--- title: "mai_replier-9b-v3" kind: "Kit" category: "Companions" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9ff023cb-3596-4e1a-a1f5-e060ed40171d" updated: "2026-11-12" --- # mai\_replier-9b-v3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Plays a persona in Chinese QQ group chats by reading the chat history and writing one short, casual reply with no visible reasoning. A merged 9B fine-tune of Qwen3.5-9B that also reads inline images. mai\_replier-9b-v3 is a kit in the Companions category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by tcmofashi ## About ### What it is mai\_replier-9b-v3 is the replier model of the MaiBot ecosystem, a bot that joins QQ group chats in a fixed character. It reads the group chat history and writes one short, casual reply in character, with no chain of thought. It is a full merged 9B model fine-tuned from Qwen3.5-9B with LoRA, and it can read images placed inline in the chat. ### Who it suits People who run MaiBot-style chat bots in Chinese group chats and want a self-hosted model that replies in persona. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Community | Group admins | Run a persona bot in a QQ group | A character that chats with members | | Software | Bot developers | Self-host the replier behind MaiBot | Serve it with vLLM for the bot | | Research | Dialogue researchers | Study persona-conditioned group chat replies | Test how it handles images in the chat | If you want a general assistant or an English chat model, you can skip it. ### Where to use it - QQ group chats, through the MaiBot ecosystem - Your own computer with Python and transformers, on Windows, macOS or Linux - A vLLM server, for hosting it behind a bot | Use case | Who | What they get back | | --- | --- | --- | | Persona replies | Bot developers | One short, casual line in character | | Chats with images | Group admins | Replies that take inline images into account | | Self-hosted serving | Platform teams | An endpoint for the bot to call | ### How to set it up - Python with pip - About 18.8 GB of disk space for the BF16 weights, or about 4.7 GB at 4-bit (an estimate) - A GPU with enough memory, since the card's snippet uses device\_map auto 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load them with transformers, or start a vLLM server. ```sh pip install -U huggingface_hub hf download tcmofashi/mai_replier-9b-v3 ``` ### How to use it Build two messages. The system message holds the character's name and persona. The user message holds the chat history lines and a closing prompt asking the character to reply. Always pass enable\_thinking as False. The sampling settings sit in the generate call below. ```python from transformers import AutoModelForImageTextToText, AutoProcessor import torch MODEL = "tcmofashi/mai_replier_9b_v3" processor = AutoProcessor.from_pretrained(MODEL) model = AutoModelForImageTextToText.from_pretrained(MODEL, dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "system", "content": "你的名字是小千。是一个大二在读女大学生…"}, {"role": "user", "content": "你正在qq群里聊天…\n现在，你说："}, ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, chat_template_kwargs={"enable_thinking": False}, ) inputs = processor(text=text, return_tensors="pt").to(model.device) out = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.95, repetition_penalty=1.15, do_sample=True, eos_token_id=[248046, 248044]) print(processor.tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)) ``` To serve it with vLLM, the card gives this command. ```bash vllm serve tcmofashi/mai_replier_9b_v3 \ --served-model-name mai_replier_9b_v3 \ --max-model-len 8192 \ --gpu-memory-utilization 0.90 \ --port 8200 ``` ### Example You give the model a persona and a short chat history where someone asks the character a question. It returns one casual sentence in that character's voice. This is an illustration of the input and output the card describes. ### For newcomers and experts If you are new, know that this model is built for one job, replying inside a chat in character. Start by copying the card's snippet and changing the persona text. For experts, the card lists Qwen3.5-9B with 24 linear-attention GDN layers and 8 full-attention layers, LoRA SFT with r=64 and alpha=32, merged into full weights. Images are embedded where they appear in the text. The latest 3 keep a 640 long edge and older ones shrink to 128. Chat history becomes a system and user message → Images are embedded at their place in the text → The model skips reasoning and replies → One short in-character line comes out ### Good to know The card calls this a community experiment for QQ group role play. - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need a GPU for a 9B model. - Data it reaches: downloading reaches Hugging Face. The model only reads the chat text and images you pass in. - Not for: speaking for real people. The card says outputs do not represent any real person's views, and its prompt tells the model to avoid illegal, sexual, violent and political content. ## README ````markdown # mai_replier-9b-v3 Plays a persona in Chinese QQ group chats by reading the chat history and writing one short, casual reply with no visible reasoning. A merged 9B fine-tune of Qwen3.5-9B that also reads inline images. Made by tcmofashi on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: mai_replier-9b-v3 is the replier model of the MaiBot ecosystem, a bot that joins QQ group chats in a fixed character. It reads the group chat history and writes one short, casual reply in character, with no chain of thought. It is a full merged 9B model fine-tuned from Qwen3.5-9B with LoRA, and it can read images placed inline in the chat. Who it suits: People who run MaiBot-style chat bots in Chinese group chats and want a self-hosted model that replies in persona. ```table Industry | Who uses it | How they use it | Example Community | Group admins | Run a persona bot in a QQ group | A character that chats with members Software | Bot developers | Self-host the replier behind MaiBot | Serve it with vLLM for the bot Research | Dialogue researchers | Study persona-conditioned group chat replies | Test how it handles images in the chat ``` If you want a general assistant or an English chat model, you can skip it. Where to use it: - QQ group chats, through the MaiBot ecosystem - Your own computer with Python and transformers, on Windows, macOS or Linux - A vLLM server, for hosting it behind a bot ```table Use case | Who | What they get back Persona replies | Bot developers | One short, casual line in character Chats with images | Group admins | Replies that take inline images into account Self-hosted serving | Platform teams | An endpoint for the bot to call ``` How to set it up: - Python with pip - About 18.8 GB of disk space for the BF16 weights, or about 4.7 GB at 4-bit (an estimate) - A GPU with enough memory, since the card's snippet uses device_map auto 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load them with transformers, or start a vLLM server. ```sh pip install -U huggingface_hub hf download tcmofashi/mai_replier-9b-v3 ``` How to use it: Build two messages. The system message holds the character's name and persona. The user message holds the chat history lines and a closing prompt asking the character to reply. Always pass enable_thinking as False. The sampling settings sit in the generate call below. ```python from transformers import AutoModelForImageTextToText, AutoProcessor import torch MODEL = "tcmofashi/mai_replier_9b_v3" processor = AutoProcessor.from_pretrained(MODEL) model = AutoModelForImageTextToText.from_pretrained(MODEL, dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "system", "content": "你的名字是小千。是一个大二在读女大学生…"}, {"role": "user", "content": "你正在qq群里聊天…\n现在，你说："}, ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, chat_template_kwargs={"enable_thinking": False}, ) inputs = processor(text=text, return_tensors="pt").to(model.device) out = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.95, repetition_penalty=1.15, do_sample=True, eos_token_id=[248046, 248044]) print(processor.tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)) ``` To serve it with vLLM, the card gives this command. ```bash vllm serve tcmofashi/mai_replier_9b_v3 \ --served-model-name mai_replier_9b_v3 \ --max-model-len 8192 \ --gpu-memory-utilization 0.90 \ --port 8200 ``` Example: You give the model a persona and a short chat history where someone asks the character a question. It returns one casual sentence in that character's voice. This is an illustration of the input and output the card describes. For newcomers and experts: If you are new, know that this model is built for one job, replying inside a chat in character. Start by copying the card's snippet and changing the persona text. For experts, the card lists Qwen3.5-9B with 24 linear-attention GDN layers and 8 full-attention layers, LoRA SFT with r=64 and alpha=32, merged into full weights. Images are embedded where they appear in the text. The latest 3 keep a 640 long edge and older ones shrink to 128. ```flow Chat history becomes a system and user message Images are embedded at their place in the text The model skips reasoning and replies One short in-character line comes out ``` Good to know: The card calls this a community experiment for QQ group role play. - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need a GPU for a 9B model. - Data it reaches: downloading reaches Hugging Face. The model only reads the chat text and images you pass in. - Not for: speaking for real people. The card says outputs do not represent any real person's views, and its prompt tells the model to avoid illegal, sexual, violent and political content. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 9.4B - Weights: about 18.8 GB in BF16; about 4.7 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 660 - Likes: 8 - License: apache-2.0 - Languages: zh - Based on: Qwen/Qwen3.5-9B - Quantized versions: 3 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, text-generation, chatbot, qq-group-chat, roleplay, chinese, retro-replyer, multimodal, interleaved-image ## Links - Model: https://huggingface.co/tcmofashi/mai_replier-9b-v3 - Files: https://huggingface.co/tcmofashi/mai_replier-9b-v3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download tcmofashi/mai_replier-9b-v3 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [tcmofashi/mai_replier-9b-v3 on Hugging Face](https://huggingface.co/tcmofashi/mai_replier-9b-v3) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | mai\_replier-9b-v3 (this page) | 660 | 8 | 9.4B | apache-2.0 | 2026-11-06 | | [Crystal-31B-RL](https://mindket.com/p/99a094d7-2586-4463-a832-d231301c170f) | 295 | 9 | 31.3B | apache-2.0 | 2026-10-19 | | [Tallow](https://mindket.com/p/56a4cf69-5e12-4fe9-81ce-c7b88ca883cb) | 681 | 8 | 15.7M | apache-2.0 | 2026-11-07 | | [BeeLLM](https://mindket.com/p/98b73fad-0b53-444e-b033-c043ce93b1ec) | 1,850 | 46 | 4.4M | apache-2.0 | 2026-11-11 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-06 - First published: 2026-11-06 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open tcmofashi/mai_replier-9b-v3 on Hugging Face](https://huggingface.co/tcmofashi/mai_replier-9b-v3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9ff023cb-3596-4e1a-a1f5-e060ed40171d) - [Its page on Mindket](https://mindket.com/p/9ff023cb-3596-4e1a-a1f5-e060ed40171d)