初始化项目，由ModelHub XC社区提供模型

Model: Epiculous/Crimson_Dawn-v0.2-GGUF
Source: Original Platform

README.md

@@ -0,0 +1,55 @@
+---
+license: apache-2.0
+datasets:
+- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
+- anthracite-org/stheno-filtered-v1.1
+- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
+- Gryphe/Sonnet3.5-Charcard-Roleplay
+- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
+- anthracite-org/kalo-opus-instruct-22k-no-refusal
+- anthracite-org/nopm_claude_writing_fixed
+- anthracite-org/kalo_opus_misc_240827
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- ru
+- zh
+- ja
+pipeline_tag: text-generation
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64adfd277b5ff762771e4571/AEWJsybnM6wILRxgwlmrU.png)
+
+Taking what seemed to work out well with Crimson_Dawn-v0.1, the new Crimson_Dawn-v0.2 is the same training methodology, training on [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) this time I've added significantly more data, as well as trained using RSLoRA as opposed to regular LoRA. Another key change is training on ChatML as opposed to Mistral Formatting.
+
+# Quants!
+[full](https://huggingface.co/Epiculous/Crimson_Dawn-v0.2) / [exl2](https://huggingface.co/Epiculous/Crimson_Dawn-v0.2-exl2) / <strong>gguf</strong>
+
+## Prompting
+The v0.2 models are trained on ChatML, the prompting structure goes a little something like this:
+
+```
+<|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
+```
+
+### Context and Instruct
+The v0.2 models are trained on ChatML, please use that Context and Instruct template.
+
+### Current Top Sampler Settings
+[Spicy_Temp](https://files.catbox.moe/9npj0z.json) <br/>
+[Violet_Twilight-Nitral-Special](https://files.catbox.moe/ot54u3.json) <br/>
+
+## Training
+Training was done twice over 2 epochs each on two 2x [NVIDIA A6000 GPUs](https://www.nvidia.com/en-us/design-visualization/rtx-a6000/) using LoRA. A two-phased approach was used in which the base model was trained 2 epochs on RP data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on instruct, and the new instruct LoRA was applied to the modified base, resulting in what you see here.
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)