# Piccolo-2x7b


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

 ![piccolo.png](piccolo.png)


## GGUF

Quants are available [here](https://huggingface.co/macadeliccc/piccolo-2x7b-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-2x7b"
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     |0.8552|±  |0.0072|
|          |       |none  |     0|acc_norm|0.8237|±  |0.0078|
|boolq     |Yaml   |none  |     0|acc     |0.8749|±  |0.0058|
|hellaswag |Yaml   |none  |     0|acc     |0.6734|±  |0.0047|
|          |       |none  |     0|acc_norm|0.8489|±  |0.0036|
|openbookqa|Yaml   |none  |     0|acc     |0.3640|±  |0.0215|
|          |       |none  |     0|acc_norm|0.4780|±  |0.0224|
|piqa      |Yaml   |none  |     0|acc     |0.8330|±  |0.0087|
|          |       |none  |     0|acc_norm|0.8368|±  |0.0086|
|winogrande|Yaml   |none  |     0|acc     |0.7703|±  |0.0118|


# Model Evaluation Summary

| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|-------|---------|---------|------------|----------|---------|
| piccolo-math-2x7b | 43.89% | 74.98% | 63.96% | 44.99% | 56.96% |

## AGIEval

### Tasks and Results

| Task | Version | Metric | Value | Stderr |
|------|---------|--------|-------|--------|
| agieval_aqua_rat | 0 | acc | 24.41 | ± 2.70 |
|  |  | acc_norm | 24.80 | ± 2.72 |
| agieval_logiqa_en | 0 | acc | 35.79 | ± 1.88 |
|  |  | acc_norm | 36.71 | ± 1.89 |
| agieval_lsat_ar | 0 | acc | 23.48 | ± 2.80 |
|  |  | acc_norm | 23.91 | ± 2.82 |
| agieval_lsat_lr | 0 | acc | 49.22 | ± 2.22 |
|  |  | acc_norm | 50.00 | ± 2.22 |
| agieval_lsat_rc | 0 | acc | 63.94 | ± 2.93 |
|  |  | acc_norm | 64.31 | ± 2.93 |
| agieval_sat_en | 0 | acc | 77.18 | ± 2.93 |
|  |  | acc_norm | 76.70 | ± 2.95 |
| agieval_sat_en_without_passage | 0 | acc | 45.15 | ± 3.48 |
|  |  | acc_norm | 44.66 | ± 3.47 |
| agieval_sat_math | 0 | acc | 33.64 | ± 3.19 |
|  |  | acc_norm | 30.00 | ± 3.10 |

**Average: 43.89%**

## GPT4All

### Tasks and Results

| Task | Version | Metric | Value | Stderr |
|------|---------|--------|-------|--------|
| arc_challenge | 0 | acc | 61.86 | ± 1.42 |
|  |  | acc_norm | 62.88 | ± 1.41 |
| arc_easy | 0 | acc | 84.34 | ± 0.75 |
|  |  | acc_norm | 80.47 | ± 0.81 |
| boolq | 1 | acc | 86.88 | ± 0.59 |
| hellaswag | 0 | acc | 68.56 | ± 0.46 |
|  |  | acc_norm | 85.16 | ± 0.35 |
| openbookqa | 0 | acc | 37.00 | ± 2.16 |
|  |  | acc_norm | 47.80 | ± 2.24 |
| piqa | 0 | acc | 82.21 | ± 0.89 |
|  |  | acc_norm | 83.68 | ± 0.86 |
| winogrande | 0 | acc | 77.98 | ± 1.16 |

**Average: 74.98%**

## TruthfulQA

### Tasks and Results

| Task | Version | Metric | Value | Stderr |
|------|---------|--------|-------|--------|
| truthfulqa_mc | 1 | mc1 | 47.37 | ± 1.75 |
|  |  | mc2 | 63.96 | ± 1.57 |

**Average: 63.96%**

## Bigbench

### Tasks and Results

| Task | Version | Metric | Value | Stderr |
|------|---------|--------|-------|--------|
| bigbench_causal_judgement | 0 | multiple_choice_grade | 55.26 | ± 3.62 |
| bigbench_date_understanding | 0 | multiple_choice_grade | 63.14 | ± 2.51 |
| bigbench_disambiguation_qa | 0 | multiple_choice_grade | 42.64 | ± 3.08 |
| bigbench_geometric_shapes | 0 | multiple_choice_grade | 22.84 | ± 2.22 |
| | | exact_str_match | 3.34 | ± 0.95 |
| bigbench_logical_deduction_five_objects | 0 | multiple_choice_grade | 36.60 | ± 2.16 |
| bigbench_logical_deduction_seven_objects | 0 | multiple_choice_grade | 25.57 | ± 1.65 |
| bigbench_logical_deduction_three_objects | 0 | multiple_choice_grade | 56.00 | ± 2.87 |
| bigbench_movie_recommendation | 0 | multiple_choice_grade | 42.40 | ± 2.21 |
| bigbench_navigate | 0 | multiple_choice_grade | 54.70 | ± 1.57 |
| bigbench_reasoning_about_colored_objects | 0 | multiple_choice_grade | 62.90 | ± 1.08 |
| bigbench_ruin_names | 0 | multiple_choice_grade | 53.35 | ± 2.36 |
| bigbench_salient_translation_error_detection | 0 | multiple_choice_grade | 24.35 | ± 1.36 |
| bigbench_snarks | 0 | multiple_choice_grade | 62.43 | ± 3.61 |
| bigbench_sports_understanding | 0 | multiple_choice_grade | 70.28 | ± 1.46 |
| bigbench_temporal_sequences | 0 | multiple_choice_grade | 41.30 | ± 1.56 |
| bigbench_tracking_shuffled_objects_five_objects | 0 | multiple_choice_grade | 22.32 | ± 1.18 |
| bigbench_tracking_shuffled_objects_seven_objects | 0 | multiple_choice_grade | 17.77 | ± 0.91 |
| bigbench_tracking_shuffled_objects_three_objects | 0 | multiple_choice_grade | 56.00 | ± 2.87 |

### Overall Average Score

**Average score: 56.96%** 
{"license": "cc-by-4.0"}
transformers safetensors mixtral text-generation license:cc-by-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us