Instructions to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with HERMES:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

llama-cpp-python

How to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0",
	filename="hermes-Qwen3.6-27B-FT-Q4_K_M.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 mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0: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 mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0: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 mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M

Use Docker

docker model run hf.co/mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M

LM Studio
Jan
Ollama
How to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with Ollama:
```
ollama run hf.co/mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M
```

Unsloth Studio

How to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 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 mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 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 mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 to start chatting

How to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0: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": "mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0: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 mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with Docker Model Runner:
```
docker model run hf.co/mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M
```

Lemonade

How to use mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0:Q4_K_M

Run and chat with the model

lemonade run user.hermes-Qwen3.6-27B-FT-Q8_0-Q4_K_M

List all available models

lemonade list

hermes-Qwen3.6-27B-FT-Q8_0

Fine-tuned Qwen3.6-27B (hybrid Mamba-2 + attention) GGUF quantizations for llama.cpp / Lemonade SDK.

Model Details

Base model: Qwen/Qwen3.6-27B
Architecture: Hybrid Mamba-2 (48 Mamba + 16 attention layers)
Fine-tuning: QLoRA r=32, train_hermes_mamba.py
Training data: 509 examples (Hermes v2 SFT dataset)

Files

File	Quant	Size	Notes
`hermes-Qwen3.6-27B-FT-q8_0.gguf`	Q8_0	~27 GB	Highest quality
`hermes-Qwen3.6-27B-FT-Q4_K_M.gguf`	Q4_K_M	~16 GB	Good quality/size balance

Usage

This model uses the user. prefix convention for Lemonade SDK:

# lemonade config
model_id: "user.hermes-Qwen3.6-27B-FT-Q8_0"

For llama.cpp directly:

./llama-server -m hermes-Qwen3.6-27B-FT-q8_0.gguf -ngl 99 -c 262144

Context Length

Q8_0: 262,144 tokens (via TurboQuant tbq3 compression)
Q4_K_M: 131,072 tokens

Notes

Converted with --no-mtp flag to strip MTP head (block 32 causes load crash)
Q8_0 quantized from F16 GGUF source (NOT directly from HuggingFace — direct Q8_0 drops SSM tensors)
Hybrid Mamba-2 architecture: no fla-core or causal-conv1d required for inference

Downloads last month: 127

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

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Model tree for mkadrlik/hermes-Qwen3.6-27B-FT-Q8_0

Base model

Qwen/Qwen3.6-27B

Quantized

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