Instructions to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF",
	filename="Qwen3.5-4B.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"question\": \"What is my name?\",\n    \"context\": \"My name is Clara and I live in Berkeley.\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-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 nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-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 nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M

Use Docker

docker model run hf.co/nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF with Ollama:
```
ollama run hf.co/nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M
```

Unsloth Studio

How to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-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 nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-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 nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF to start chatting

How to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-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": "nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-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 nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-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 nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF with Docker Model Runner:
```
docker model run hf.co/nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M
```

Lemonade

How to use nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nphearum/Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-4BxOpus-4.6-Code-Reasoning-Full-Distilled-GGUF / README.md

nphearum

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	---
	tags:
	- gguf
	- llama.cpp
	- unsloth
	- vision-language-model
	---

	# Qwen3.5-4BxOpus-5.6-Code-Reasoning-Full-Distilled-GGUF : GGUF

	This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).

	Example usage:
	- For text only LLMs: `llama-cli -hf nphearum/Qwen3.5-4BxOpus-5.6-Code-Reasoning-Full-Distilled-GGUF --jinja`
	- For multimodal models: `llama-mtmd-cli -hf nphearum/Qwen3.5-4BxOpus-5.6-Code-Reasoning-Full-Distilled-GGUF --jinja`

	## Available Model files:
	- `Qwen3.5-4B.Q2_K_L.gguf`
	- `Qwen3.5-4B.BF16-mmproj.gguf`
	This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)