Instructions to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6", dtype="auto") - llama-cpp-python
How to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6", filename="Tifa-DeepsexV3-14b-Chat-NoCot-0626-Q6.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-DeepsexV3-14b-GGUF-Q6 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-DeepsexV3-14b-GGUF-Q6 # Run inference directly in the terminal: llama-cli -hf ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 # Run inference directly in the terminal: llama-cli -hf ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
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-DeepsexV3-14b-GGUF-Q6 # Run inference directly in the terminal: ./llama-cli -hf ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
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-DeepsexV3-14b-GGUF-Q6 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
Use Docker
docker model run hf.co/ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
- LM Studio
- Jan
- Ollama
How to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 with Ollama:
ollama run hf.co/ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
- Unsloth Studio
How to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 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-DeepsexV3-14b-GGUF-Q6 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-DeepsexV3-14b-GGUF-Q6 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-DeepsexV3-14b-GGUF-Q6 to start chatting
- Pi
How to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 with Docker Model Runner:
docker model run hf.co/ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
- Lemonade
How to use ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6
Run and chat with the model
lemonade run user.Tifa-DeepsexV3-14b-GGUF-Q6-{{QUANT_TAG}}List all available models
lemonade list
Tifa-DeepSexV3-14b
- 在线试用/Онлайновая пробная версия:WebUI
本模型基于Qwen3 14b-base进行深度优化,模型还在迭代中,目前最新为0701版本。可使用官网测试连接测试(一定要选择开源测试)
本版本特点
长文优化、单次输出可超过5000字。 超长关联,细微场景伏笔可在几千字后收回。 控制器支持,可使用控制器精确控制输出字数、风格、段落格式。 负面词汇避免,可设置不想看到的词,避免输出。
致谢
- Qwen系列模型提供的强大基座
- Deepseek团队提供的研究思路
- LeftNorth团队提供的技术支持
- Tifa角色扮演模型的创新架构
- HuggingFace社区的量化工具支持
license: apache-2.0
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We're not able to determine the quantization variants.

# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ValueFX9507/Tifa-DeepsexV3-14b-GGUF-Q6", dtype="auto")