|
--- |
|
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 |
|
--- |
|
|
|
 |
|
|
|
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) |
