--- license: cc-by-4.0 --- # 🐾 Piccolo-4x7b 🐾 **In loving memory of my dog Klaus (Piccolo)** _~ Piccolo (Italian): the little one ~_ ![piccolo.png](piccolo.png) # 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-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|n-shot| Metric |Value | |Stderr| |----------|-------|------|-----:|--------|-----:|---|-----:| |arc_easy |Yaml |none | 0|acc |0.8371|± |0.0076| | | |none | 0|acc_norm|0.8064|± |0.0081| |boolq |Yaml |none | 0|acc |0.8685|± |0.0059| |hellaswag |Yaml |none | 0|acc |0.6687|± |0.0047| | | |none | 0|acc_norm|0.8416|± |0.0036| |openbookqa|Yaml |none | 0|acc |0.3580|± |0.0215| | | |none | 0|acc_norm|0.4740|± |0.0224| |piqa |Yaml |none | 0|acc |0.8243|± |0.0089| | | |none | 0|acc_norm|0.8308|± |0.0087| |winogrande|Yaml |none | 0|acc |0.7609|± |0.0120|