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# ollama modelfile
FROM ./Qwen3-Sex-Q8_0.gguf
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
<|im_start|>assistant
{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
{{ end }}{{ end }}"""
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER repeat_penalty 1.08
PARAMETER num_predict 2048
PARAMETER num_ctx 4096

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size 8051283328

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---
license: apache-2.0
language:
- zh
- en
base_model:
- Qwen/Qwen3-4B-Instruct-2507
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3
- chat
- sft
- nsfw
- not-for-all-audiences
datasets:
- ystemsrx/Erotic_Literature_Collection
- ystemsrx/Bad_Data_Alpaca
---
<div align="center">
# Qwen3-Sex
<p>
<a href="https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507"><img alt="Base Model" src="https://img.shields.io/badge/Base-Qwen3--4B--Instruct--2507-1f6feb"></a>
<a href="https://www.apache.org/licenses/LICENSE-2.0"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue.svg"></a>
<img alt="Dtype" src="https://img.shields.io/badge/dtype-bfloat16-8957e5">
<img alt="Context" src="https://img.shields.io/badge/context-262K-2ea44f">
<img alt="Audience" src="https://img.shields.io/badge/Audience-18%2B-red">
<img alt="Not for all audiences" src="https://img.shields.io/badge/⚠️-NSFW-c00">
</p>
<p>
<a href="./README.md">简体中文</a> | <strong>English</strong>
</p>
</div>
A conversational model fine-tuned from **Qwen3-4B-Instruct-2507**.
> [!WARNING]
> This model is intended for **adult users (18+) only**. It may generate adult, explicit, or otherwise NSFW content. Please read the [Disclaimer](#disclaimer) before using it.
---
## Model Overview
| Item | Value |
| ------------------- | -------------------------------------------------------------------------------- |
| **Base model** | [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) |
| **Dtype** | bfloat16 |
| **Context length** | native 262,144 (4K–32K recommended for everyday chat) |
| **Chat template** | ChatML (`<|im_start|>` / `<|im_end|>`) |
## Repository Contents
| File | Description |
| ---------------------------------------------------- | --------------------------------------------------------------------- |
| `model-*.safetensors` | Full weights (bfloat16) |
| `config.json` / `tokenizer*` / `chat_template.jinja` | Model & tokenizer configuration |
| `generation_config.json` | **Recommended inference parameters (keep defaults for best results)** |
| `infer.py` | Ready-to-use multi-turn CLI chat script |
| `Modelfile` | Modelfile for importing into Ollama |
| `Qwen3-Sex-BF16.gguf` | Unquantized GGUF (BF16, ~8 GB) |
| `Qwen3-Sex-Q8_0.gguf` | Q8_0 quantized GGUF (~4.3 GB) |
## Recommended Inference Settings
> [!IMPORTANT]
> The values in `generation_config.json` are tuned for this model and **should be kept as the defaults for optimal quality**.
```json
{
"do_sample": true,
"temperature": 0.7,
"top_p": 0.8,
"repetition_penalty": 1.08,
"max_new_tokens": 2048,
"eos_token_id": [151645, 151643],
"pad_token_id": 151643,
"bos_token_id": 151643
}
```
---
## Quick Start
### Option 1: Run `infer.py` directly (recommended)
The repo ships with an interactive CLI script that loads the model and applies the parameters from `generation_config.json` automatically:
```bash
# Set up a virtual environment (uv is recommended)
uv venv
source .venv/bin/activate
uv pip install torch transformers accelerate
# Start chatting
python infer.py
```
> [!TIP]
> Type `clear` to reset the multi-turn history; type `exit` or `quit` to leave.
### Option 2: Minimal inference code
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
MODEL_NAME = "ystemsrx/Qwen3-Sex"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model.eval()
# Load the recommended generation config (keeping defaults gives the best quality)
gen_config = GenerationConfig.from_pretrained(MODEL_NAME)
messages = [
{"role": "user", "content": "Hello, please introduce yourself briefly."},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to(model.device)
with torch.inference_mode():
output_ids = model.generate(**inputs, generation_config=gen_config)
response = tokenizer.decode(
output_ids[0][inputs["input_ids"].shape[-1]:],
skip_special_tokens=True,
)
print(response)
```
### Option 3: Use the GGUF files in Ollama
GGUF builds (`Qwen3-Sex-BF16.gguf` and `Qwen3-Sex-Q8_0.gguf`) are included so you can load the model directly with Ollama:
```bash
# Build the Ollama model from the bundled Modelfile
ollama create Qwen3-Sex -f Modelfile
# Start chatting
ollama run Qwen3-Sex
```
> [!CAUTION]
> GGUF builds — especially the Q8_0 quantized one — produce **lower output quality** than the original safetensors weights. Fine details, long-context coherence, and generation stability may degrade. If quality is critical, prefer Option 1 or Option 2.
---
## Disclaimer
> [!CAUTION]
> Downloading or using this model constitutes your acknowledgement and acceptance of all terms below. **If you do not agree, stop downloading and using the model immediately.**
<details open>
<summary><strong>Click to fold / unfold the full notice</strong></summary>
1. **Adult content warning**: This model has been fine-tuned on adult-oriented data and may produce explicit, sensitive, or NSFW text. **Anyone under the age of 18 (or under the age of majority in their jurisdiction) is strictly prohibited from accessing, downloading, or using this model.**
2. **Permitted use**: The model is provided solely for academic research, personal non-commercial use, and fictional creative writing. Users must comply with all applicable laws and regulations in their jurisdiction. **It must not be used to generate illegal content, content that infringes the rights of others, sexual content involving minors, non-consensual sexual content involving real persons, or any other content that violates public order or morality.**
3. **No liability**: The author(s) of this model accept **no responsibility** for any direct or indirect consequences arising from its use, including but not limited to legal liability, emotional or reputational harm, or financial loss. Generated content does not represent the views or positions of the author(s).
4. **Third-party platforms**: When deploying or using this model on any third-party platform, service, or application, you must additionally comply with that platform's terms of service and content policies.
5. **Use at your own risk**: The model may produce factually incorrect, biased, or harmful output. Users are responsible for evaluating and accepting these risks.
6. **Acceptance**: Downloading or using this model constitutes your acknowledgement and acceptance of all terms above. If you do not agree, stop downloading and using the model immediately.
</details>
## License
This model is released under the [Apache License 2.0](LICENSE), and inherits the relevant license terms of the base model Qwen/Qwen3-4B-Instruct-2507.

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---
license: apache-2.0
language:
- zh
- en
base_model:
- Qwen/Qwen3-4B-Instruct-2507
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3
- chat
- sft
- nsfw
- not-for-all-audiences
datasets:
- ystemsrx/Erotic_Literature_Collection
- ystemsrx/Bad_Data_Alpaca
---
<div align="center">
# Qwen3-Sex
<p>
<a href="https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507"><img alt="Base Model" src="https://img.shields.io/badge/Base-Qwen3--4B--Instruct--2507-1f6feb"></a>
<a href="https://www.apache.org/licenses/LICENSE-2.0"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue.svg"></a>
<img alt="Dtype" src="https://img.shields.io/badge/dtype-bfloat16-8957e5">
<img alt="Context" src="https://img.shields.io/badge/context-262K-2ea44f">
<img alt="Audience" src="https://img.shields.io/badge/Audience-18%2B-red">
<img alt="Not for all audiences" src="https://img.shields.io/badge/⚠️-NSFW-c00">
</p>
<p>
<strong>简体中文</strong> | <a href="./README.en.md">English</a>
</p>
</div>
基于 **Qwen3-4B-Instruct-2507** 进行 SFT 后得到的对话模型。
> [!WARNING]
> 本模型面向 **18 岁以上成年用户**，可能输出包含成人内容、露骨描写等不适合所有受众的文本。请在使用前阅读下方[免责声明](#免责声明)。
---
## 模型简介
| 项目 | 内容 |
| ----------------- | --------------------------------------------------- |
| **基础模型** | [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) |
| **数据类型** | bfloat16 |
| **上下文长度** | 原生支持 262,144（建议日常对话使用 4K–32K） |
| **对话模板** | ChatML（`<|im_start|>` / `<|im_end|>`） |
## 仓库内容
| 文件 | 说明 |
| ---------------------------------------------------- | -------------------------------------------- |
| `model-*.safetensors` | 完整权重（bfloat16） |
| `config.json` / `tokenizer*` / `chat_template.jinja` | 模型与分词器配置 |
| `generation_config.json` | **推荐推理参数（请保持默认以获得最佳效果）** |
| `infer.py` | 开箱即用的命令行多轮对话脚本 |
| `Modelfile` | Ollama 导入用的 Modelfile |
| `Qwen3-Sex-BF16.gguf` | 未量化 GGUF（BF16，约 8 GB） |
| `Qwen3-Sex-Q8_0.gguf` | Q8_0 量化 GGUF（约 4.3 GB） |
## 推荐推理配置
> [!IMPORTANT]
> `generation_config.json` 中的参数是经过调优后推荐使用的配置，**强烈建议保持默认**。
```json
{
"do_sample": true,
"temperature": 0.7,
"top_p": 0.8,
"repetition_penalty": 1.08,
"max_new_tokens": 2048,
"eos_token_id": [151645, 151643],
"pad_token_id": 151643,
"bos_token_id": 151643
}
```
---
## 快速使用
### 方式一：直接运行 `infer.py`（推荐）
仓库内已附带交互式命令行脚本，自动加载模型并按 `generation_config.json` 中的参数推理，开箱即用：
```bash
# 安装依赖（建议使用 uv 创建虚拟环境）
uv venv
source .venv/bin/activate
uv pip install torch transformers accelerate
# 启动对话
python infer.py
```
> [!TIP]
> 输入 `clear` 清空多轮对话历史，输入 `exit` 或 `quit` 退出。
### 方式二：最小化推理代码
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
MODEL_NAME = "ystemsrx/Qwen3-Sex"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model.eval()
# 加载推荐推理配置（保持默认即可获得最佳效果）
gen_config = GenerationConfig.from_pretrained(MODEL_NAME)
messages = [
{"role": "user", "content": "你好，简单介绍一下你自己。"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to(model.device)
with torch.inference_mode():
output_ids = model.generate(**inputs, generation_config=gen_config)
response = tokenizer.decode(
output_ids[0][inputs["input_ids"].shape[-1]:],
skip_special_tokens=True,
)
print(response)
```
### 方式三：导入 Ollama 使用 GGUF
仓库中提供了 GGUF 格式模型文件（`Qwen3-Sex-BF16.gguf` 和 `Qwen3-Sex-Q8_0.gguf`），可直接通过 Ollama 加载：
```bash
# 使用仓库内的 Modelfile 创建模型
ollama create Qwen3-Sex -f Modelfile
# 启动对话
ollama run Qwen3-Sex
```
> [!CAUTION]
> GGUF 格式（尤其是 Q8_0 量化版）相比原始 safetensors 权重，**推理质量会有所下降**，部分细节、长上下文连贯性以及生成的稳定性可能不如直接使用 `infer.py`。如果对生成质量有较高要求，请优先使用方式一或方式二。
---
## 免责声明
> [!CAUTION]
> 下载或使用本模型即表示您已阅读、理解并同意以下全部条款。**如不同意，请立即停止下载和使用。**
<details open>
<summary><strong>点击折叠 / 展开完整声明</strong></summary>
1. **成人内容警告**：本模型在含有成人导向内容的语料上进行了微调，可能生成包含露骨、敏感或不适合工作场所（NSFW）的文本。**严禁未满 18 周岁的用户访问、下载或使用本模型。**
2. **使用范围**：本模型仅供学术研究、个人非商业用途及虚构创作参考。使用者必须遵守所在国家或地区的法律法规，**不得将本模型用于任何违法、侵犯他人权益、传播未成年人不当内容、生成真实人物不雅信息或其他违反公序良俗的目的。**
3. **责任归属**：模型作者对使用本模型产生的任何直接或间接后果（包括但不限于法律责任、精神损害、财产损失等）**不承担任何责任**。模型生成的内容不代表作者的立场或观点。
4. **平台合规**：在任何第三方平台、服务或应用中使用本模型时，请同时遵守该平台的使用条款及内容政策。
5. **风险自负**：模型可能产生事实性错误、偏见性表达或有害内容，使用者应自行甄别并承担使用风险。
6. **下载即视为同意**：下载或使用本模型即表示您已阅读、理解并同意以上全部条款。如不同意，请立即停止下载和使用。
</details>
## 许可证
本模型遵循 [Apache License 2.0](LICENSE) 协议，并继承基础模型 Qwen/Qwen3-4B-Instruct-2507 的相关许可条款。

61
chat_template.jinja Normal file
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

71
config.json Normal file
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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9728,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 262144,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 5000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.2.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

11
generation_config.json Normal file
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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [151645, 151643],
"pad_token_id": 151643,
"max_new_tokens": 2048,
"temperature": 0.7,
"top_p": 0.8,
"repetition_penalty": 1.08,
"transformers_version": "5.2.0"
}

190
infer.py Normal file
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import signal
import torch
from threading import Thread, Event
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
StoppingCriteria,
StoppingCriteriaList,
TextIteratorStreamer,
)
from pathlib import Path
MODEL_PATH = Path(__file__).resolve().parent
DEFAULT_SYSTEM = ""
class StopOnEvent(StoppingCriteria):
def __init__(self, event):
self.event = event
def __call__(self, input_ids, scores, **kwargs):
return self.event.is_set()
def load_model():
print(f"Loading model from: {MODEL_PATH}")
tokenizer = AutoTokenizer.from_pretrained(
MODEL_PATH,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
device_map="auto",
trust_remote_code=True,
)
model.eval()
gen_config = GenerationConfig.from_pretrained(
MODEL_PATH,
trust_remote_code=True,
)
print(f"Loaded generation config:\n{gen_config}")
return tokenizer, model, gen_config
def build_messages(history, user_input, system=DEFAULT_SYSTEM):
messages = []
if system:
messages.append({"role": "system", "content": system})
for u, a in history:
messages.append({"role": "user", "content": u})
messages.append({"role": "assistant", "content": a})
messages.append({"role": "user", "content": user_input})
return messages
def join_thread(thread, stop_event):
stop_event.set()
# 不再 break: 确保 daemon 线程真正结束, 避免 shutdown 阶段残留线程
while thread.is_alive():
try:
thread.join(timeout=0.1)
except KeyboardInterrupt:
stop_event.set()
@torch.inference_mode()
def stream_chat(tokenizer, model, gen_config, history, user_input):
messages = build_messages(history, user_input)
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to(model.device)
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=True,
skip_special_tokens=True,
)
stop_event = Event()
stopping_criteria = StoppingCriteriaList([StopOnEvent(stop_event)])
generate_kwargs = dict(
**inputs,
streamer=streamer,
generation_config=gen_config,
stopping_criteria=stopping_criteria,
)
thread = Thread(
target=model.generate,
kwargs=generate_kwargs,
daemon=True,
)
thread.start()
response = ""
interrupted = False
try:
for new_text in streamer:
print(new_text, end="", flush=True)
response += new_text
except KeyboardInterrupt:
interrupted = True
stop_event.set()
print("\n[Interrupted. Type exit to quit or continue chatting.]")
finally:
join_thread(thread, stop_event)
if not interrupted:
print()
return response, interrupted
def main():
try:
tokenizer, model, gen_config = load_model()
except KeyboardInterrupt:
print("\nBye.")
return
print("\n=== Qwen3-Sex Chat ===")
print("Type 'exit' / 'quit' to quit, or type 'clear' to clear history.")
history = []
while True:
try:
user_input = input("User: ").strip()
except (EOFError, KeyboardInterrupt):
print("\nBye.")
break
if not user_input:
continue
if user_input.lower() in {"exit", "quit"}:
print("Bye.")
break
if user_input.lower() == "clear":
history = []
print("[History cleared]\n")
continue
print("Assistant: ", end="", flush=True)
response, interrupted = stream_chat(
tokenizer,
model,
gen_config,
history,
user_input,
)
if response.strip() and not interrupted:
history.append((user_input, response))
if __name__ == "__main__":
try:
main()
finally:
while True:
try:
signal.signal(signal.SIGINT, signal.SIG_IGN)
break
except KeyboardInterrupt:
continue
except (ValueError, OSError):
break

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406
model.safetensors.index.json Normal file
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