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
QuantFactory/Crimson_Dawn-v0.2-GGUF
This is quantized version of Epiculous/Crimson_Dawn-v0.2 created using llama.cpp
Original Model Card
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 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!
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
Violet_Twilight-Nitral-Special
Training
Training was done twice over 2 epochs each on two 2x NVIDIA A6000 GPUs 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.
