---
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](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/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)

```