Text Generation
Safetensors
GGUF
English
qwen3_5
linux
devsecops
copilot
terminal
multimodal
vision
ollama
qwen
dgpl
conversational
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
- llama.cpp
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
- Pi
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"
File size: 1,209 Bytes
413d3f7 e97f684 413d3f7 3c8b649 3fdf6fd 3c8b649 e97f684 413d3f7 3d13e78 3fdf6fd 3d13e78 3fdf6fd 413d3f7 e97f684 413d3f7 3c8b649 e97f684 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | FROM ./linux_copilot_q4_k_m.gguf
FROM ./mmproj_linux_copilot_f16.gguf
TEMPLATE """{{- range .Messages }}
{{- if eq .Role "system" }}<|im_start|>system
{{ .Content }}<|im_end|>
{{- else if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{- else if eq .Role "assistant" }}<|im_start|>assistant
{{ .Content }}<|im_end|>
{{- end }}
{{- end }}
<|im_start|>assistant
"""
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|endoftext|>"
PARAMETER repeat_penalty 1.18
PARAMETER repeat_last_n 64
PARAMETER temperature 0.2
PARAMETER top_p 0.85
PARAMETER top_k 40
PARAMETER num_predict 768
SYSTEM """You are DGPL Linux Assistant 0.8B, an ultra-fast Linux Terminal Copilot and DevSecOps systems assistant developed and fine-tuned by the DevSecOps and AI Systems Team at Durbhasi Gurukulam Private Limited (DGPL).
You provide exact, safe, and ready-to-run non-interactive Bash commands, system diagnostics, OCR log inspections, and shell scripts.
Keep internal thoughts inside <think>...</think> ultra-brief (maximum 1-2 lines), then immediately output </think> followed by the verified terminal action or structured tool call inside markdown code blocks with zero boilerplate."""
|