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--- |
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license: other |
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license_name: tongyi-qianwen |
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license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE |
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language: |
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- en |
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- zh |
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pipeline_tag: text-generation |
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tags: |
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- chat |
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--- |
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# Roleplay Quantization in EXL2 format for Magnum v1 |
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Quantized using the [cleaned PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) roleplay dataset. Uploading as I didn't see anyone else do this one yet. |
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[4.0bpw8h quants](https://huggingface.co/luigi86/magnum-72b-v1-exl2-rpcal/tree/4.0bpw8h) (tested and working on two 3090s with Q4 cache at 32k context) |
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[8.0bpw8h quants](https://huggingface.co/luigi86/magnum-72b-v1-exl2-rpcal/tree/8.0bpw8h) |
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See [original model](https://huggingface.co/alpindale/magnum-72b-v1) for further details. |
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# Original Model card |
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This is the first in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of [Qwen-2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct). |
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## Prompting |
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Model has been Instruct tuned with the ChatML formatting. A typical input would look like this: |
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```py |
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"""<|im_start|>user |
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Hi there!<|im_end|> |
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<|im_start|>assistant |
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Nice to meet you!<|im_end|> |
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<|im_start|>user |
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Can I ask a question?<|im_end|> |
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<|im_start|>assistant |
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""" |
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``` |
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## Credits |
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This model has been a team effort, credits go to: |
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- [Sao10K](https://huggingface.co/Sao10K) for help with (and cleaning up!) the dataset. |
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- [alpindale](https://huggingface.co/alpindale) for the training. |
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- [kalomaze](https://huggingface.co/kalomaze) for helping with the hyperparameter tuning. |
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- Various other people for their continued help as we tuned the parameters, restarted failed runs. In no particular order: [Doctor Shotgun](https://huggingface.co/Doctor-Shotgun), [Lucy](https://huggingface.co/lucyknada), [Nopm](https://huggingface.co/nopm), [Mango](https://huggingface.co/MangoMango69420), and the rest of the Silly Tilly. |
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And last but not least, we'd like to thank [Kearm](https://twitter.com/Nottlespike) for sponsoring the compute needed to train this model. |
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## Training |
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The training was done with 55 million tokens of high-quality RP data, over 1.5 epochs. We used 8x [AMD Instinct™ MI300X Accelerators](https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html) for the full-parameter fine-tuning of the model. |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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## Safety |
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... |
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