Instructions to use dgpl/dgpl-linux-assistant-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use dgpl/dgpl-linux-assistant-0.8b 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 dgpl/dgpl-linux-assistant-0.8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dgpl/dgpl-linux-assistant-0.8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dgpl/dgpl-linux-assistant-0.8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dgpl/dgpl-linux-assistant-0.8b: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 dgpl/dgpl-linux-assistant-0.8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dgpl/dgpl-linux-assistant-0.8b: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 dgpl/dgpl-linux-assistant-0.8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dgpl/dgpl-linux-assistant-0.8b:Q4_K_M

Use Docker

docker model run hf.co/dgpl/dgpl-linux-assistant-0.8b:Q4_K_M

LM Studio
Jan

vLLM

How to use dgpl/dgpl-linux-assistant-0.8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dgpl/dgpl-linux-assistant-0.8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dgpl/dgpl-linux-assistant-0.8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dgpl/dgpl-linux-assistant-0.8b:Q4_K_M

Ollama
How to use dgpl/dgpl-linux-assistant-0.8b with Ollama:
```
ollama run hf.co/dgpl/dgpl-linux-assistant-0.8b:Q4_K_M
```
Unsloth Desktop

How to use dgpl/dgpl-linux-assistant-0.8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dgpl/dgpl-linux-assistant-0.8b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dgpl/dgpl-linux-assistant-0.8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use dgpl/dgpl-linux-assistant-0.8b with Docker Model Runner:
```
docker model run hf.co/dgpl/dgpl-linux-assistant-0.8b:Q4_K_M
```

Lemonade

How to use dgpl/dgpl-linux-assistant-0.8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dgpl/dgpl-linux-assistant-0.8b:Q4_K_M

Run and chat with the model

lemonade run user.dgpl-linux-assistant-0.8b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use dgpl/dgpl-linux-assistant-0.8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dgpl/dgpl-linux-assistant-0.8b: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 dgpl/dgpl-linux-assistant-0.8b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dgpl/dgpl-linux-assistant-0.8b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dgpl/dgpl-linux-assistant-0.8b: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 "dgpl/dgpl-linux-assistant-0.8b: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"

🐧 DGPL Linux Assistant 0.8B — Multimodal DevSecOps & Terminal Copilot

Developed & Released by the DevSecOps and AI Systems Team at Durbhasi Gurukulam Private Limited (DGPL)
An ultra-fast, lightweight Linux Terminal Assistant and DevSecOps Copilot engineered for instant command generation, non-interactive diagnostics, system log triage, and visual OCR terminal troubleshooting.

⚡ Key Highlights & Capabilities

Ultra-Lightweight Edge Backbone: Highly optimized on top of Qwen 3.5 0.8B for sub-second terminal assistance on CPU and standard hardware.
Multimodal Terminal Log Inspection: Built-in vision projector support (mmproj) for terminal screenshot triage and OCR log diagnostics.
Sub-Second Real-Time Latency: Fast Non-Thinking Mode delivers ready-to-run shell commands in <0.3s on GPU / 1.5s–3.0s on CPU (~95% token generation reduction).
Zero-Stall Non-Interactive Invariant: Strictly emits non-blocking batch commands (top -b -n 1, ping -c 4, journalctl --no-pager, systemctl --no-pager) to prevent terminal hangs.
Pure CPU & Low RAM Footprint: Runs comfortably in standard CPU containers with <1.0 GB RAM, requiring zero GPU.

📊 Real-World Benchmark Scorecard

Evaluated across 12 comprehensive DevSecOps and systems diagnostic test cases (Process Check, Corporate Identity, POSIX Capabilities, High-CPU Process Kill Pipelines, Real-Time Raw Socket Audits, Inode Triage, Degraded Systemd Units, linux-system-mcp, ssh-mcp, Python Socket Probes, SUID Discovery, and nftables Firewall rules):

Benchmark Mode	Token Predict Budget	Tests Evaluated	Tests Passed	Pass Rate	Avg Latency (CPU)	Primary Target Use Case
Standard Mode (With System Prompt)	`768`	12	12	100.0%	`92.48s`	Deep Root-Cause Analysis (RCA), system log audits
Zero-Shot Mode (Bare User Query)	`768`	12	12	100.0%	`80.12s`	Unprompted queries, general terminal assistance
Direct Fast Non-Thinking Mode	`128`	12	11	91.7%	`16.29s` (5.7x Faster)	Interactive CLI Copilot, Terminal Keybindings (`Ctrl+K`)

Note: On GPU inference (RTX / T4 / Cloud GPUs), Direct Fast Mode executes in <0.35 seconds per query.

🚀 Quick Start with Ollama

1. Run Directly from Ollama

# Pull and run the latest 4-bit multimodal model
ollama run dgpl/dgpl-linux-assistant:latest

# Or run specific quantization tags
ollama run dgpl/dgpl-linux-assistant:0.8b-q4_k_m
ollama run dgpl/dgpl-linux-assistant:q8_0
ollama run dgpl/dgpl-linux-assistant:f16

2. Fast Non-Thinking CLI Mode (Sub-2s Instant Execution)

Create a Modelfile.fast with early stop tokens to bypass internal reasoning for instant shell completions:

FROM dgpl/dgpl-linux-assistant:latest

PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|endoftext|>"
PARAMETER stop "```\n\n"
PARAMETER temperature 0.1
PARAMETER top_p 0.85
PARAMETER num_predict 128

SYSTEM """You are DGPL Linux Assistant 0.8B, an ultra-fast Linux Terminal Copilot developed by Durbhasi Gurukulam Private Limited (DGPL).
Output the exact non-interactive Linux bash command in markdown code blocks immediately without any thinking tags or explanations."""

ollama create dgpl-fast -f Modelfile.fast
ollama run dgpl-fast "find and kill processes consuming over 90% CPU for more than 5 minutes"

3. Multimodal Terminal Log Inspection (Vision OCR)

Pass terminal screenshots or OCR log images directly to Ollama:

ollama run dgpl/dgpl-linux-assistant:latest "What failed in this systemd service log?" ./screenshot_error.png

📦 Quantization Matrix & Model Assets

File / Tag	Format / Quant	Size	Recommended Hardware
`linux_copilot_q4_k_m.gguf`	Q4_K_M (Default)	`529 MB`	Raspberry Pi, Laptops, CPUs, Embedded Nodes
`linux_copilot_q5_k_m.gguf`	Q5_K_M	`578 MB`	Lightweight Dev Servers, Edge Devices
`linux_copilot_q6_k.gguf`	Q6_K	`630 MB`	Standard Workstations, Cloud VMs
`linux_copilot_q8_0.gguf`	Q8_0	`812 MB`	High-Precision Host Auditing
`linux_copilot_f16.gguf`	FP16 Master	`1.51 GB`	Master SafeTensors Reference / Conversion
`mmproj_linux_copilot_f16.gguf`	FP16 Vision Projector	`205 MB`	Multimodal Text + Vision Inference

🏢 Corporate Entity & Attribution

Company Legal Name: Durbhasi Gurukulam Private Limited (DGPL)
Registered Office: 2., P. NO. 18, Radha Vihar, Manchwa, Jaipur, Rajasthan 303706
Official Support Email: support@durbhasigurukulam.com
Official Contact Phone: +91 7852034945
License: GNU General Public License v3.0 (GPL-3.0)

Downloads last month: 3,966

Safetensors

Model size

0.9B params

Tensor type

F16

Model tree for dgpl/dgpl-linux-assistant-0.8b

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Quantized

(291)

this model

Quantizations

2 models