---
datasets:
- CreitinGameplays/gemma-r1-test
language:
- en
base_model:
- google/gemma-2-2b-it
pipeline_tag: text-generation
library_name: transformers
---

Chat template:
```
<start_of_turn>user
{user_prompt}<end_of_turn>
<start_of_turn>model
<think>
```

Code for testing:
```python
# test the model
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

def main():
    model_id = "CreitinGameplays/gemma-2-2b-it-R1-exp"

    # Load the tokenizer.
    tokenizer = AutoTokenizer.from_pretrained(model_id)

    # Load the model using bitsandbytes 8-bit quantization if CUDA is available.
    if torch.cuda.is_available():
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            load_in_4bit=True,
            device_map="auto"
        )
        device = torch.device("cuda")
    else:
        model = AutoModelForCausalLM.from_pretrained(model_id)
        device = torch.device("cpu")

    # Define the generation parameters.
    generation_kwargs = {
        "max_new_tokens": 4096,
        "do_sample": True,
        "temperature": 0.6,
        "top_k": 40,
        "top_p": 0.9,
        "repetition_penalty": 1.1,
        "num_return_sequences": 1,
        "pad_token_id": tokenizer.eos_token_id
    }

    print("Enter your prompt (type 'exit' to quit):")
    while True:
        # Get user input.
        user_input = input("Input> ")
        if user_input.lower().strip() in ("exit", "quit"):
            break

        # Construct the prompt in your desired format.
        prompt = f"""
<start_of_turn>user
{user_input}<end_of_turn>
<start_of_turn>model
<think>
"""

        # Tokenize the prompt and send to the selected device.
        input_ids = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=True).to(device)

        # Create a new TextStreamer instance for streaming responses.
        streamer = TextStreamer(tokenizer)
        generation_kwargs["streamer"] = streamer

        print("\nAssistant Response:")
        # Generate the text (tokens will stream to stdout via the streamer).
        outputs = model.generate(input_ids, **generation_kwargs)

if __name__ == "__main__":
    main()
```

#INeedSomeGPU