Instructions to use Ila-AI/IlaAI-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ila-AI/IlaAI-v1.1 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-v1.1")

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-v1.1 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-v1.1"

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-v1.1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ila-AI/IlaAI-v1.1 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-v1.1"

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-v1.1

Run Hermes

hermes

OpenClaw new

How to use Ila-AI/IlaAI-v1.1 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-v1.1"

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-v1.1" \
  --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-v1.1 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-v1.1"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Ila-AI/IlaAI-v1.1"
# 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-v1.1",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

IlaAI-v1.1 / README.md

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metadata

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: Ila-AI/IlaAI-v1
datasets:
  - KissanAI/Thinking-climate-100k
pipeline_tag: text-generation

IlaAI-v1.1 🌱

Earth · Crop · Intelligence

An open-source Agricultural AI for Bharat's Farmers

🌾 What's New in v1.1?

IlaAI-v1.1 is an improved version of IlaAI-v1 with:

5x more training data — 9,000 rows vs 1,800 rows
Better dataset — KissanAI Thinking-climate-100k (chain-of-thought reasoning)
More training — 3,000 iterations vs 2,000 iterations
Better Val Loss — 0.821 vs 0.874
Multilingual system prompt — responds in user's language

🚀 Quick Start

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

model, tokenizer = load("Ila-AI/IlaAI-v1.1")

messages = [
    {"role": "system", "content": "You are IlaAI, an expert agricultural assistant for Indian farmers. Always respond in the same language the user writes in. Keep answers concise, practical and under 200 words."},
    {"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=1000, sampler=sampler, verbose=True)

📊 Training Details

Detail	Value
Base Model	IlaAI-v1 (Qwen3-4B)
Framework	MLX LoRA
Hardware	Apple M4 Mac Mini (24GB)
Dataset	KissanAI/Thinking-climate-100k
Training rows	9,000
Validation rows	1,000
Training iters	3,000
LoRA rank	8
Final Val Loss	0.821
Peak Memory	4.507 GB

🗣️ Language Support

Language	Status
English	✅ Excellent
Hindi	✅ Good
Telugu	⚠️ Basic
Tamil	⚠️ Basic
Kannada	⚠️ Basic
Others	⚠️ Basic

Full multilingual support coming in IlaAI-v2 with real multilingual agriculture data.

⚠️ Limitations

Best performance in English — primary training language
Hindi — good quality responses
Other Indian languages — basic support, improving in v2
Text only — vision model coming separately

🗺️ Roadmap

✅ Phase 1 — Foundation

IlaAI-v1 — English agriculture advisory
IlaAI-v1.1 — Improved English + basic multilingual

🔄 Phase 2 — True Multilingual (v2)

Real multilingual agriculture data (BPCC + AI4Bharat)
22+ Indian languages with proper quality
Release IlaAI-v2

👁️ Phase 3 — Vision

Crop disease detection from photos
IlaAI-Vision model

📱 Phase 4 — Mobile App

Free Android & iOS app
On-device inference
Voice input in Indian languages

🌍 Phase 5 — Global

Expand beyond India
Support farmers worldwide
API for developers

📜 License

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

🙏 Acknowledgements

KissanAI for open-sourcing Dhenu models and datasets
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