File size: 9,589 Bytes
5cd1a45 3354d9e 5cd1a45 c93382a e75eaf7 c93382a e75eaf7 f324ae3 58252cd 69ab5d5 58252cd f324ae3 3354d9e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
---
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|
|