Text Generation
GGUF
llama.cpp
lmstudio
reasoning
chain-of-thought
qwen
qwen3.6
Mixture of Experts
distillation
conversational
Instructions to use lordx64/Qwable-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lordx64/Qwable-v1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lordx64/Qwable-v1-GGUF", filename="Qwable-v1.IQ4_XS.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 lordx64/Qwable-v1-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 lordx64/Qwable-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf lordx64/Qwable-v1-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 lordx64/Qwable-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf lordx64/Qwable-v1-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 lordx64/Qwable-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf lordx64/Qwable-v1-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 lordx64/Qwable-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lordx64/Qwable-v1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/lordx64/Qwable-v1-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use lordx64/Qwable-v1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lordx64/Qwable-v1-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": "lordx64/Qwable-v1-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lordx64/Qwable-v1-GGUF:Q4_K_M
- Ollama
How to use lordx64/Qwable-v1-GGUF with Ollama:
ollama run hf.co/lordx64/Qwable-v1-GGUF:Q4_K_M
- Unsloth Studio
How to use lordx64/Qwable-v1-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 lordx64/Qwable-v1-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 lordx64/Qwable-v1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lordx64/Qwable-v1-GGUF to start chatting
- Pi
How to use lordx64/Qwable-v1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lordx64/Qwable-v1-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": "lordx64/Qwable-v1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use lordx64/Qwable-v1-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 lordx64/Qwable-v1-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 lordx64/Qwable-v1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use lordx64/Qwable-v1-GGUF with Docker Model Runner:
docker model run hf.co/lordx64/Qwable-v1-GGUF:Q4_K_M
- Lemonade
How to use lordx64/Qwable-v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lordx64/Qwable-v1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwable-v1-GGUF-Q4_K_M
List all available models
lemonade list
| base_model: lordx64/Qwable-v1 | |
| library_name: gguf | |
| pipeline_tag: text-generation | |
| tags: | |
| - gguf | |
| - llama.cpp | |
| - lmstudio | |
| - reasoning | |
| - chain-of-thought | |
| - qwen | |
| - qwen3.6 | |
| - moe | |
| - distillation | |
| quantized_by: lordx64 | |
| license: apache-2.0 | |
| # Qwable-v1-GGUF | |
| GGUF quantizations of [`lordx64/Qwable-v1`](https://huggingface.co/lordx64/Qwable-v1) for | |
| use with [llama.cpp](https://github.com/ggerganov/llama.cpp) and | |
| [LM Studio](https://lmstudio.ai/). | |
| The base model is a reasoning-distilled variant of Qwen3.6-35B-A3B fine-tuned | |
| to imitate the chain-of-thought style of Claude Opus 4.7. It thinks in explicit | |
| `<think>...</think>` blocks before producing the final answer. | |
| ## Quant files | |
| See the file list for all available quant levels. Common choices: | |
| | File | Quant | Approx size | Use case | | |
| |---|---|---|---| | |
| | `*.IQ4_XS.gguf` | IQ4_XS | ~18 GB | Smallest quant with good quality — default pick for LM Studio | | |
| | `*.Q4_K_M.gguf` | Q4_K_M | ~21 GB | Balanced quality / size | | |
| | `*.Q5_K_M.gguf` | Q5_K_M | ~25 GB | Higher quality | | |
| | `*.Q8_0.gguf` | Q8_0 | ~35 GB | Near-lossless | | |
| ## Running in llama.cpp | |
| ```bash | |
| llama-server \ | |
| -m Qwable-v1.IQ4_XS.gguf \ | |
| --host 127.0.0.1 --port 18081 \ | |
| -c 32768 -fa on \ | |
| --cache-type-k q8_0 --cache-type-v turbo4 | |
| ``` | |
| ## Running in LM Studio | |
| Search for `lordx64/Qwable-v1-GGUF` inside LM Studio's model browser and pick the quant | |
| that fits your RAM/VRAM. The model should appear automatically once HF indexes | |
| this repo. | |
| ## License | |
| Apache 2.0, inherited from the base model. See | |
| [`lordx64/Qwable-v1`](https://huggingface.co/lordx64/Qwable-v1) for training details, | |
| evaluations, and intended use. | |