Instructions to use soob3123/Veiled-Rose-22B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use soob3123/Veiled-Rose-22B-gguf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="soob3123/Veiled-Rose-22B-gguf") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("soob3123/Veiled-Rose-22B-gguf", dtype="auto") - llama-cpp-python
How to use soob3123/Veiled-Rose-22B-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="soob3123/Veiled-Rose-22B-gguf", filename="Veiled-Rose-22B-Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use soob3123/Veiled-Rose-22B-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf soob3123/Veiled-Rose-22B-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf soob3123/Veiled-Rose-22B-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf soob3123/Veiled-Rose-22B-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf soob3123/Veiled-Rose-22B-gguf:Q4_K_M
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 soob3123/Veiled-Rose-22B-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf soob3123/Veiled-Rose-22B-gguf:Q4_K_M
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 soob3123/Veiled-Rose-22B-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf soob3123/Veiled-Rose-22B-gguf:Q4_K_M
Use Docker
docker model run hf.co/soob3123/Veiled-Rose-22B-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use soob3123/Veiled-Rose-22B-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "soob3123/Veiled-Rose-22B-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "soob3123/Veiled-Rose-22B-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/soob3123/Veiled-Rose-22B-gguf:Q4_K_M
- SGLang
How to use soob3123/Veiled-Rose-22B-gguf with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "soob3123/Veiled-Rose-22B-gguf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "soob3123/Veiled-Rose-22B-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "soob3123/Veiled-Rose-22B-gguf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "soob3123/Veiled-Rose-22B-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use soob3123/Veiled-Rose-22B-gguf with Ollama:
ollama run hf.co/soob3123/Veiled-Rose-22B-gguf:Q4_K_M
- Unsloth Studio
How to use soob3123/Veiled-Rose-22B-gguf 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 soob3123/Veiled-Rose-22B-gguf 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 soob3123/Veiled-Rose-22B-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for soob3123/Veiled-Rose-22B-gguf to start chatting
- Pi
How to use soob3123/Veiled-Rose-22B-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf soob3123/Veiled-Rose-22B-gguf:Q4_K_M
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": "soob3123/Veiled-Rose-22B-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use soob3123/Veiled-Rose-22B-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf soob3123/Veiled-Rose-22B-gguf:Q4_K_M
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 soob3123/Veiled-Rose-22B-gguf:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use soob3123/Veiled-Rose-22B-gguf with Docker Model Runner:
docker model run hf.co/soob3123/Veiled-Rose-22B-gguf:Q4_K_M
- Lemonade
How to use soob3123/Veiled-Rose-22B-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull soob3123/Veiled-Rose-22B-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Veiled-Rose-22B-gguf-Q4_K_M
List all available models
lemonade list
โง Veiled Rose โง
Mystery is at the heart of creativity. That, and surprise...As creative channels, we need to trust the darkness.
Veiled Rose emerges under the moon's knowing glow, an enigmatic presence designed for immersive narrative experiences. This 22B parameter model possesses significant reasoning capabilities and cognitive depth, ideal for weaving intricate roleplay scenarios. Veiled Rose excels at crafting richly atmospheric storytelling where nuanced character interactions and complex mysteries unfold with coherence and perception. Expect narratives shrouded in secrets, driven by sophisticated motivations, and unveiled through compelling, intelligent storytelling.
โ Features โ
- โก Deeper Atmospheric Immersion: Even richer, moon-drenched scenarios bloom, now infused with greater intellectual subtlety and environmental awareness.
- โก Enhanced Reasoning & Consistency: Characters maintain unwavering personas driven by more sophisticated internal logic and demonstrate sharper, more consistent reasoning throughout complex narratives.
- โก Intricate Narrative Mystery: Enigmatic storylines unfold with greater coherence, logical depth, and the capacity for more complex plot structures and surprising, yet earned, revelations.
- โก Profound Emotional & Cognitive Nuance: The unspoken, the veiled meanings, and the intricate web of character thoughts and motivations are rendered with heightened clarity and psychological depth.
โ Limitations โ
- Flourishes most in atmospheric, introspective, or character-driven scenarios requiring nuanced interaction.
- Due to its depth, may occasionally produce responses that are overly complex or dense for simpler contexts.
- Uncensored in Roleplay mode (e.g., SillyTavern), adheres to safety guidelines in Assistant mode (no system prompt).
- Use one of the Amoral models for a fully uncensored but potentially less nuanced experience.
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