KissanAI/Thinking-climate-100k
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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)How to use Ila-AI/IlaAI-v1.1 with Pi:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Ila-AI/IlaAI-v1.1"
# 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"
}
]
}
}
}# Start Pi in your project directory: pi
How to use Ila-AI/IlaAI-v1.1 with Hermes Agent:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Ila-AI/IlaAI-v1.1"
# 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
hermes
How to use Ila-AI/IlaAI-v1.1 with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Ila-AI/IlaAI-v1.1"
# 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 is an improved version of IlaAI-v1 with:
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)
| 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 | 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.
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