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-4x7b"
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.

🏆 Evaluations

Tasks	Version	Filter	Metric	Value		Stderr
arc_easy	Yaml	none	acc	0.8371	±	0.0076
		none	acc_norm	0.8064	±	0.0081
boolq	Yaml	none	acc	0.8685	±	0.0059
hellaswag	Yaml	none	acc	0.6687	±	0.0047
		none	acc_norm	0.8416	±	0.0036
openbookqa	Yaml	none	acc	0.3580	±	0.0215
		none	acc_norm	0.4740	±	0.0224
piqa	Yaml	none	acc	0.8243	±	0.0089
		none	acc_norm	0.8308	±	0.0087
winogrande	Yaml	none	acc	0.7609	±	0.0120