Instructions to use finis-est/qwen3.5-27b-aconite-v0-Q6_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use finis-est/qwen3.5-27b-aconite-v0-Q6_K with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="finis-est/qwen3.5-27b-aconite-v0-Q6_K",
	filename="qwen3.5-27b-aconite-v0-Q6_K.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 finis-est/qwen3.5-27b-aconite-v0-Q6_K with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K
# Run inference directly in the terminal:
llama-cli -hf finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K
# Run inference directly in the terminal:
llama-cli -hf finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

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 finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

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 finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

Use Docker

docker model run hf.co/finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

LM Studio
Jan
Ollama
How to use finis-est/qwen3.5-27b-aconite-v0-Q6_K with Ollama:
```
ollama run hf.co/finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K
```

Unsloth Studio

How to use finis-est/qwen3.5-27b-aconite-v0-Q6_K 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 finis-est/qwen3.5-27b-aconite-v0-Q6_K 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 finis-est/qwen3.5-27b-aconite-v0-Q6_K to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for finis-est/qwen3.5-27b-aconite-v0-Q6_K to start chatting

How to use finis-est/qwen3.5-27b-aconite-v0-Q6_K with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

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": "finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use finis-est/qwen3.5-27b-aconite-v0-Q6_K with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

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 finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use finis-est/qwen3.5-27b-aconite-v0-Q6_K with Docker Model Runner:
```
docker model run hf.co/finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K
```

Lemonade

How to use finis-est/qwen3.5-27b-aconite-v0-Q6_K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull finis-est/qwen3.5-27b-aconite-v0-Q6_K:Q6_K

Run and chat with the model

lemonade run user.qwen3.5-27b-aconite-v0-Q6_K-Q6_K

List all available models

lemonade list

Qwen3.5 27B Aconite v0 — Q6_K GGUF

Q6_K quantization of trashpanda-org/qwen3.5-27b-aconite-v0.

Quant Details

Property	Value
Source	`trashpanda-org/qwen3.5-27b-aconite-v0`
Quant	Q6_K (6.56 BPW)
Size	~21 GB
Format	GGUF (llama.cpp)
Original Precision	bf16

Usage

Load with any llama.cpp-compatible runtime (llama.cpp, KoboldCpp, ollama, LM Studio, etc.):

llama-cli -m qwen3.5-27b-aconite-v0-Q6_K.gguf -p "Your prompt here"

Notes

Quantized from the bf16 source weights using llama.cpp's convert_hf_to_gguf.py → llama-quantize
Q6_K preserves near-original quality at ~41% of the bf16 size
Fits comfortably on 2×T4 (32 GB) without CPU offload