Update README.md

README.md

@@ -1,65 +1,100 @@
 ---
-library_name: transformers
 license: apache-2.0
 language:
 - en
-- bn
 - hi
-- kn
-- gu
-- mr
-- ml
-- or
-- pa
 - ta
 - te
-base_model: sarvamai/sarvam-m
-base_model_relation: finetune
+- kn
+- ml
+- bn
+- mr
+- gu
+pipeline_tag: text-generation
 tags:
-- llama-cpp
-- gguf-my-repo
+- Axion
+- Indic
+library_name: transformers
 ---
 
-# AdvRahul/sarvam-m-Q5_K_M-GGUF
-This model was converted to GGUF format from [`sarvamai/sarvam-m`](https://huggingface.co/sarvamai/sarvam-m) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
-Refer to the [original model card](https://huggingface.co/sarvamai/sarvam-m) for more details on the model.
-
-## Use with llama.cpp
-Install llama.cpp through brew (works on Mac and Linux)
-
-```bash
-brew install llama.cpp
-
-```
-Invoke the llama.cpp server or the CLI.
-
-### CLI:
-```bash
-llama-cli --hf-repo AdvRahul/sarvam-m-Q5_K_M-GGUF --hf-file sarvam-m-q5_k_m.gguf -p "The meaning to life and the universe is"
-```
-
-### Server:
-```bash
-llama-server --hf-repo AdvRahul/sarvam-m-Q5_K_M-GGUF --hf-file sarvam-m-q5_k_m.gguf -c 2048
-```
-
-Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
-
-Step 1: Clone llama.cpp from GitHub.
-```
-git clone https://github.com/ggerganov/llama.cpp
-```
-
-Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
-```
-cd llama.cpp && LLAMA_CURL=1 make
-```
-
-Step 3: Run inference through the main binary.
-```
-./llama-cli --hf-repo AdvRahul/sarvam-m-Q5_K_M-GGUF --hf-file sarvam-m-q5_k_m.gguf -p "The meaning to life and the universe is"
-```
-or 
-```
-./llama-server --hf-repo AdvRahul/sarvam-m-Q5_K_M-GGUF --hf-file sarvam-m-q5_k_m.gguf -c 2048
-```
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/AdvRahul" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-Axion-blue" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/AdvRahul" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AdvRahul-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/AdvRahul" target="_blank" style="margin: 2px;">
+    <img alt="Github" src="https://img.shields.io/badge/GitHub-Axion-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://x.com/yourhandle" target="_blank" style="margin: 2px;">
+    <img alt="X" src="https://img.shields.io/badge/X-Axion-6080F0?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <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;"/>
+  </a>
+</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)