Instructions to use digitalai/DgMind-20B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use digitalai/DgMind-20B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="digitalai/DgMind-20B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("digitalai/DgMind-20B-GGUF", dtype="auto")

llama-cpp-python

How to use digitalai/DgMind-20B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="digitalai/DgMind-20B-GGUF",
	filename="gpt-oss-20b.MXFP4.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use digitalai/DgMind-20B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf digitalai/DgMind-20B-GGUF
# Run inference directly in the terminal:
llama-cli -hf digitalai/DgMind-20B-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf digitalai/DgMind-20B-GGUF
# Run inference directly in the terminal:
llama-cli -hf digitalai/DgMind-20B-GGUF

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 digitalai/DgMind-20B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf digitalai/DgMind-20B-GGUF

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 digitalai/DgMind-20B-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf digitalai/DgMind-20B-GGUF

Use Docker

docker model run hf.co/digitalai/DgMind-20B-GGUF

LM Studio
Jan

vLLM

How to use digitalai/DgMind-20B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "digitalai/DgMind-20B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "digitalai/DgMind-20B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/digitalai/DgMind-20B-GGUF

SGLang

How to use digitalai/DgMind-20B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "digitalai/DgMind-20B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "digitalai/DgMind-20B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "digitalai/DgMind-20B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "digitalai/DgMind-20B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use digitalai/DgMind-20B-GGUF with Ollama:
```
ollama run hf.co/digitalai/DgMind-20B-GGUF
```

Unsloth Studio

How to use digitalai/DgMind-20B-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 digitalai/DgMind-20B-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 digitalai/DgMind-20B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for digitalai/DgMind-20B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use digitalai/DgMind-20B-GGUF with Docker Model Runner:
```
docker model run hf.co/digitalai/DgMind-20B-GGUF
```

Lemonade

How to use digitalai/DgMind-20B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull digitalai/DgMind-20B-GGUF

Run and chat with the model

lemonade run user.DgMind-20B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

DgMind 20B: Advanced Reasoning & Expert Coding Assistant

DgMind 20B is a state-of-the-art, fine-tuned large language model designed for high-level logical reasoning and professional-grade software development. Built upon the GPT-OSS 20B architecture, this model has been optimized using the Unsloth library to provide efficient yet powerful performance on consumer-grade hardware.

👤 Identity & Developer

Model Name: DgMind
Developer: Erfan Mohamadnia
Core Persona: A specialized AI assistant that excels in complex coding tasks, architectural decisions, and deep logical analysis.

📊 Training Details

Base Model: GPT-OSS 20B (Unsloth 4-bit optimized)
Dataset: Code-290k-ShareGPT
Technique: LoRA (Low-Rank Adaptation)
Optimization: Fine-tuned specifically on responses to enhance conversational accuracy and identity injection.

📈 Performance & Convergence

The model demonstrates a stable decrease in training loss, ensuring precise instruction following and a minimized hallucination rate in coding contexts.

💬 Prompt Template (Chat Format)

DgMind uses the following message structure to maintain context and role separation:


{% for message in messages %}{{ '<|start|>' + message['role'] + '<|message|>' + message['content'] + '<|end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|start|>assistant<|message|>' }}{% endif %}

Example:


<|start|>user<|message|>Write a Python script for a custom API gateway.<|end|>

<|start|>assistant<|message|>

🛠 Deployment & Usage

Local Execution via Ollama

Download the .gguf file.
Create a file named Modelfile:


FROM "./DgMind-20B.Q4_K_M.gguf"

PARAMETER temperature 0.7

SYSTEM """You are DgMind, a helpful AI assistant developed by Erfan Mohamadnia. You specialize in advanced reasoning and expert-level coding."""

Run: ollama create DgMind -f Modelfile then ollama run DgMind.

Server Integration (llama.cpp)

Run the internal API server:


./llama-server -m DgMind-20B.Q4_K_M.gguf --host 0.0.0.0 --port 8080 --n-gpu-layers 62

📜 Acknowledgments

Special thanks to the Unsloth AI team for their memory-efficient fine-tuning kernels, and to ajibawa-2023 for providing the high-quality ShareGPT dataset.