Instructions to use Trilogix1/Qwen_Coder_F32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Trilogix1/Qwen_Coder_F32 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Trilogix1/Qwen_Coder_F32", dtype="auto")

llama-cpp-python

How to use Trilogix1/Qwen_Coder_F32 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Trilogix1/Qwen_Coder_F32",
	filename="Hugston-Qwen3-Coder-30B-A3B-Instruct-F32.IQ4_NL.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Trilogix1/Qwen_Coder_F32 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Trilogix1/Qwen_Coder_F32:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf Trilogix1/Qwen_Coder_F32:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Trilogix1/Qwen_Coder_F32:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf Trilogix1/Qwen_Coder_F32:IQ4_NL

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 Trilogix1/Qwen_Coder_F32:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf Trilogix1/Qwen_Coder_F32:IQ4_NL

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 Trilogix1/Qwen_Coder_F32:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Trilogix1/Qwen_Coder_F32:IQ4_NL

Use Docker

docker model run hf.co/Trilogix1/Qwen_Coder_F32:IQ4_NL

LM Studio
Jan
Ollama
How to use Trilogix1/Qwen_Coder_F32 with Ollama:
```
ollama run hf.co/Trilogix1/Qwen_Coder_F32:IQ4_NL
```

Unsloth Studio new

How to use Trilogix1/Qwen_Coder_F32 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 Trilogix1/Qwen_Coder_F32 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 Trilogix1/Qwen_Coder_F32 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Trilogix1/Qwen_Coder_F32 to start chatting

Pi new

How to use Trilogix1/Qwen_Coder_F32 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Trilogix1/Qwen_Coder_F32:IQ4_NL

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": "Trilogix1/Qwen_Coder_F32:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Trilogix1/Qwen_Coder_F32 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Trilogix1/Qwen_Coder_F32:IQ4_NL

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 Trilogix1/Qwen_Coder_F32:IQ4_NL

Run Hermes

hermes

Docker Model Runner
How to use Trilogix1/Qwen_Coder_F32 with Docker Model Runner:
```
docker model run hf.co/Trilogix1/Qwen_Coder_F32:IQ4_NL
```

Lemonade

How to use Trilogix1/Qwen_Coder_F32 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Trilogix1/Qwen_Coder_F32:IQ4_NL

Run and chat with the model

lemonade run user.Qwen_Coder_F32-IQ4_NL

List all available models

lemonade list

Trilogix1 commited on Nov 17, 2025

Commit

33ca023

verified ·

1 Parent(s): d9aeae2

Update README.md

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README.md +12 -0

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@@ -15,7 +15,19 @@ pipeline_tag: text-code-generation
 [▶ Watch the HugstonOne at work on Vimeo](https://vimeo.com/1137181007?share=copy&fl=sv&fe=ci)
 ## Highlights

 [▶ Watch the HugstonOne at work on Vimeo](https://vimeo.com/1137181007?share=copy&fl=sv&fe=ci)
+## Usage
+-Download App HugstonOne at Hugston.com or at https://github.com/Mainframework
+---
+-Download model from https://hugston.com/explore?folder=llm_models or Huggingface
+---
+-If you already have the Llm Model downloaded chose it by clicking pick model in HugstonOne
+-Then click Load model in Cli or Server
+-For multimodal use you need a VL/multimodal LLM model with the Mmproj file in the same folder.
+-Select model and select mmproj.
+-Note: if the mmproj is inside the same folder with other models non multimodal, the non model will not load unless the mmproj is moved from folder.
 ## Highlights