Instructions to use Digirocket/drok-v2 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 Digirocket/drok-v2 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 Digirocket/drok-v2:Q8_0 # Run inference directly in the terminal: llama cli -hf Digirocket/drok-v2:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Digirocket/drok-v2:Q8_0 # Run inference directly in the terminal: llama cli -hf Digirocket/drok-v2:Q8_0
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 Digirocket/drok-v2:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Digirocket/drok-v2:Q8_0
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 Digirocket/drok-v2:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Digirocket/drok-v2:Q8_0
Use Docker
docker model run hf.co/Digirocket/drok-v2:Q8_0
- LM Studio
- Jan
- vLLM
How to use Digirocket/drok-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Digirocket/drok-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Digirocket/drok-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Digirocket/drok-v2:Q8_0
- Ollama
How to use Digirocket/drok-v2 with Ollama:
ollama run hf.co/Digirocket/drok-v2:Q8_0
- Unsloth Studio
How to use Digirocket/drok-v2 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 Digirocket/drok-v2 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 Digirocket/drok-v2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Digirocket/drok-v2 to start chatting
- Pi
How to use Digirocket/drok-v2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Digirocket/drok-v2:Q8_0
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": "Digirocket/drok-v2:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Digirocket/drok-v2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Digirocket/drok-v2:Q8_0
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 Digirocket/drok-v2:Q8_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Digirocket/drok-v2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Digirocket/drok-v2:Q8_0
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 "Digirocket/drok-v2:Q8_0" \ --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 Digirocket/drok-v2 with Docker Model Runner:
docker model run hf.co/Digirocket/drok-v2:Q8_0
- Lemonade
How to use Digirocket/drok-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Digirocket/drok-v2:Q8_0
Run and chat with the model
lemonade run user.drok-v2-Q8_0
List all available models
lemonade list
File size: 1,514 Bytes
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license: llama3.2
language:
- en
pipeline_tag: text-generation
tags:
- digirocket
- digital-marketing
- chat
- llama-3.2
- gguf
- conversational
base_model: meta-llama/Llama-3.2-3B-Instruct
---
# DROK v2 โ DigiRocket Technologies AI Assistant
Fine-tuned **Llama 3.2 3B Instruct** specialized for **DigiRocket Technologies** โ
a digital marketing and technology solutions provider.
## Specialization
DROK is an expert in:
- **Digital Marketing** (SEO, SEM, SMM, CRO, email marketing, content)
- **Web Development** (responsive design, e-commerce platforms, UI/UX)
- **Branding** (logo design, visual identity, brand storytelling)
- **Dropshipping** (supplier sourcing, store setup, scaling)
- **DigiRocket Services** (pricing tiers, case studies, consultation)
## Training Details
- **Base model:** [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
- **Method:** QLoRA 4-bit fine-tuning (NF4 quantization, LoRA r=16)
- **Dataset:** 1,343 DigiRocket-domain Q&A pairs (synthetic, qwen2.5-7b generated)
- **Epochs:** 3
- **Training infrastructure:** Lightning.ai Tesla T4
## Quantization
This release is the **Q8_0 GGUF** quantized version (~3.4 GB), suitable for
llama.cpp / HF Inference Endpoints serving.
## Company
DigiRocket Technologies โ global digital marketing agency:
- ๐ฎ๐ณ Gurgaon, India (HQ)
- ๐บ๐ธ Dover, USA
- ๐ฌ๐ง London, UK
Websites: [digirocket.io](https://www.digirocket.io) | [digirockett.com](https://www.digirockett.com)
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