metadata

license: mit
language:
  - en
base_model:
  - Qwen/Qwen2.5-32B-Instruct
pipeline_tag: text-generation

Apollo Model

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

GGUF

mradermacher/Apollo-v3-32B-GGUF

thanks mradermacher for this gguf

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)