Instructions to use CasperYL/pascal-unsloth-mistral-lora-chs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CasperYL/pascal-unsloth-mistral-lora-chs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.3-bnb-4bit") model = PeftModel.from_pretrained(base_model, "CasperYL/pascal-unsloth-mistral-lora-chs") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use CasperYL/pascal-unsloth-mistral-lora-chs with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for CasperYL/pascal-unsloth-mistral-lora-chs to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for CasperYL/pascal-unsloth-mistral-lora-chs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for CasperYL/pascal-unsloth-mistral-lora-chs to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="CasperYL/pascal-unsloth-mistral-lora-chs", max_seq_length=2048, )
阿獭 Mistral LoRA CHS
一个用于模仿《动物森友会》中阿獭中文“人生小哲理”风格的 LoRA adapter。
这是一个粉丝向 / 研究向的小项目,用于本地测试角色风格微调效果。本项目与 Nintendo、Animal Crossing 或任何官方权利方无关。
Base Model
This adapter was trained on:
unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This is a LoRA adapter only. It is not a standalone model.
Intended Use
适合用于短的单轮中文风格生成:
- 阿獭式随口聊天
- 海边、扇贝、发呆相关的小感悟
- 简短、出人意料的中文哲理句
- 本地 LoRA 推理测试
Limitations
- 单轮效果明显好于多轮聊天。
- 多轮历史开启后,回答可能跑偏、重复或变得不自然。
- 可能生成不通顺、不准确或非官方的句子。
- 它是在模仿风格,不是官方语录检索器。
- 中文效果目前弱于英文版本。
Example Prompt
玩家:我潜水的时候捡到一个扇贝。
阿獭:
Loading Example
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
adapter_id = "CasperYL/pascal-unsloth-mistral-lora-chs"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(
"unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
device_map="auto",
)
model = PeftModel.from_pretrained(base_model, adapter_id)
Training Data
The adapter was trained from a small, single-turn Alpaca-style dataset built from Simplified Chinese 阿獭-style quotes and hand-written player prompts.
License / Rights Note
这是一个粉丝向 adapter,仅用于研究和个人实验。Animal Crossing 和阿獭属于其相应权利方。使用时请同时遵守 base model license 以及源材料相关权利限制。
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Model tree for CasperYL/pascal-unsloth-mistral-lora-chs
Base model
mistralai/Mistral-7B-v0.3 Finetuned
mistralai/Mistral-7B-Instruct-v0.3