Apollo Model

This is an experimental hybrid reasoning model built on Qwen2.5-32B-Instruct

Merge Method

This model was merged using the Model Stock merge method using Qwen/Qwen2.5-32B-Instruct as a base.

Enable reasoning

prompt the LLM with think deeper and step by step

Example code

''' from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "rootxhacker/Apollo-v3-32B"

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

prompt = "How many r's are in the word strawberry" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True )

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

generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response)

'''