Instructions to use Ila-AI/IlaAI-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ila-AI/IlaAI-v3 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-v3")

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-v3 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-v3"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-v3"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Ila-AI/IlaAI-v3 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-v3"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Ila-AI/IlaAI-v3"
# 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-v3",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Ila-AI/IlaAI-v3 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-v3"

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-v3

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Ila-AI/IlaAI-v3 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-v3"

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-v3" \
  --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"

IlaAI-v3 / README.md

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metadata

language:
  - en
  - hi
  - te
license: apache-2.0
library_name: mlx
tags:
  - agriculture
  - indian-farming
  - fine-tuned
  - lora
  - qwen3
  - mlx
  - crops
  - farming
  - india
  - kcc
  - kisan-call-center
base_model: Ila-AI/IlaAI-v1.1
datasets:
  - KissanAI/Thinking-climate-100k
  - daskoushik/farmers-call-query-data-qa
pipeline_tag: text-generation

IlaAI-v3 🌱

Earth · Crop · Intelligence

An open-source Agricultural AI for Bharat's Farmers

🌾 What is IlaAI?

IlaAI (इला — Sanskrit for "the earth that gives") is an open-source LLM series built specifically for Indian agriculture. Our mission is simple — give every farmer in India access to expert agricultural advice, for free, in their own language.

"For the hands that feed a billion 🌾"

🏆 What's New in v3?

IlaAI-v3 is our biggest leap yet — trained on real farmer data from India's Kisan Call Center (KCC).

🏆 Best val loss ever — 0.598
🌾 175,000+ real farmer Q&A pairs from KCC
🗣️ Telugu support (experimental)
⚡ Fast responses with enable_thinking=False
🇮🇳 India-specific — real crops, real problems, real solutions

📊 How v3 Compares

Feature	v1	v1.1	v3
Dataset	Synthetic 96K	Thinking 100K	Real KCC 175K
Val Loss	0.695	0.821	0.598
English	✅ Good	✅ Better	✅ Excellent
Hindi	❌ Weak	⚠️ Basic	✅ Good
Telugu	❌ None	❌ None	⚠️ Experimental
Real Farmer Data	❌	❌	✅
Thinking Disabled	❌	❌	✅
Response Speed	Slow	Slow	Fast

🌾 About the KCC Dataset

The Kisan Call Center (KCC) is a Government of India initiative launched in 2004. Farmers across India call a toll-free number (1800-180-1551) to get expert advice on agriculture. Every call is logged — the farmer's question and the expert's answer.

This dataset contains 175,000+ real farmer questions with expert answers, covering:

Crop diseases and pest management
Fertilizer and pesticide recommendations
Irrigation and water management
Government schemes and subsidies
Market prices and crop selection
Region-specific advice across all Indian states

Training on this data means IlaAI speaks like a real agricultural expert — because it learned from real agricultural experts. 🌱

🚀 Quick Start

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

model, tokenizer = load("Ila-AI/IlaAI-v3")

messages = [
    {"role": "system", "content": "You are IlaAI, an expert agricultural assistant for Indian farmers. Always respond in the same language the user writes in. Be concise, practical and helpful."},
    {"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,
    enable_thinking=False  # Always add this for fast responses!
)

sampler = make_sampler(temp=0.3, top_p=0.9)
response = generate(model, tokenizer, prompt=text, max_tokens=500, sampler=sampler, verbose=True)

⚡ Important — Always Disable Thinking Mode

text = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=False,
    enable_thinking=False  # Critical! Always add this.
)

Without enable_thinking=False, the model wastes tokens on internal reasoning and responses are slower and lower quality.

📊 Training Details

Detail	Value
Base Model	IlaAI-v1.1 (Qwen3-4B)
Framework	MLX LoRA
Hardware	Apple M4 Mac Mini (24GB)
Dataset 1	KCC 175K real farmer Q&A
Dataset 2	KissanAI Thinking-climate-100k
Dataset 3	1,515 Telugu Q&A pairs
Training iters	3,000
LoRA rank	8
Final Val Loss	0.598
Peak Memory	8.3 GB

🗣️ Language Support

Language	Status
English	✅ Excellent
Hindi	✅ Good
Telugu	⚠️ Experimental
Tamil	🔄 Coming in v4
Kannada	🔄 Coming in v4
Marathi	🔄 Coming in v4
Bengali	🔄 Coming in v4
Gujarati	🔄 Coming in v4
Punjabi	🔄 Coming in v4

📈 Model Evolution

Version	Dataset	Val Loss	Status
v1	KissanAI 96K synthetic	0.695	✅ Released
v1.1	KissanAI 100K thinking	0.821	✅ Released
v3	KCC 175K real + Telugu	0.598	✅ Released
v4	Full 22+ Indian languages	TBD	🔄 Coming

🗺️ Complete Roadmap

✅ Phase 1 — Foundation (Done)

IlaAI-v1 — English agriculture advisory (96K synthetic rows)
IlaAI-v1.1 — Improved English (100K thinking dataset)
IlaAI-v3 — KCC real farmer data + Telugu experimental

🔄 Phase 2 — True Multilingual (v4)

22+ Indian languages with proper quality
Real multilingual agriculture Q&A
BPCC + AI4Bharat datasets
Release IlaAI-v4

👁️ Phase 3 — Vision (IlaAI-Vision)

Crop disease detection from photos
Support all major Indian crops
Combine text + vision into one model

📱 Phase 4 — Mobile App (Free Forever)

Free Android app
Free iOS app
On-device inference (no internet needed)
Voice input in Indian languages
Available on Play Store & App Store

🌍 Phase 5 — Global

Expand beyond India
Support farmers worldwide — Africa, Southeast Asia, South America
Open API for developers
WhatsApp bot integration

🤝 Contributing

We welcome contributions from developers, farmers, agronomists, and language experts!

📂 Add regional crop data
🌐 Help with Indian language translations
🐛 Report issues
💡 Suggest features

📜 License

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

🙏 Acknowledgements

KissanAI for open-sourcing Dhenu models and datasets
Kisan Call Center — Government of India
Qwen Team for Qwen3 base models
Apple MLX Team for MLX framework
Every Indian farmer who inspired this project 🌾

For the hands that feed a billion 🌾
HuggingFace · GitHub