Reinforcement Learning
Transformers
GGUF
Chinese
English
incremental-pretraining
sft
roleplay
cot
sex
conversational
Not-For-All-Audiences
Instructions to use ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4", dtype="auto") - llama-cpp-python
How to use ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4", filename="Tifa-Deepsex-14b-CoT-Chat-IQ4_NL.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL # Run inference directly in the terminal: llama-cli -hf ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL # Run inference directly in the terminal: llama-cli -hf ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL
Use Docker
docker model run hf.co/ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL
- LM Studio
- Jan
- Ollama
How to use ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 with Ollama:
ollama run hf.co/ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL
- Unsloth Studio
How to use ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 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 ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 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 ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 with Docker Model Runner:
docker model run hf.co/ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL
- Lemonade
How to use ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ValueFX9507/Tifa-Deepsex-14b-CoT-GGUF-Q4:IQ4_NL
Run and chat with the model
lemonade run user.Tifa-Deepsex-14b-CoT-GGUF-Q4-IQ4_NL
List all available models
lemonade list
Update README.md
Browse files
README.md
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---
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base_model:
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- deepseek-ai/deepseek-r1-14b
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language:
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- zh
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- en
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library_name: transformers
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tags:
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- incremental-pretraining
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- sft
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- reinforcement-learning
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- roleplay
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- cot
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- sex
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license: apache-2.0
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---
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# Tifa-Deepseek-14b-CoT
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- **HF Model**: [ValueFX9507/Tifa-Deepsex-14b-CoT](https://huggingface.co/ValueFX9507/Tifa-Deepsex-14b-CoT)
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- **GGUF**: [F16](https://huggingface.co/ValueFX9507/Tifa-Deepsex-14b-CoT)(更多量化版本持续更新中)
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- **Demo APK**: [点击下载](http://app.visionsic.com/download/projectchat.apk)
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本模型基于Deepseek-R1-14B进行深度优化,通过三重训练策略显著增强角色扮演、小说文本生成与思维链(CoT)能力。特别适合需要长程上下文关联的创作场景。
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## 示例(因COT模型特点,上下文不连贯时可以使用Demo软件中的故事模式)
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## 目标
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针对原版Deepseek-R1-14B在长文本生成连贯性不足和角色扮演能力薄弱的核心缺陷(主要由于训练数据中小说类语料占比过低),本模型通过多阶段优化提升其角色扮演能力。
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## 模型亮点
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🔥 **四阶段进化架构**:
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1. **增量预训练**:注入0.4T Token 小说,使用16k上下文训练,增强文本连贯性(70%爱情动作小说)
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2. **Tifa-SFT**:融合全球Top4角色扮演模型Tifa的10万条高质量数据
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3. **CoT恢复训练**:采用Deepseek-32B/685B数据重建推理能力
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4. **RL强化**:保留发散性思维标签的同时优化生成质量
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💡 **工程创新**:
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- 16k超长上下文训练
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- 随机截断训练增强鲁棒性
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- 8×H20 GPU全量微调
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## 模型详情
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| 属性 | 规格 |
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|-------|------|
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| 基础架构 | Deepseek-R1-14B |
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| 最大上下文 | 128k |
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| 训练数据 | 0.4T小说 + 10万条SFT + Deepseek混合数据 |
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| 训练设备 | 8×H20 GPU集群 |
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| 量化支持 | GGUF(全系列量化计划中) |
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## 使用场景
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✅ **推荐场景**:
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- 角色扮演对话
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- 需要发散性思维的创意写作
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- 复杂逻辑的思维链(CoT)推理
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- 基于上下文的深度角色交互
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❌ **局限场景**:
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- 数学计算与代码生成
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- 短文本即时问答
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- 需要严格事实性的场景
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## 注意事项
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⚠️ 本模型使用数据包含小说版权内容及Tifa模型衍生数据,请遵守:
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1. 禁止商用
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2. 角色扮演数据需遵循[Tifa使用协议](https://leftnorth.com/terms.html)
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3. 生成内容需符合当地法律法规
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## 💡 使用建议
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**最佳实践**:
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```python
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# 启用角色扮演模式
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prompt = """<system>进入Tifa角色引擎...</system>
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<user>你现在是流浪武士楚夜,正站在长安城屋顶上</user>
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<think>
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需要体现人物孤傲的气质
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加入武侠特有的环境描写
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保持对话的冷峻风格
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</think>
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<楚夜>"""
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```
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**参数推荐**:
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```python
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generation_config = {
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"temperature": 0.8,
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"top_p": 0.8,
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"repetition_penalty": 1.17,
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"max_new_tokens": 1536,
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"do_sample": True
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}
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```
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## 致谢
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- Deepseek系列模型提供的强大基座
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- Tifa角色扮演模型的创新架构
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- HuggingFace社区的量化工具支持
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---
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license: apache-2.0
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---
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