artha-1.1b

⟁ Artha-1.1B

Artha (Sanskrit: अर्थ) — meaning "essence", "purpose", "meaning"

A fine-tuned TinyLlama 1.1B model trained on Artha — a token-efficient, math-based language for human-AI communication.

The Idea

Why are we talking to AI in English?

There are 7,000 languages in the world. We picked English almost by accident — because that's what the training data was in. But what's truly universal across every language, every culture, every human mind?

Mathematics and Logic.

Artha is a compressed language built on mathematical notation where every token carries maximum meaning and nothing else survives.

What it does

"Please summarise this article in 3 bullet points, focus on key facts" → sum[article](#3, fmt:bullets) +facts "Fix the bug in this Python code and explain what was wrong" → fixcode → {diff+explain} "Write a formal email to a client, under 150 words" → gen[eml](@client, tone:formal, ~150w) "Compare React and Vue for a beginner, format as table" → cmp[React, Vue] → {table}

Results

Key Insight

Compression only works when the model is natively trained on the language. A standard English tokenizer sees fmt:bullets as 4 tokens. An Artha tokenizer sees it as 1. The model doesn't translate — it thinks natively in Artha.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load base model
base = AutoModelForCausalLM.from_pretrained(
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load Artha tokenizer and resize embeddings
tokenizer = AutoTokenizer.from_pretrained("siddsukh/artha-1.1b")
base.resize_token_embeddings(len(tokenizer), mean_resizing=False)

# Load LoRA adapter
model = PeftModel.from_pretrained(base, "siddsukh/artha-1.1b")
model.eval()

def compress(prompt):
    input_text = f"<|artha|>\n{prompt}\n<|compress|>\n"
    inputs = tokenizer(input_text, return_tensors="pt").to(
        next(model.parameters()).device
    )
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=64,
            do_sample=False,
            pad_token_id=tokenizer.eos_token_id,
        )
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=False)
    return decoded.split("<|compress|>")[-1].split("<|end|>")[0].strip()

# Example
print(compress("Please summarise this in 3 bullet points, focus on facts"))
# → sum[this](#3, fmt:bullets) +facts

Training Details

• Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
• Method: LoRA (r=16, alpha=32, target: q_proj, v_proj)
• Training data: 50,000 English↔Artha pairs
• Hardware: Google Colab TPU v5e
• Epochs: 3
• Compression achieved: 73% average token reduction

Links

• GitHub: https://github.com/sidvsukhi/artha
• Language Spec: SPEC.md in the repo
• Training notebook: examples/artha_colab.ipynb

Citation

@misc{artha2025,
  author = {Siddhantsukhatankar},
  title = {Artha: A Token-Efficient Math-Based Language for Human-AI Communication},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/siddsukh/artha-1.1b}
}