Instructions to use sikeaditya/AgriAssist_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sikeaditya/AgriAssist_LLM with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sikeaditya/AgriAssist_LLM",
	filename="agri_llama.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sikeaditya/AgriAssist_LLM with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sikeaditya/AgriAssist_LLM:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sikeaditya/AgriAssist_LLM:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sikeaditya/AgriAssist_LLM:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sikeaditya/AgriAssist_LLM: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 sikeaditya/AgriAssist_LLM:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sikeaditya/AgriAssist_LLM: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 sikeaditya/AgriAssist_LLM:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sikeaditya/AgriAssist_LLM:Q4_K_M

Use Docker

docker model run hf.co/sikeaditya/AgriAssist_LLM:Q4_K_M

LM Studio
Jan
Ollama
How to use sikeaditya/AgriAssist_LLM with Ollama:
```
ollama run hf.co/sikeaditya/AgriAssist_LLM:Q4_K_M
```

Unsloth Studio new

How to use sikeaditya/AgriAssist_LLM 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 sikeaditya/AgriAssist_LLM 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 sikeaditya/AgriAssist_LLM to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sikeaditya/AgriAssist_LLM to start chatting

Pi new

How to use sikeaditya/AgriAssist_LLM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sikeaditya/AgriAssist_LLM:Q4_K_M

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": "sikeaditya/AgriAssist_LLM:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sikeaditya/AgriAssist_LLM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sikeaditya/AgriAssist_LLM: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 sikeaditya/AgriAssist_LLM:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use sikeaditya/AgriAssist_LLM with Docker Model Runner:
```
docker model run hf.co/sikeaditya/AgriAssist_LLM:Q4_K_M
```

Lemonade

How to use sikeaditya/AgriAssist_LLM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sikeaditya/AgriAssist_LLM:Q4_K_M

Run and chat with the model

lemonade run user.AgriAssist_LLM-Q4_K_M

List all available models

lemonade list

AgriLlama: Plant Disease Information Assistant

AgriLlama is a fine-tuned large language model based on gemma-3-4b-it, specifically designed to provide detailed, actionable information about plant diseases to Indian farmers. It offers clear, concise, and locally relevant guidance on disease identification, symptoms, causes, severity, and treatment measures across major crops such as Sugarcane, Maize, Cotton, Rice, and Wheat.

Features

Tailored Guidance: Provides comprehensive details on various plant diseases affecting Indian crops.
Practical Recommendations: Offers clear instructions on treatment and prevention, helping farmers manage crop health.
User-Friendly: Utilizes the Alpaca Instruct Format to generate responses in simple, accessible language.
Versatile Applications: Suitable for use by farmers, agronomists, and agricultural extension workers.

Model Details

Base Model: gemma-3-4b-it
Fine-Tuning Dataset: Custom dataset of 200 samples focusing on plant diseases in Indian agriculture.
Intended Use: Assisting in the identification, explanation, and management of plant diseases.

Installation

To use AgriLlama, install the required libraries:

pip install transformers torch

Usage

Using Hugging Face Transformers

Here’s an example of how to use AgriLlama with the Hugging Face Transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the tokenizer and model from the Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("your-username/AgriLlama")
model = AutoModelForCausalLM.from_pretrained("your-username/AgriLlama")
# Define a prompt
prompt = "Explain Red Rot in sugarcane in simple terms for Indian farmers."
# Tokenize and generate a response
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
# Decode and print the generated response
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Note: Replace your-username/AgriLlama with the actual path of your repository.

Using Ollama

You can also use AgriLlama with Ollama, a simple way to run large language models locally.

Install Ollama if you haven't already:

curl -fsSL https://ollama.ai/install.sh | sh

Pull the model from Ollama Library

ollama pull sike_aditya/AgriLlama

Run the model using Ollama:

ollama run AgriLlama "Explain Red Rot in sugarcane in simple terms for Indian farmers."

This will generate a response based on the model’s fine-tuned dataset.

Fine-Tuning and Training

AgriLlama was fine-tuned using a custom dataset created in the Alpaca Instruct Format. The dataset covers detailed plant disease information tailored to the Indian context and includes samples for:

Sugarcane: Bacterial Blight, Healthy, Red Rot
Maize: Blight, Common Rust, Gray Leaf Spot, Healthy
Cotton: Bacterial Blight, Curl Virus, Fusarium Wilt, Healthy
Rice: Bacterial Blight, Blast, Brownspot, Tungro
Wheat: Healthy, Septoria, Strip Rust

Dataset

The fine-tuning dataset consists of carefully curated samples that provide comprehensive, accurate information designed to help users manage crop diseases effectively.

Contact

For questions or suggestions, please open an issue in the repository or contact the authors directly.

Happy farming with AgriLlama!

Downloads last month: 9

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

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