Instructions to use Ila-AI/IlaAI-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ila-AI/IlaAI-v1 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Ila-AI/IlaAI-v1")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Ila-AI/IlaAI-v1 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Ila-AI/IlaAI-v1"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ila-AI/IlaAI-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ila-AI/IlaAI-v1 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Ila-AI/IlaAI-v1"

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 Ila-AI/IlaAI-v1

Run Hermes

hermes

OpenClaw new

How to use Ila-AI/IlaAI-v1 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Ila-AI/IlaAI-v1"

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 "Ila-AI/IlaAI-v1" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use Ila-AI/IlaAI-v1 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Ila-AI/IlaAI-v1"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Ila-AI/IlaAI-v1"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Ila-AI/IlaAI-v1",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

IlaAI-v1 / README.md

mohantvj

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	---
	language:
	- en
	- hi
	- te
	- ta
	- kn
	- mr
	- bn
	- gu
	- pa
	license: apache-2.0
	library_name: mlx
	tags:
	- agriculture
	- indian-farming
	- fine-tuned
	- lora
	- qwen3
	- mlx
	- crops
	- farming
	- india
	base_model: Qwen/Qwen3-4B
	datasets:
	- KissanAI/india-climate-qa-synth-v1
	pipeline_tag: text-generation
	---

	<div align="center">

	<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/67a527986b39b58ea8528fb0/i_tvZOmu3un4ABR8Z0Fyp.png" alt="IlaAI Logo" width="120"/>

	# IlaAI-v1 🌱
	### Earth · Crop · Intelligence

	An open-source Agricultural AI for Bharat's Farmers

	[![HuggingFace](https://img.shields.io/badge/HuggingFace-Ila--AI-yellow?logo=huggingface)](https://huggingface.co/Ila-AI)
	[![GitHub](https://img.shields.io/badge/GitHub-IlaAI-green?logo=github)](https://github.com/IlaAI)
	[![License](https://img.shields.io/badge/License-Apache%202.0-blue)](LICENSE)
	[![Base Model](https://img.shields.io/badge/Base-Qwen3--4B-purple)]()
	[![Val Loss](https://img.shields.io/badge/Val%20Loss-0.695-brightgreen)]()
	</div>

	---

	## 🌾 About IlaAI

	IlaAI (इला — Sanskrit for "the earth that gives") is an open-source LLM fine-tuned specifically for Indian agriculture. Built on Qwen3-4B and trained on 96,000+ Indian agriculture Q&A pairs, IlaAI helps farmers and developers get accurate, practical farming advice.

	> "For the hands that feed a billion 🌾"

	---

	## ✨ Capabilities

	- 🌾 iagnosis** — symptoms, causes, treatments
	- 💊 Pesticide & Fertilizer Advice — what to use, how much, when
	- 🌦️ Weather-based Advisory — sowing, irrigation, harvest timing
	- t Schemes** — eligibility, documents, how to apply
	- 📈 Market Price Guidance — mandi prices, sell/hold advice
	- 🐛 Pest Management — detection and treatment
	- 🌱 Soil Health — soil types, nutrients, improvement tips

	---

	## 🚀 Quick Start

	```python
	from mlx_lm import load, generate
	from mlx_lm.sample_utils import make_sampler

	model, tokenizer = load("Ila-AI/IlaAI-v1")

	messages = [
	{"rolontent": "You are IlaAI, an expert agricultural assistant for Indian farmers. Answer clearly and helpfully."},
	{"role": "user", "content": "My wheat crop has yellow spots on leaves. What should I do?"}
	]

	text = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=False
	)

	sampler = make_sampler(temp=0.7, top_p=0.9)
	response = generate(model, tokenizer, prompt=text, max_tokens=300, sampler=sampler, verbose=True)
	```

	---

	## 📊 Training Details

	\| Detail \| Value \|
	\|--------\|-------\|
	\| Base Model \| Qwen3-4B (4-bit quantized) \|
	\| Framework \| MLX LoRA \|
	\| Hardware \| Apple M4 Mac Mini (24GB) \|
	\| Dataset \| KissanAI/india-climate-qa-synth-v1 \|
	\| Training rows \| 92,060 \|
	\| Validation rows \| 4,845 \|
	\| Training iters \| 5,000 \|
	\| LoRA rank \| 8 \|
	\| Final Val Loss \| 0.695 \|
	\| Peak Memory \| 3.894 GB \|

	---

	## ⚠️ Limitations

	- Best performance in E — Hindi and other Indian languages are supported but quality varies
	- Text only — does not process images (vision model coming in v2)
	- v1 release — multilingual improvement planned for v2

	---

	## 🗺️ Roadmap

	### ✅ Phase 1 — Foundation (Current)
	- [x] Organization setup (GitHub + HuggingFace)
	- [x] Dataset curation (96K rows)
	- [x] IlaAI-v1 — English agriculture advisory
	- [x] Published on HuggingFace

	### 🔄 Phase 2 — Multilingual (Coming Soon)
	- [ ] Add Hindi, Telugu, Tamil, Kannada, Marathi, Bengali, Gujarati, Punjabi datasets
	- [ ] Fine-tune IlaAI-v2 on 22+ Indian languages
	- [ ] Improve response quality across all languages
	- [ ] Release IlaAI-v2 on HuggingFace

	### 👁️ Phase 3 — Vision (Coming Soon)
	- [ ] Fine-tune vision model on Indian crop disease images
	- [ ] Support all major Indian crops — Rice, Wheat, Cotton, Sugarcane, Pulses
	- [ ] Release IlaAI-Vision on HuggingFace
	- [ ] Combine text + vision into one unified model

	### 📱 Phase 4 — Mobile App (Coming Soon)
	- [ ] IlaAI Android app — free, forever
	- [ ] IlaAI iOS app — free, forever
	- [ ] On-device inference (no internet needed)
	- [ ] Voice input in Indian languages
	- [ ] Available on Play Store & App Store

	### 🌐 Phase 5 — Community (Coming Soon)
	- [ ] Open dataset contributions from farmers
	- [ ] Regional crop data by state
	- [ ] API for developers to build on IlaAI
	- [ ] WhatsApp bot integration

	---

	## 📜 License

	Apache 2.0 — free to use, fine-tune, and build upon.

	---

	## 🙏 Acknowledgements

	- [Kface.co/KissanAI) for open-sourcing Dhenu models and datasets
	- [Qwen Team](https://huggingface.co/Qwen) for Qwen3 base models
	- [Apple MLX Team](https://github.com/ml-explore/mlx) for MLX framework
	- Every Indian farmer who inspired this project 🌾

	---

	<div align="center">
	<b>For the hands that feed a billion 🌾</b><br/>
	<a href="https://huggingface.co/Ila-AI">HuggingFace</a> · <a href="https://github.com/IlaAI">GitHub</a>
	</div>