Instructions to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF", dtype="auto") - llama-cpp-python
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF", filename="Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-BF16.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 llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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 llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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 llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M
Use Docker
docker model run hf.co/llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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": "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M
- SGLang
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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 "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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": "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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 "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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": "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with Ollama:
ollama run hf.co/llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M
- Unsloth Studio
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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 llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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 llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF to start chatting
- Pi
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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": "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-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 llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with Docker Model Runner:
docker model run hf.co/llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M
- Lemonade
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic-GGUF-Q4_K_M
List all available models
lemonade list
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| <title>Qwythos evaluation results vs. base Qwen3.5-9B</title> | |
| <desc>Grouped horizontal bar chart. gsm8k flex 0.670 to 0.860 (+0.19); gsm8k strict 0.510 to 0.810 (+0.30); gpqa_diamond flex 0.630 to 0.580 (-0.05); gpqa_diamond strict 0.050 to 0.010 (-0.04); mmlu 0.232 to 0.575 (+0.34); arc_challenge acc 0.470 to 0.490 (+0.02); arc_challenge acc_norm 0.400 to 0.410 (+0.01).</desc> | |
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| <text x="835" y="477" fill="#6f9a7e">ENORMOUS</text> | |
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| <text x="40" y="656" font-family="ui-monospace, 'SF Mono', Menlo, monospace" font-size="11" fill="#55534e">plate · qwythos eval-v1 · 7 tasks · accuracy 0–1</text> | |
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