Files
Azure_Dusk-v0.2-GGUF/README.md
ModelHub XC 41704e0362 初始化项目,由ModelHub XC社区提供模型
Model: Epiculous/Azure_Dusk-v0.2-GGUF
Source: Original Platform
2026-07-13 15:28:10 +08:00

2.2 KiB

license, datasets, language, pipeline_tag
license datasets language pipeline_tag
apache-2.0
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
en
fr
de
es
it
pt
ru
zh
ja
text-generation

image/png

Following up on Crimson_Dawn-v0.2 we have Azure_Dusk-v0.2! 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.

full / exl2 / gguf

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.

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