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maomao88/anime-personality-chat-gguf overview

Overview This repository contains a GGUF format model, which is a quantized version of Mistral 7B Instruct natively merged with the maomao88/anime waifu mistra…

ggufendataset:maomao88/anime-waifu-personality-chat-with-questionsbase_model:mistralai/Mistral-7B-Instruct-v0.1base_model:quantized:mistralai/Mistral-7B-Instruct-v0.1license:mitendpoints_compatibleregion:usconversational

Runs locally from ~4.07 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
mistral-merged-Q4_K_M.ggufGGUFQ4_K_M4.07 GBDownload

Model Details

Model IDmaomao88/anime-personality-chat-gguf
Authormaomao88
Pipeline
Licensemit
Base modelmistralai/Mistral-7B-Instruct-v0.1
Last modified2026-06-25T03:14:34.000Z

Model README

---

license: mit

datasets:

  • maomao88/anime-waifu-personality-chat-with-questions

language:

  • en

base_model:

  • mistralai/Mistral-7B-Instruct-v0.1

---

Overview

This repository contains a GGUF format model, which is a quantized version of Mistral-7B-Instruct natively merged with the maomao88/anime-waifu-mistral-lora adapter.

The base model was fine-tuned using Low-Rank Adaptation (LoRA) to adapt Mistral's conversational architecture into a highly stylized, engaging, and expressive anime-themed persona, and has been fully baked into the model weights prior to quantization.

Run model with llama_cpp

  1. Install dependency
pip install llama-cpp-python
  1. Load the model
from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="maomao88/anime-personality-chat-gguf",
    filename="mistral-merged-Q4_K_M.gguf",
    n_ctx=2048,       # Set context window token ceiling limit
    n_gpu_layers=-1   # Set to -1 to push ALL layers straight into GPU VRAM (0 for pure CPU)
)
  1. Run the model
def chat_with_personality(trait, user_input, max_new_tokens=100, temperature=0.8):
    """
    Generate a response from the fine-tuned model using a given personality trait.
    """
    messages = [
        {
            "role": "system",
            "content": f"You are an anime character with the following personality: {trait}."
        },
        {
            "role": "user",
            "content": user_input
        }
    ]

    # Map the arguments properly to your function parameters
    response_dict = llm.create_chat_completion(
        messages=messages,
        temperature=temperature,
        max_tokens=max_new_tokens
    )

    # Extract the text string from the OpenAI-style dictionary payload
    assistant_reply = response_dict['choices'][0]['message']['content'].strip()

    return assistant_reply

chat_with_personality("tsundere", "What do you think of the moon?")
# Response Example: I mean, it’s not like the moon is super romantic or anything, right?

The anime characters supported are as follows:

tsundere (傲娇)

yandere (病娇)

himedere (公主娇)

genki (元气)

moe (萌系)

bakadere (笨蛋)

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