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Ila-AI/IlaAI-v3
IlaAI-v3 is a text generation model from Ila-AI. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
Downloads ยท 30 days
103
33% of all-time downloads
All-time downloads
311
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.safetensors2.3 GB ยท 99%
How the weights are stored.
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From the Hugging Face model README
An open-source Agricultural AI for Bharat's Farmers
</div>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"
IlaAI-v3 is our biggest leap yet โ trained on real farmer data from India's Kisan Call Center (KCC).
enable_thinking=False| 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 |
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:
Training on this data means IlaAI speaks like a real agricultural expert โ because it learned from real agricultural experts. ๐ฑ
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)
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.
| 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 | 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 |
| 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 |
We welcome contributions from developers, farmers, agronomists, and language experts!
Apache 2.0 โ free to use, fine-tune, and build upon.