Instructions to use Dinoman1221/Legal-Assistant-LLM-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Dinoman1221/Legal-Assistant-LLM-GGUF 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 Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dinoman1221/Legal-Assistant-LLM-GGUF: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 Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Dinoman1221/Legal-Assistant-LLM-GGUF: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 Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Dinoman1221/Legal-Assistant-LLM-GGUF with Ollama:
```
ollama run hf.co/Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Dinoman1221/Legal-Assistant-LLM-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dinoman1221/Legal-Assistant-LLM-GGUF: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": "Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Dinoman1221/Legal-Assistant-LLM-GGUF with Docker Model Runner:
```
docker model run hf.co/Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M
```

Lemonade

How to use Dinoman1221/Legal-Assistant-LLM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Legal-Assistant-LLM-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Dinoman1221/Legal-Assistant-LLM-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dinoman1221/Legal-Assistant-LLM-GGUF: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 Dinoman1221/Legal-Assistant-LLM-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Dinoman1221/Legal-Assistant-LLM-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dinoman1221/Legal-Assistant-LLM-GGUF: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 "Dinoman1221/Legal-Assistant-LLM-GGUF: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"

Legal Assistant LLM (GGUF) 📌 Overview

This is a fine-tuned legal-domain language model designed to answer legal questions in an instruction-following format.

Base Model: LLaMA 3.2 1B Instruct

Fine-tuning Method: LoRA

Quantization: Q4_K_M (GGUF)

Format: GGUF

Inference Compatible With:

Ollama

llama.cpp

🎯 Intended Use

This model is intended for:

Educational purposes

Legal concept explanation

Academic research

⚠️ Not intended for professional legal advice.

📊 Training Details

Method: Supervised Fine-Tuning (SFT)

LoRA rank: 64

Epochs: 3

Batch Size: 2

Gradient Accumulation: 4

Max Sequence Length: 2048

🧾 Dataset

Custom instruction-response legal dataset in JSON format:

{ "Instruction": "What is Article 21?", "Response": "Article 21 of the Indian Constitution guarantees..." } 💻 How To Run Using Ollama ollama create legal-assistant -f Modelfile ollama run legal-assistant Using llama.cpp ./main -m Llama-3.2-1B-Instruct_Q4_K_M.gguf -p "What is bail under Indian law?" ⚠️ Limitations

May hallucinate legal details

Does not replace a lawyer

Requires human verification

Downloads last month: 21

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

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