File size: 19,275 Bytes
e5c1dbe |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 |
---
language:
- en
license: mit
library_name: transformers
inference:
parameters:
max_new_tokens: 64
do_sample: true
temperature: 0.1
repetition_penalty: 10
no_repeat_ngram_size: 4
eta_cutoff: 0.0006
renormalize_logits: true
widget:
- text: My name is El Microondas the Wise, and
example_title: El Microondas
- text: Kennesaw State University is a public
example_title: Kennesaw State University
- text: >-
Bungie Studios is an American video game developer. They are most famous for
developing the award winning Halo series of video games. They also made
Destiny. The studio was founded
example_title: Bungie
- text: The Mona Lisa is a world-renowned painting created by
example_title: Mona Lisa
- text: >-
The Harry Potter series, written by J.K. Rowling, begins with the book
titled
example_title: Harry Potter Series
- text: >-
Question: I have cities, but no houses. I have mountains, but no trees. I
have water, but no fish. What am I?
Answer:
example_title: Riddle
- text: The process of photosynthesis involves the conversion of
example_title: Photosynthesis
- text: >-
Jane went to the store to buy some groceries. She picked up apples, oranges,
and a loaf of bread. When she got home, she realized she forgot
example_title: Story Continuation
- text: >-
Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and
another train leaves Station B at 10:00 AM and travels at 80 mph, when will
they meet if the distance between the stations is 300 miles?
To determine
example_title: Math Problem
- text: In the context of computer programming, an algorithm is
example_title: Algorithm Definition
pipeline_tag: text-generation
model-index:
- name: nano-phi-115M-v0.1
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: 24.15
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
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: 29.99
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
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: 25.46
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
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: 44.3
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
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: 51.45
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
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: 0
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
name: Open LLM Leaderboard
datasets:
- kenhktsui/minipile_quality_score_v1
- kenhktsui/simple_wikipedia_LM_quality_score_v1
- kenhktsui/refinedweb-3m_quality_score_v1
- kenhktsui/TM-DATA_quality_score_v1
- kenhktsui/openwebtext_quality_score_v1
- HuggingFaceTB/cosmopedia
---
# Model Card for nano-phi-192M-v0.1
This is a continual effort from [kenhktsui/nano-phi-115M-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-v0.1).
The model is not aligned.
Major differences:
- bigger tokenizer's vocab size
- addition of [HuggingFaceTB/cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) as training dataset
- training token: 19B vs 7B
## How to use
To use the model, you will need transformer version >= 4.37.2
```
pip install transformers>=4.37.2
```
```
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="kenhktsui/nano-phi-192M-v0.1")
pipe("I am a machine learning researcher. I work on", max_new_tokens=50, repetition_penalty=10.0)
```
## Some metrics
- model
- hidden_size: 768
- num_key_value_heads: 8 (grouped query attention)
- num_attention_heads: 24
- num_hidden_layers: 6
- context length: 1024
- total params: 192M
- training:
- global steps: 36,000
## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Metric |kenhktsui/nano-phi-191M-v0.1 |[kenhktsui/nano-phi-115M-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-v0.1)|[microsoft/phi-2](https://huggingface.co/microsoft/phi-2) (Reproduced)|
|-----------------------|---------------------------|---------------------------|---------------------------|
| Avg. |29.24 | 28.68 |61.53 |
| ARC (25-shot) |24.15 | 21.93 |61.52 |
| HellaSwag (10-shot) | 29.99 | 27.87 |75.13 |
| MMLU (5-shot) |25.46 | 25.30 |58.23 |
| TruthfulQA (0-shot) |44.30 | 46.01 |44.46 |
| Winogrande (5-shot) |51.54 | 50.99 |74.51 |
| GSM8K (5-shot) |0.0 | 0.0 |55.34 |
Details:
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
| Task |Version| Metric |Value | |Stderr|
|--------|------:|--------|-----:|---|-----:|
|arc_easy| 0|acc |0.4596|± |0.0102|
| | |acc_norm|0.4070|± |0.0101|
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 25, batch_size: 8
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.1911|± |0.0115|
| | |acc_norm|0.2415|± |0.0125|
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 10, batch_size: 8
| Task |Version| Metric |Value | |Stderr|
|---------|------:|--------|-----:|---|-----:|
|hellaswag| 0|acc |0.2833|± |0.0045|
| | |acc_norm|0.2999|± |0.0046|
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.2583|± |0.0153|
| | |mc2 |0.4430|± |0.0152|
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 8
| Task |Version| Metric |Value | |Stderr|
|-------------------------------------------------|------:|--------|-----:|---|-----:|
|hendrycksTest-abstract_algebra | 1|acc |0.2200|± |0.0416|
| | |acc_norm|0.2200|± |0.0416|
|hendrycksTest-anatomy | 1|acc |0.2593|± |0.0379|
| | |acc_norm|0.2593|± |0.0379|
|hendrycksTest-astronomy | 1|acc |0.1711|± |0.0306|
| | |acc_norm|0.1711|± |0.0306|
|hendrycksTest-business_ethics | 1|acc |0.2400|± |0.0429|
| | |acc_norm|0.2400|± |0.0429|
|hendrycksTest-clinical_knowledge | 1|acc |0.2566|± |0.0269|
| | |acc_norm|0.2566|± |0.0269|
|hendrycksTest-college_biology | 1|acc |0.2639|± |0.0369|
| | |acc_norm|0.2639|± |0.0369|
|hendrycksTest-college_chemistry | 1|acc |0.1800|± |0.0386|
| | |acc_norm|0.1800|± |0.0386|
|hendrycksTest-college_computer_science | 1|acc |0.3300|± |0.0473|
| | |acc_norm|0.3300|± |0.0473|
|hendrycksTest-college_mathematics | 1|acc |0.3000|± |0.0461|
| | |acc_norm|0.3000|± |0.0461|
|hendrycksTest-college_medicine | 1|acc |0.2023|± |0.0306|
| | |acc_norm|0.2023|± |0.0306|
|hendrycksTest-college_physics | 1|acc |0.2843|± |0.0449|
| | |acc_norm|0.2843|± |0.0449|
|hendrycksTest-computer_security | 1|acc |0.2200|± |0.0416|
| | |acc_norm|0.2200|± |0.0416|
|hendrycksTest-conceptual_physics | 1|acc |0.2511|± |0.0283|
| | |acc_norm|0.2511|± |0.0283|
|hendrycksTest-econometrics | 1|acc |0.2807|± |0.0423|
| | |acc_norm|0.2807|± |0.0423|
|hendrycksTest-electrical_engineering | 1|acc |0.2897|± |0.0378|
| | |acc_norm|0.2897|± |0.0378|
|hendrycksTest-elementary_mathematics | 1|acc |0.2804|± |0.0231|
| | |acc_norm|0.2804|± |0.0231|
|hendrycksTest-formal_logic | 1|acc |0.2143|± |0.0367|
| | |acc_norm|0.2143|± |0.0367|
|hendrycksTest-global_facts | 1|acc |0.1700|± |0.0378|
| | |acc_norm|0.1700|± |0.0378|
|hendrycksTest-high_school_biology | 1|acc |0.3226|± |0.0266|
| | |acc_norm|0.3226|± |0.0266|
|hendrycksTest-high_school_chemistry | 1|acc |0.2759|± |0.0314|
| | |acc_norm|0.2759|± |0.0314|
|hendrycksTest-high_school_computer_science | 1|acc |0.2700|± |0.0446|
| | |acc_norm|0.2700|± |0.0446|
|hendrycksTest-high_school_european_history | 1|acc |0.2606|± |0.0343|
| | |acc_norm|0.2606|± |0.0343|
|hendrycksTest-high_school_geography | 1|acc |0.3081|± |0.0329|
| | |acc_norm|0.3081|± |0.0329|
|hendrycksTest-high_school_government_and_politics| 1|acc |0.3627|± |0.0347|
| | |acc_norm|0.3627|± |0.0347|
|hendrycksTest-high_school_macroeconomics | 1|acc |0.2641|± |0.0224|
| | |acc_norm|0.2641|± |0.0224|
|hendrycksTest-high_school_mathematics | 1|acc |0.2630|± |0.0268|
| | |acc_norm|0.2630|± |0.0268|
|hendrycksTest-high_school_microeconomics | 1|acc |0.3403|± |0.0308|
| | |acc_norm|0.3403|± |0.0308|
|hendrycksTest-high_school_physics | 1|acc |0.3113|± |0.0378|
| | |acc_norm|0.3113|± |0.0378|
|hendrycksTest-high_school_psychology | 1|acc |0.2716|± |0.0191|
| | |acc_norm|0.2716|± |0.0191|
|hendrycksTest-high_school_statistics | 1|acc |0.4491|± |0.0339|
| | |acc_norm|0.4491|± |0.0339|
|hendrycksTest-high_school_us_history | 1|acc |0.2402|± |0.0300|
| | |acc_norm|0.2402|± |0.0300|
|hendrycksTest-high_school_world_history | 1|acc |0.2363|± |0.0277|
| | |acc_norm|0.2363|± |0.0277|
|hendrycksTest-human_aging | 1|acc |0.2197|± |0.0278|
| | |acc_norm|0.2197|± |0.0278|
|hendrycksTest-human_sexuality | 1|acc |0.2824|± |0.0395|
| | |acc_norm|0.2824|± |0.0395|
|hendrycksTest-international_law | 1|acc |0.2479|± |0.0394|
| | |acc_norm|0.2479|± |0.0394|
|hendrycksTest-jurisprudence | 1|acc |0.2037|± |0.0389|
| | |acc_norm|0.2037|± |0.0389|
|hendrycksTest-logical_fallacies | 1|acc |0.2393|± |0.0335|
| | |acc_norm|0.2393|± |0.0335|
|hendrycksTest-machine_learning | 1|acc |0.1875|± |0.0370|
| | |acc_norm|0.1875|± |0.0370|
|hendrycksTest-management | 1|acc |0.2039|± |0.0399|
| | |acc_norm|0.2039|± |0.0399|
|hendrycksTest-marketing | 1|acc |0.1795|± |0.0251|
| | |acc_norm|0.1795|± |0.0251|
|hendrycksTest-medical_genetics | 1|acc |0.3000|± |0.0461|
| | |acc_norm|0.3000|± |0.0461|
|hendrycksTest-miscellaneous | 1|acc |0.2644|± |0.0158|
| | |acc_norm|0.2644|± |0.0158|
|hendrycksTest-moral_disputes | 1|acc |0.2225|± |0.0224|
| | |acc_norm|0.2225|± |0.0224|
|hendrycksTest-moral_scenarios | 1|acc |0.2726|± |0.0149|
| | |acc_norm|0.2726|± |0.0149|
|hendrycksTest-nutrition | 1|acc |0.2353|± |0.0243|
| | |acc_norm|0.2353|± |0.0243|
|hendrycksTest-philosophy | 1|acc |0.2283|± |0.0238|
| | |acc_norm|0.2283|± |0.0238|
|hendrycksTest-prehistory | 1|acc |0.2099|± |0.0227|
| | |acc_norm|0.2099|± |0.0227|
|hendrycksTest-professional_accounting | 1|acc |0.2411|± |0.0255|
| | |acc_norm|0.2411|± |0.0255|
|hendrycksTest-professional_law | 1|acc |0.2458|± |0.0110|
| | |acc_norm|0.2458|± |0.0110|
|hendrycksTest-professional_medicine | 1|acc |0.3897|± |0.0296|
| | |acc_norm|0.3897|± |0.0296|
|hendrycksTest-professional_psychology | 1|acc |0.2141|± |0.0166|
| | |acc_norm|0.2141|± |0.0166|
|hendrycksTest-public_relations | 1|acc |0.1818|± |0.0369|
| | |acc_norm|0.1818|± |0.0369|
|hendrycksTest-security_studies | 1|acc |0.2490|± |0.0277|
| | |acc_norm|0.2490|± |0.0277|
|hendrycksTest-sociology | 1|acc |0.2537|± |0.0308|
| | |acc_norm|0.2537|± |0.0308|
|hendrycksTest-us_foreign_policy | 1|acc |0.2900|± |0.0456|
| | |acc_norm|0.2900|± |0.0456|
|hendrycksTest-virology | 1|acc |0.1807|± |0.0300|
| | |acc_norm|0.1807|± |0.0300|
|hendrycksTest-world_religions | 1|acc |0.1813|± |0.0295|
| | |acc_norm|0.1813|± |0.0295|
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 8
| Task |Version|Metric|Value | |Stderr|
|----------|------:|------|-----:|---|-----:|
|winogrande| 0|acc |0.5154|± | 0.014|
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 8
|Task |Version|Metric|Value| |Stderr|
|-----|------:|------|----:|---|-----:|
|gsm8k| 0|acc | 0|± | 0|
|