Instructions to use Mungert/DiffuCoder-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Mungert/DiffuCoder-7B-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mungert/DiffuCoder-7B-Instruct-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 Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mungert/DiffuCoder-7B-Instruct-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 Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Mungert/DiffuCoder-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use Mungert/DiffuCoder-7B-Instruct-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 Mungert/DiffuCoder-7B-Instruct-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 Mungert/DiffuCoder-7B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mungert/DiffuCoder-7B-Instruct-GGUF to start chatting

How to use Mungert/DiffuCoder-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mungert/DiffuCoder-7B-Instruct-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": "Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Mungert/DiffuCoder-7B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Mungert/DiffuCoder-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use Mungert/DiffuCoder-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DiffuCoder-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Mungert/DiffuCoder-7B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mungert/DiffuCoder-7B-Instruct-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 Mungert/DiffuCoder-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

DiffuCoder-7B-Instruct-GGUF / README.md

Mungert

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	---
	base_model:
	- apple/DiffuCoder-7B-Base
	tags:
	- code
	- text-diffusion-model
	- diffusion large language model
	license: unknown
	---

	# <span style="color: #7FFF7F;">DiffuCoder-7B-Instruct GGUF Models</span>


	## <span style="color: #7F7FFF;">Model Generation Details</span>

	This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`21c02174`](https://github.com/ggerganov/llama.cpp/commit/21c021745d781edf9c44b4972ef6cbbf53b0ecff).





	---

	## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>

	I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

	In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here:
	👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)

	While this does increase model file size, it significantly improves precision for a given quantization level.

	### I'd love your feedback—have you tried this? How does it perform for you?




	---

	<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
	Click here to get info on choosing the right GGUF model format
	</a>

	---



	<!--Begin Original Model Card-->

	### DiffuCoder-7B-Instruct

	The DiffuCoder-7B-Instruct model builds on the DiffuCoder-7B-Base checkpoint with instruction-tuning to better follow code-related prompts.

	- Training recipe: with a newly introduced pad token, we train this model with fixed length conditionally on [OpenCoder-SFT](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2) data for 5 epochs.

	- Benchmarks: Demonstrates stronger instruction-following capabilities than the Base model.


	#### More details and usage examples:

	- Paper: [DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation](https://arxiv.org/abs/2506.20639)

	- GitHub: https://github.com/apple/ml-diffucoder

	```
	import torch
	from transformers import AutoModel, AutoTokenizer

	model_path = "apple/DiffuCoder-7B-Instruct"
	model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True)
	tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
	model = model.to("cuda").eval()

	query = "Write a function to find the shared elements from the given two lists."
	prompt = f"""<\|im_start\|>system
	You are a helpful assistant.<\|im_end\|>
	<\|im_start\|>user
	{query.strip()}
	<\|im_end\|>
	<\|im_start\|>assistant
	""" ## following the template of qwen; you can also use apply_chat_template function

	TOKEN_PER_STEP = 1 # diffusion timesteps * TOKEN_PER_STEP = total new tokens

	inputs = tokenizer(prompt, return_tensors="pt")
	input_ids = inputs.input_ids.to(device="cuda")
	attention_mask = inputs.attention_mask.to(device="cuda")

	output = model.diffusion_generate(
	input_ids,
	attention_mask=attention_mask,
	max_new_tokens=256,
	output_history=True,
	return_dict_in_generate=True,
	steps=256//TOKEN_PER_STEP,
	temperature=0.3,
	top_p=0.95,
	alg="entropy",
	alg_temp=0.,
	)
	generations = [
	tokenizer.decode(g[len(p) :].tolist())
	for p, g in zip(input_ids, output.sequences)
	]

	print(generations[0].split('<\|dlm_pad\|>')[0])
	```

	#### Acknowledgement
	To power this HuggingFace model release, we reuse [Dream](https://huggingface.co/Dream-org/Dream-v0-Base-7B)'s modeling architecture and generation utils.


	<!--End Original Model Card-->

	---

	# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

	Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

	👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)


	The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)

	💬 How to test:
	Choose an AI assistant type:
	- `TurboLLM` (GPT-4.1-mini)
	- `HugLLM` (Hugginface Open-source models)
	- `TestLLM` (Experimental CPU-only)

	### What I’m Testing
	I’m pushing the limits of small open-source models for AI network monitoring, specifically:
	- Function calling against live network services
	- How small can a model go while still handling:
	- Automated Nmap security scans
	- Quantum-readiness checks
	- Network Monitoring tasks

	🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):
	- ✅ Zero-configuration setup
	- ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
	- 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

	### Other Assistants
	🟢 TurboLLM – Uses gpt-4.1-mini :
	- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
	- Create custom cmd processors to run .net code on Quantum Network Monitor Agents
	- Real-time network diagnostics and monitoring
	- Security Audits
	- Penetration testing (Nmap/Metasploit)

	🔵 HugLLM – Latest Open-source models:
	- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

	### 💡 Example commands you could test:
	1. `"Give me info on my websites SSL certificate"`
	2. `"Check if my server is using quantum safe encyption for communication"`
	3. `"Run a comprehensive security audit on my server"`
	4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!

	### Final Word

	I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.

	If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

	I'm also open to job opportunities or sponsorship.

	Thank you! 😊