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mistralai/Mistral-7B-Instruct-v0.3 plus the runtime profile that produced the preferred "hard coherent board character" behavior locally.1{
2 "adapter_scale": 1.15,
3 "temperature": 0.72,
4 "top_p": 0.86,
5 "top_k": 60,
6 "repetition_penalty": 1.08,
7 "no_repeat_ngram_size": 4,
8 "max_new_tokens": 260,
9 "min_new_tokens": 0,
10 "max_context_tokens": 3072,
11 "primer": "hard",
12 "user_wrapper": "board-hard",
13 "assistant_prefix": ""
14}chat_lora.py. If you load only the adapter in a generic chat UI, it may become softer or more assistant-like.1cd F:\mistral-board-training
2powershell -ExecutionPolicy Bypass -File .\scripts\start_chat_lora_hard.ps11import torch
2from peft import PeftModel
3from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
4
5base = "mistralai/Mistral-7B-Instruct-v0.3"
6adapter = "YOUR_USERNAME/mistral-board-hard-character-lora"
7
8quant = BitsAndBytesConfig(
9 load_in_4bit=True,
10 bnb_4bit_quant_type="nf4",
11 bnb_4bit_use_double_quant=True,
12 bnb_4bit_compute_dtype=torch.float16,
13)
14
15tokenizer = AutoTokenizer.from_pretrained(base)
16model = AutoModelForCausalLM.from_pretrained(
17 base,
18 quantization_config=quant,
19 torch_dtype=torch.float16,
20 device_map="auto",
21)
22model = PeftModel.from_pretrained(model, adapter)
23model.eval()JAN_LMSTUDIO_GGUF_GUIDE_RU.md and export_hard_character_gguf.ps1.adapter_model.safetensors - LoRA adapter weightsadapter_config.json - PEFT configHARD_CHARACTER_SETTINGS.json - saved runtime settingschat_lora.py - local chat runner with hard wrapper/primerREADME_HARD_CHARACTER_RU.md - Russian local usage notesJAN_LMSTUDIO_GGUF_GUIDE_RU.md - Russian Jan/LM Studio guide