# Piccolo-8x7b


  **In loving memory of my dog Klaus (Piccolo)**
    
  _~ Piccolo (Italian): the little one ~_

 ![piccolo.png](piccolo.png)


Based on mlabonne/NeuralBeagle-7b
Quants are available [here](https://huggingface.co/macadeliccc/piccolo-8x7b-GGUF)

# Code Example

Inference and Evaluation colab available [here](https://colab.research.google.com/drive/1ZqLNvVvtFHC_4v2CgcMVh7pP9Fvx0SbI?usp=sharing)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

def generate_response(prompt):
    """
    Generate a response from the model based on the input prompt.
    Args:
    prompt (str): Prompt for the model.

    Returns:
    str: The generated response from the model.
    """
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id)

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return response

model_id = "macadeliccc/piccolo-8x7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,load_in_4bit=True)

prompt = "What is the best way to train Cane Corsos?"

print("Response:")
print(generate_response(prompt), "\n")
```

The model is capable of quality code, math, and logical reasoning. Try whatever questions you think of.

## Example output 

![example_output](https://huggingface.co/macadeliccc/piccolo-8x7b-GGUF/resolve/main/piccolo-llama-2.png)

# Evaluations

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/mN8jXeBsgTGL6fC09s5nx.png)

https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__piccolo-8x7b

{"license": "cc-by-4.0"}
transformers safetensors mixtral text-generation license:cc-by-4.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us