---
license: gemma
---

Great at reasoning, but woke as fuck! This is a fine-tune over the Gemma-2-27B-it, since the base model fine-tuning is not generating coherent content.

```
You: Today is Tuesday. On Thursday last week, I had 10 apples. Then on Saturday I ate 5. I bought 8 apples, and 3 oranges yesterday. What day was yesterday?
Here's how to solve this problem:

* **"Thursday last week" was 5 days before Tuesday.**  Since there are 7 days in a week, counting backward from Tuesday we get to Thursday. 
* **"Yesterday" was 2 days before today.** Since today is Tuesday, yesterday was Monday.

Therefore, yesterday was Monday.
You: How many apples do I have today?
Let's break down the problem:

* You started with 10 apples on Thursday last week.
* You ate 5 apples on Saturday, leaving you with 10 - 5 = 5 apples.
* You bought 8 apples yesterday, so you had 5 + 8 = 13 apples.

Therefore, you have 13 apples today. 

You: How many oranges did I have on Saturday?
You didn't have any oranges on Saturday because you bought 3 oranges yesterday.
```




# Inference Code:

```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_path = "migtissera/Tess-v2.5-Gemma-2-27B-alpha"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path, 
    device_map="auto",
    torch_dtype=torch.bfloat16
)

terminators = [
    tokenizer.convert_tokens_to_ids("<end_of_turn>"),
]


def generate_text(llm_prompt):
    inputs = tokenizer.encode(llm_prompt, add_special_tokens=False, return_tensors="pt")
    input_ids = inputs.to("cuda")
    length = len(input_ids[0])

    instance = {
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    generation = model.generate(
        input_ids, 
        max_length=length + instance["generate_len"],
        use_cache=True,
        do_sample=True,
        top_p=instance["top_p"],
        temperature=instance["temperature"],
        top_k=instance["top_k"],
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=terminators,
    )
    # rest= tokenizer.decode(generation[0])
    output = generation[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<bos><start_of_turn>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<end_of_turn>\n<start_of_turn>model\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n<start_of_turn>user\n"
```