--- license: apache-2.0 language: - en - hi - ta - te - kn - ml - bn - mr - gu pipeline_tag: text-generation tags: - Axion - Indic library_name: transformers ---
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# 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 "" in output_text: reasoning_content = output_text.split("")[0].rstrip("\n") content = output_text.split("")[-1].lstrip("\n").rstrip("") else: reasoning_content = "" content = output_text.rstrip("") print("reasoning content:", reasoning_content) print("content:", content)