Update README.md

README.md

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 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 library_name: peft
 pipeline_tag: text-generation
+license: mit
 tags:
 - base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
 - lora
 - transformers
+- token-efficiency
+- prompt-compression
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-
-- **Developed by:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
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-- **Demo [optional]:** [More Information Needed]
-
-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+# ⟁ 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
+
+| Prompt Type | English Tokens | Artha Tokens | Saving |
+|---|---|---|---|
+| Summarisation | 12 | 3 | 75% |
+| Code fix | 12 | 3 | 75% |
+| Email generation | 14 | 3 | 79% |
+| Comparison | 10 | 4 | 60% |
+| Explanation | 11 | 3 | 73% |
+| **Average** | **12** | **3** | **73%** |
+
+## 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
+
+```python
+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
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
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-[More Information Needed]
-
-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
-### Framework versions
-
-- PEFT 0.19.1
+- **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
+
+```bibtex
+@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}
+}
+```