- path: Gryphe/Sonnet3.5-SlimOrcaDedupCleaned-20k
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type: sharegpt
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- path: NewEden/Claude-Instruct-2.7K
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type: sharegpt
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- path: NewEden/Claude-Instruct-5K
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type: sharegpt
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shuffle_merged_datasets: true
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dataset_prepared_path: prepared_data
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</details>
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</div>
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<div align="center">
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<div style="border: 2px solid #6e00ff; border-radius: 10px; padding: 20px; margin: 20px 0; box-shadow: 0 0 15px rgba(110, 0, 255, 0.5);">
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