Text Generation
MLX
Safetensors
English
Hindi
Telugu
qwen3
agriculture
indian-farming
fine-tuned
lora
crops
farming
india
kcc
kisan-call-center
conversational
4-bit precision
Instructions to use Ila-AI/IlaAI-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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
- Pi
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"
File size: 7,113 Bytes
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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
---
<div align="center">
<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/67a527986b39b58ea8528fb0/i_tvZOmu3un4ABR8Z0Fyp.png" alt="IlaAI Logo" width="120"/>
# IlaAI-v3 ๐ฑ
### Earth ยท Crop ยท Intelligence
**An open-source Agricultural AI for Bharat's Farmers**
[](https://huggingface.co/Ila-AI)
[](https://github.com/IlaAI)
[](LICENSE)
[]()
</div>
---
## ๐พ What is IlaAI?
**IlaAI** (เคเคฒเคพ โ Sanskrit for *"the earth that gives"*) is an open-source LLM series built specifically for Indian agriculture. Our mission is simple โ give every farmer in India access to expert agricultural advice, for free, in their own language.
> "For the hands that feed a billion ๐พ"
---
## ๐ What's New in v3?
IlaAI-v3 is our biggest leap yet โ trained on **real farmer data** from India's Kisan Call Center (KCC).
- ๐ **Best val loss ever โ 0.598**
- ๐พ **175,000+ real farmer Q&A pairs** from KCC
- ๐ฃ๏ธ **Telugu support** (experimental)
- โก **Fast responses** with `enable_thinking=False`
- ๐ฎ๐ณ **India-specific** โ real crops, real problems, real solutions
## ๐ 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 | โ
Good | โ
Better | โ
Excellent |
| Hindi | โ Weak | โ ๏ธ Basic | โ
Good |
| Telugu | โ None | โ None | โ ๏ธ Experimental |
| Real Farmer Data | โ | โ | โ
|
| Thinking Disabled | โ | โ | โ
|
| Response Speed | Slow | Slow | **Fast** |
---
## ๐พ 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 โ 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 โ because it learned from real agricultural experts. ๐ฑ
---
## ๐ Quick Start
```python
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)
```
---
## โก Important โ Always Disable Thinking Mode
```python
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.
---
## ๐ 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 |
---
## ๐ฃ๏ธ Language Support
| Language | Status |
|----------|--------|
| English | โ
Excellent |
| Hindi | โ
Good |
| Telugu | โ ๏ธ Experimental |
| Tamil | ๐ Coming in v4 |
| Kannada | ๐ Coming in v4 |
| Marathi | ๐ Coming in v4 |
| Bengali | ๐ Coming in v4 |
| Gujarati | ๐ Coming in v4 |
| Punjabi | ๐ Coming in v4 |
---
## ๐ Model Evolution
| Version | Dataset | Val Loss | Status |
|---------|---------|----------|--------|
| v1 | KissanAI 96K synthetic | 0.695 | โ
Released |
| v1.1 | KissanAI 100K thinking | 0.821 | โ
Released |
| v3 | KCC 175K real + Telugu | **0.598** | โ
Released |
| v4 | Full 22+ Indian languages | TBD | ๐ Coming |
---
## ๐บ๏ธ Complete Roadmap
### โ
Phase 1 โ Foundation (Done)
- [x] IlaAI-v1 โ English agriculture advisory (96K synthetic rows)
- [x] IlaAI-v1.1 โ Improved English (100K thinking dataset)
- [x] IlaAI-v3 โ KCC real farmer data + Telugu experimental
### ๐ Phase 2 โ True Multilingual (v4)
- [ ] 22+ Indian languages with proper quality
- [ ] Real multilingual agriculture Q&A
- [ ] BPCC + AI4Bharat datasets
- [ ] Release IlaAI-v4
### ๐๏ธ Phase 3 โ Vision (IlaAI-Vision)
- [ ] Crop disease detection from photos
- [ ] Support all major Indian crops
- [ ] Combine text + vision into one model
### ๐ฑ Phase 4 โ 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
### ๐ Phase 5 โ Global
- [ ] Expand beyond India
- [ ] Support farmers worldwide โ Africa, Southeast Asia, South America
- [ ] Open API for developers
- [ ] WhatsApp bot integration
---
## ๐ค Contributing
We welcome contributions from developers, farmers, agronomists, and language experts!
- ๐ Add regional crop data
- ๐ Help with Indian language translations
- ๐ Report issues
- ๐ก Suggest features
---
## ๐ License
Apache 2.0 โ free to use, fine-tune, and build upon.
---
## ๐ Acknowledgements
- [KissanAI](https://huggingface.co/KissanAI) for open-sourcing Dhenu models and datasets
- [Kisan Call Center](https://data.gov.in) โ Government of India
- [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 ๐พ
---
<div align="center">
<b>For the hands that feed a billion ๐พ</b><br/>
<a href="https://huggingface.co/Ila-AI">HuggingFace</a> ยท <a href="https://github.com/IlaAI">GitHub</a>
</div>
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