README.md

---
license: apache-2.0
language:
- en
- hi
- ta
- te
- kn
- ml
- bn
- mr
- gu
pipeline_tag: text-generation
tags:
- Axion
- Indic
library_name: transformers
---

<div align="center" style="line-height: 1;">
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  <a href="https://huggingface.co/AdvRahul" target="_blank" style="margin: 2px;">
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  </a>
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  <a href="#license" style="margin: 2px;">
    <img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
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</div>

# Axion-Pro-Indic-24B  


## Model Information  

**Axion-Pro-Indic-24B** is a multilingual, hybrid-reasoning, text-only language model built on Mistral-Small.  
This post-trained version delivers exceptional improvements over the base model:  

- **+20%** average improvement on Indian language benchmarks  
- **+21.6%** enhancement on math benchmarks  
- **+17.6%** boost on programming benchmarks  
- **+86%** improvement in romanized Indian language GSM-8K benchmarks (languages × mathematics intersection).  

### Key Features  

- **Hybrid Thinking Mode**: Supports both "think" and "non-think" modes.  
- **Advanced Indic Skills**: Post-trained on Indian languages + English, reflecting Indian cultural values.  
- **Superior Reasoning Capabilities**: Outperforms similarly sized models on coding and math benchmarks.  
- **Seamless Multilingual Experience**: Full support for Indic scripts and romanized text.  

---

## Quickstart  

### With Transformers  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "AdvRahul/Axion-Pro-Indic-24B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, torch_dtype="auto", device_map="auto"
)

prompt = "Who are you and what is your purpose on this planet?"

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    enable_thinking=True,  # Default True; set False for no-think mode
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(**model_inputs, max_new_tokens=8192)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()
output_text = tokenizer.decode(output_ids)

if "</think>" in output_text:
    reasoning_content = output_text.split("</think>")[0].rstrip("\n")
    content = output_text.split("</think>")[-1].lstrip("\n").rstrip("</s>")
else:
    reasoning_content = ""
    content = output_text.rstrip("</s>")

print("reasoning content:", reasoning_content)
print("content:", content)