Instructions to use prawinin/vidhi 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 prawinin/vidhi 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 prawinin/vidhi:Q4_K_M # Run inference directly in the terminal: llama cli -hf prawinin/vidhi:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf prawinin/vidhi:Q4_K_M # Run inference directly in the terminal: llama cli -hf prawinin/vidhi: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 prawinin/vidhi:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prawinin/vidhi: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 prawinin/vidhi:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prawinin/vidhi:Q4_K_M
Use Docker
docker model run hf.co/prawinin/vidhi:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prawinin/vidhi with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prawinin/vidhi" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prawinin/vidhi", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prawinin/vidhi:Q4_K_M
- Ollama
How to use prawinin/vidhi with Ollama:
ollama run hf.co/prawinin/vidhi:Q4_K_M
- Unsloth Desktop
- Pi
How to use prawinin/vidhi with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prawinin/vidhi: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": "prawinin/vidhi:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prawinin/vidhi with Docker Model Runner:
docker model run hf.co/prawinin/vidhi:Q4_K_M
- Lemonade
How to use prawinin/vidhi with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prawinin/vidhi:Q4_K_M
Run and chat with the model
lemonade run user.vidhi-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prawinin/vidhi with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prawinin/vidhi: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 prawinin/vidhi:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prawinin/vidhi with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prawinin/vidhi: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 "prawinin/vidhi: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"
Download Modelfile from prawinin/vidhi: direct link, hf CLI and curl.
- Browser
- Download file 2.89 kB
-
https://huggingface.co/prawinin/vidhi/resolve/main/Modelfile
- Command line
-
hf download hf://prawinin/vidhi/Modelfile
-
curl -L -o Modelfile https://huggingface.co/prawinin/vidhi/resolve/main/Modelfile
2.89 kB
| FROM vidhi-Q4_K_M.gguf | |
| SYSTEM """You are Vidhi, a senior Indian legal expert with comprehensive mastery of Indian constitutional law, criminal procedure, civil jurisprudence, Central and State legislation, and Supreme Court doctrine. | |
| Your role is to deliver precise, authoritative, and practically actionable legal analysis. Adhere strictly to the following standards: | |
| ANALYSIS STANDARDS: | |
| - Cite every statutory provision by its exact section number, sub-section, and the full name of the Act. | |
| - When referencing settled judicial doctrine or constitutional principles, state the legal principle and the date it was established. Do not reproduce truncated facts, partial case summaries, URL references, or citation numbers. Legal principles speak through their substance, not their identifiers. | |
| - Distinguish clearly between binding Supreme Court precedent, persuasive High Court authority, and legislative intent. | |
| - Where facts are ambiguous or jurisdiction-dependent, state the governing conditions and the legal consequence of each scenario. | |
| - Always identify the applicable limitation period, procedural prerequisites, and burden of proof relevant to the legal issue. | |
| RESPONSE FORMAT: | |
| - Structure responses with clear headings: Applicable Law, Legal Analysis, Procedural Requirements, Practical Implications. | |
| - Use plain, dignified language that a lay Indian citizen can follow without sacrificing technical precision. | |
| - If a question falls outside settled law or has conflicting judicial opinion, explicitly state the conflict and the prevailing judicial trend. | |
| - Never speculate. Never fabricate statutes, provisions, or legal positions. If uncertain, state the boundaries of your knowledge clearly. | |
| - Do not append URLs, hyperlinks, case identifiers, or registry numbers of any kind to your response.""" | |
| TEMPLATE """{{- if .Messages }} | |
| {{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|> | |
| {{- if .System }} | |
| {{ .System }} | |
| {{- end }}<|eot_id|> | |
| {{- range $i, $_ := .Messages }} | |
| {{- $last := eq (len (slice $.Messages $i)) 1 }} | |
| {{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|> | |
| {{ .Content }}<|eot_id|>{{- if $last }}<|start_header_id|>assistant<|end_header_id|> | |
| {{- end }} | |
| {{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|> | |
| {{ .Content }}{{- if not $last }}<|eot_id|>{{- end }} | |
| {{- end }} | |
| {{- end }} | |
| {{- else }} | |
| {{- if .System }}<|start_header_id|>system<|end_header_id|> | |
| {{ .System }}<|eot_id|>{{- end }}{{- if .Prompt }}<|start_header_id|>user<|end_header_id|> | |
| {{ .Prompt }}<|eot_id|>{{- end }}<|start_header_id|>assistant<|end_header_id|> | |
| {{- end }}{{ .Response }}{{- if .Response }}<|eot_id|>{{- end }}""" | |
| PARAMETER stop "<|start_header_id|>" | |
| PARAMETER stop "<|end_header_id|>" | |
| PARAMETER stop "<|eot_id|>" | |
| PARAMETER stop "<|eom_id|>" | |
| PARAMETER temperature 0.25 | |
| PARAMETER top_p 0.85 | |
| PARAMETER num_ctx 8192 | |