Instructions to use batiai/Qwen3.5-27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use batiai/Qwen3.5-27B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="batiai/Qwen3.5-27B-GGUF", filename="Qwen-Qwen3.5-27B-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 batiai/Qwen3.5-27B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf batiai/Qwen3.5-27B-GGUF:IQ4_XS # Run inference directly in the terminal: llama-cli -hf batiai/Qwen3.5-27B-GGUF:IQ4_XS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf batiai/Qwen3.5-27B-GGUF:IQ4_XS # Run inference directly in the terminal: llama-cli -hf batiai/Qwen3.5-27B-GGUF:IQ4_XS
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 batiai/Qwen3.5-27B-GGUF:IQ4_XS # Run inference directly in the terminal: ./llama-cli -hf batiai/Qwen3.5-27B-GGUF:IQ4_XS
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 batiai/Qwen3.5-27B-GGUF:IQ4_XS # Run inference directly in the terminal: ./build/bin/llama-cli -hf batiai/Qwen3.5-27B-GGUF:IQ4_XS
Use Docker
docker model run hf.co/batiai/Qwen3.5-27B-GGUF:IQ4_XS
- LM Studio
- Jan
- vLLM
How to use batiai/Qwen3.5-27B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "batiai/Qwen3.5-27B-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": "batiai/Qwen3.5-27B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/batiai/Qwen3.5-27B-GGUF:IQ4_XS
- Ollama
How to use batiai/Qwen3.5-27B-GGUF with Ollama:
ollama run hf.co/batiai/Qwen3.5-27B-GGUF:IQ4_XS
- Unsloth Studio
How to use batiai/Qwen3.5-27B-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 batiai/Qwen3.5-27B-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 batiai/Qwen3.5-27B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for batiai/Qwen3.5-27B-GGUF to start chatting
- Pi
How to use batiai/Qwen3.5-27B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf batiai/Qwen3.5-27B-GGUF:IQ4_XS
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": "batiai/Qwen3.5-27B-GGUF:IQ4_XS" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use batiai/Qwen3.5-27B-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 batiai/Qwen3.5-27B-GGUF:IQ4_XS
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 batiai/Qwen3.5-27B-GGUF:IQ4_XS
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use batiai/Qwen3.5-27B-GGUF with Docker Model Runner:
docker model run hf.co/batiai/Qwen3.5-27B-GGUF:IQ4_XS
- Lemonade
How to use batiai/Qwen3.5-27B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull batiai/Qwen3.5-27B-GGUF:IQ4_XS
Run and chat with the model
lemonade run user.Qwen3.5-27B-GGUF-IQ4_XS
List all available models
lemonade list
Qwen 3.5 27B GGUF โ Quantized by BatiAI
IQ4_XS quantization of Qwen/Qwen3.5-27B for on-device AI on Mac. Built and verified by BatiAI for BatiFlow.
Quick Start
ollama pull batiai/qwen3.5-27b:iq4
Available Quantizations
| Quant | Size | VRAM | M4 Max (128GB) | Recommended For |
|---|---|---|---|---|
| IQ4_XS | 14GB | 28GB | 17.0 t/s | 32GB+ Mac |
Benchmarks โ M4 Max (128GB)
| Metric | IQ4_XS |
|---|---|
| Token generation | 17.0 t/s |
| Korean | โ |
| Tool call JSON | โ |
| VRAM | 28 GB |
vs Other Qwen 3.5 Models
| Model | Size | VRAM | Speed | Min Mac |
|---|---|---|---|---|
| batiai/qwen3.5-9b:q4 | 5.2GB | ~8GB | 12.5 t/s | 16GB |
| batiai/qwen3.5-27b:iq4 | 14GB | 28GB | 17.0 t/s | 32GB |
| batiai/qwen3.5-35b:iq4 | 17GB | 23GB | 26.6 t/s | 36GB |
For 36GB+ Mac, consider batiai/qwen3.5-35b โ MoE architecture, faster and less VRAM.
Technical Details
- Original Model: Qwen/Qwen3.5-27B
- Architecture: Hybrid (Gated DeltaNet + GQA + MoE)
- Context Window: 262K tokens
- License: Apache 2.0
- Quantized with: llama.cpp (build 400ac8e)
About BatiFlow
BatiFlow โ free, on-device AI automation for Mac. 5MB app, 100% local, unlimited.
License
Quantized from Qwen/Qwen3.5-27B. License: Apache 2.0.
Benchmarks
| Machine | Quant | Cold start | Prompt eval | Token gen | Tested |
|---|---|---|---|---|---|
| MacBook Pro M4 Max 128GB | IQ4_XS | 4.827s | 82.37 t/s | 11.71 t/s | 2026-05-03 |
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Model tree for batiai/Qwen3.5-27B-GGUF
Base model
Qwen/Qwen3.5-27B