Training process: SFT > DPO > KTO
SFT with RP/ERP, Stories and in character assistant data.
DPO focused on reducing repetition, misgendered characters and slop.
KTO focused on further reducing repetition and slop.
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Axolotl configs
Not optimized for cost / performance efficiency, YMMV.
SFT 1*H100
# ====================
# MODEL CONFIGURATION
# ====================
base_model: ConicCat/Mistral-Small-3.2-AntiRep-24B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: mistral_v7_tekken
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# ====================
# DATASET CONFIGURATION
# ====================
datasets:
- path: ./dataset.jsonl
type: chat_template
split: train
chat_template_strategy: tokenizer
field_messages: messages
message_property_mappings:
role: role
content: content
roles:
user: ["user"]
assistant: ["assistant"]
system: ["system"]
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