Create README.md

README.md

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+---
+license: apache-2.0
+language:
+  - en
+  - fr
+  - de
+  - es
+  - it
+  - pt
+  - ru
+  - zh
+  - ja
+pipeline_tag: text-generation
+tags:
+- chat
+---
+
+
+## This repo contains GGUF quants of the model. If you need the original weights, please find them [here](https://huggingface.co/anthracite-org/magnum-v2-123b).
+
+## imatrix_data included
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/hkPzhL-xYPeGGKCyAf3Qd.png)
+This is the sixth 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 [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407).
+
+## Prompting
+Model has been Instruct tuned with the Mistral formatting. A typical input would look like this:
+
+```py
+<s>[INST] SYSTEM MESSAGE\nUSER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]
+```
+
+We also provide SillyTavern presets for [Context](https://huggingface.co/anthracite-org/Magnum-123b-v1/resolve/main/Magnum-Mistral-Context.json) and [Instruct](https://huggingface.co/anthracite-org/Magnum-123b-v1/raw/main/Magnum-Mistral-Instruct.json) respectively.
+
+The Mistral preset included in SillyTavern seems to be misconfigured by default, so we recommend using these as a replacement.
+
+## Credits
+- [anthracite-org/Stheno-Data-Filtered](https://huggingface.co/datasets/anthracite-org/Stheno-Data-Filtered)
+- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
+- [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed)
+
+This model has been a team effort, and the credits goes to all members of Anthracite.
+
+## Training
+The training was done for 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.
+
+In addition to this, we noticed that Mistral Large models seemed much more sensitive to learning rate adjustments than other models:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/xCK3ISKF6pWcMyO7MEzTA.png)
+
+We hypothesize this is primarily due to the particularly narrow and low variance weight distributions typical of Mistral derived models regardless of their scale.
+
+In the end, due to the costs that would be involved in training another full 2 epochs run ($600) on an even lower rate, we settled on our third attempt: 2e-6 with an effective batch size of 64, stopped earlier than the target 2 epochs.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/d9_cBy-DuWrdnoVBbAvRV.png)
+We notice a correlation between the significance of the 2nd epoch loss drop and the strength of the learning rate, implying 4e-6 leads to more catastrophic forgetting.
+
+[<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)
+
+## Safety
+...