Upload 2 files

README.md

@@ -1,5 +1,65 @@
-### exl2 quant (measurement.json in main branch)
 ---
-### check revisions for quants
+license: other
+license_name: tongyi-qianwen
+license_link: https://huggingface.co/anthracite-org/magnum-v2-72b/blob/main/LICENSE
+language:
+  - en
+  - fr
+  - de
+  - es
+  - it
+  - pt
+  - ru
+  - zh
+  - ja
+pipeline_tag: text-generation
+tags:
+- chat
 ---
 
+## This repo contains EXL2 quants of the model. If you need the original weights, please find them [here](https://huggingface.co/anthracite-org/magnum-v2-72b).
+## Base repo only contains the measurement file, see revisions for your quant of choice.
+
+- [measurement.json](https://huggingface.co/anthracite-org/magnum-v2-72b-exl2/tree/main)
+- [3.0bpw](https://huggingface.co/anthracite-org/magnum-v2-72b-exl2/tree/3.0bpw)
+- [4.0bpw](https://huggingface.co/anthracite-org/magnum-v2-72b-exl2/tree/4.0bpw)
+- [6.0bpw](https://huggingface.co/anthracite-org/magnum-v2-72b-exl2/tree/6.0bpw)
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/u8B-5bEeroN549uxUIisV.png)
+
+This is the seventh (Lucky!) 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).
+
+## Prompting
+Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:
+
+```py
+"""<|im_start|>user
+Hi there!<|im_end|>
+<|im_start|>assistant
+Nice to meet you!<|im_end|>
+<|im_start|>user
+Can I ask a question?<|im_end|>
+<|im_start|>assistant
+"""
+```
+
+## 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 2 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.
+
+We also trained with a weight decay of 0.01 to help further stabilize the loss trajectory and mitigate catastrophic forgetting, and utilize a peak learning rate of 4e-6 to prevent the 2nd epoch loss from dropping too significantly (as it is a strong indicator of overfitting).
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/hVd5gNqSLOlWTkUb0A7iE.png)
+
+Sample Packing was done for 16k tokens rather than the 8k tokens used in our previous runs.
+
+[<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
+...