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---
license: mit
model-index:
- name: piccolo-math-2x7b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.11
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.27
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.69
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 63.86
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.87
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 70.13
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b
name: Open LLM Leaderboard
---
# Piccolo-math-2x7b
**In loving memory of my dog Klaus (Piccolo)**
_~ Piccolo (Italian): the little one ~_
# 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-math-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
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|-------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[piccolo-math-2x7b](https://huggingface.co/macadeliccc/piccolo-math-2x7b)| 43.89| 74.98| 63.96| 44.99| 56.96|
### EQ Bench
#### Benchmark Complete:
+ 2024-01-24 00:00:40
+ Time taken: 183.3 mins
+ Prompt Format: Mistral
+ Model: macadeliccc/piccolo-math-2x7b
+ Score (v2): 70.74
+ Parseable: 167.0
---------------
Batch completed
Time taken: 183.3 mins
### AGIEval
| 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
| 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
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |47.37|± | 1.75|
| | |mc2 |63.96|± | 1.57|
Average: 63.96%
### Bigbench
| 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|
Average: 44.99%
Average score: 56.96%
Elapsed time: 01:51:53
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__piccolo-math-2x7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.32|
|AI2 Reasoning Challenge (25-Shot)|69.11|
|HellaSwag (10-Shot) |87.27|
|MMLU (5-Shot) |63.69|
|TruthfulQA (0-shot) |63.86|
|Winogrande (5-shot) |79.87|
|GSM8k (5-shot) |70.13|
|