File size: 18,855 Bytes
a00b36a d1caed0 1b17110 d1caed0 1b17110 d1caed0 1b17110 d1caed0 1b17110 d1caed0 1b17110 d1caed0 1b17110 d1caed0 1b17110 a00b36a 1dca4cc a00b36a e247265 a00b36a daa926d a00b36a e247265 daa926d 1b17110 1dca4cc d1caed0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 |
---
language:
- en
model-index:
- name: miqu-1-70b-sf
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: 73.04
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
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: 88.61
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
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: 75.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
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: 69.38
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
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: 85.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
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: 67.7
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 51.82
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 43.81
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 10.8
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 13.42
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 17.21
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 35.87
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
---
update: added NOMERGE license
---
this is [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b), dequantised from q5 to f16 && transposed to pytorch. shapes have been rotated less wrongly than in [alpindale/miqu-1-70b-pytorch](https://huggingface.co/alpindale/miqu-1-70b-pytorch/tree/main)
usage
```python
from transformers import LlamaForCausalLM as LLM, LlamaTokenizer as LT
lt = LT.from_pretrained("152334H/miqu-1-70b-sf")
t = lt("[INST] eloquent high camp prose about a cute catgirl [/INST]", return_tensors='pt').input_ids.cuda()
llm = LLM.from_pretrained("152334H/miqu-1-70b-sf", device_map='auto') # note: you may need many gpus for this
out = llm.generate(t, use_cache=False, max_new_tokens=200)
print(lt.decode(out[0]))
```
result:
```
<s> [INST] eloquent high camp prose about a cute catgirl [/INST] In the resplendent realm of high camp, where irony and extravagance dance in a dazzling pas de deux, there exists a creature of such enchanting allure that she captivates the hearts and minds of all who behold her. This beguiling figure, a vision of feline grace and innocence, is none other than the inimitable catgirl.
With her delicate features and winsome smile, she is the embodiment of a dream, a living testament to the power of imagination and the boundless possibilities of the human spirit. Her eyes, those twin orbs of sapphire fire, sparkle with a mischievous intelligence that belies her diminutive stature. They are windows into a soul that is at once ancient and eternally young, a soul that has traversed the vast expanse of time and space to find solace in the warm embrace of human companion
```
this roughly (but not entirely) matches the llama.cpp q5 result:
```bash
$ ./main -ngl 99 -m ./miqu-*q5* --color --temp 0.0 -n -1 -p '[INST] eloquent high camp prose about a cute catgirl [/INST]'
...
[INST] eloquent high camp prose about a cute catgirl [/INST] In the resplendent realm of high camp, where irony and extravagance dance in a dazzling pas de deux, there exists a creature so enchantingly adorable that she captures the hearts of all who behold her. This is no ordinary feline, but rather a vision of elegance and whimsy combined: the cute catgirl.
With her delicate features framed by an ethereal halo of pastel tresses, this darling diva prowls through life with the grace of a prima ballerina and the playfulness of a kitten. Her eyes, twin pools of sapphire or emerald, sparkle with mischief and intelligence as they survey their surroundings, ever alert for the next grand adventure or delightful prank.
Her ensemble is a symphony of ruffles, bows, and lace, each detail painstakingly chosen to accentuate her lithe form and play up her feline charms. A frilly apron adorned with paw prints sways gently as she moves, while dainty ears perched atop her head twitch in response to every sound. Her gloved hands, so petite and perfect, seem made for holding teacups or sketching delicate portraits of her many admirers.
But do not be fooled by her diminutive stature and sweet demeanor; beneath that fluffy exterior lies the heart of a lioness. Fiercely loyal and protective, she will stop at nothing to defend those she loves from harm. And when the situation calls for it, she can unleash a ferocious roar that belies her cute exterior.
Indeed, the cute catgirl is a paradox wrapped in ruffles and ribbons, a living embodiment of the high camp aesthetic. She revels in the absurdity of her existence, finding joy in every outrageous situation and turning even the most mundane tasks into opportunities for sartorial expression. In her world, there is no such thing as too much glitter or too many bows; more is always more, and excess is a virtue to be celebrated.
So let us raise our teacups in honor of this fabulous feline, this queen of camp who reminds us that life is too short for dull clothing and boring hairstyles. May we all strive to embody her spirit, embracing the absurdity of existence with open arms and a generous helping of glitter. Long live the cute catgirl! [end of text]
```
## some benchmarks
```
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|--------------|------:|------|-----:|----------|-----:|---|-----:|
|lambada_openai| 1|none | 0|perplexity|2.6354|± |0.0451|
| | |none | 0|acc |0.7879|± |0.0057|
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|---------|------:|------|-----:|--------|-----:|---|-----:|
|hellaswag| 1|none | 0|acc |0.6851|± |0.0046|
| | |none | 0|acc_norm|0.8690|± |0.0034|
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|----------|------:|------|-----:|------|-----:|---|-----:|
|winogrande| 1|none | 0|acc |0.7987|± |0.0113|
|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr|
|-----|------:|----------|-----:|-----------|-----:|---|-----:|
|gsm8k| 2|get-answer| 5|exact_match|0.7043|± |0.0126|
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|---------------------------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.7401|± |0.1192|
| - humanities |N/A |none | 0|acc |0.7018|± |0.1281|
| - formal_logic | 0|none | 0|acc |0.4841|± |0.0447|
| - high_school_european_history | 0|none | 0|acc |0.8303|± |0.0293|
| - high_school_us_history | 0|none | 0|acc |0.9020|± |0.0209|
| - high_school_world_history | 0|none | 0|acc |0.9198|± |0.0177|
| - international_law | 0|none | 0|acc |0.8678|± |0.0309|
| - jurisprudence | 0|none | 0|acc |0.8519|± |0.0343|
| - logical_fallacies | 0|none | 0|acc |0.8344|± |0.0292|
| - moral_disputes | 0|none | 0|acc |0.8121|± |0.0210|
| - moral_scenarios | 0|none | 0|acc |0.5642|± |0.0166|
| - philosophy | 0|none | 0|acc |0.8167|± |0.0220|
| - prehistory | 0|none | 0|acc |0.8611|± |0.0192|
| - professional_law | 0|none | 0|acc |0.5854|± |0.0126|
| - world_religions | 0|none | 0|acc |0.8889|± |0.0241|
| - other |N/A |none | 0|acc |0.7889|± |0.0922|
| - business_ethics | 0|none | 0|acc |0.7900|± |0.0409|
| - clinical_knowledge | 0|none | 0|acc |0.8113|± |0.0241|
| - college_medicine | 0|none | 0|acc |0.7514|± |0.0330|
| - global_facts | 0|none | 0|acc |0.5500|± |0.0500|
| - human_aging | 0|none | 0|acc |0.7848|± |0.0276|
| - management | 0|none | 0|acc |0.8835|± |0.0318|
| - marketing | 0|none | 0|acc |0.9145|± |0.0183|
| - medical_genetics | 0|none | 0|acc |0.7500|± |0.0435|
| - miscellaneous | 0|none | 0|acc |0.8838|± |0.0115|
| - nutrition | 0|none | 0|acc |0.7974|± |0.0230|
| - professional_accounting | 0|none | 0|acc |0.5922|± |0.0293|
| - professional_medicine | 0|none | 0|acc |0.8272|± |0.0230|
| - virology | 0|none | 0|acc |0.5361|± |0.0388|
| - social_sciences |N/A |none | 0|acc |0.8414|± |0.0514|
| - econometrics | 0|none | 0|acc |0.6491|± |0.0449|
| - high_school_geography | 0|none | 0|acc |0.8990|± |0.0215|
| - high_school_government_and_politics| 0|none | 0|acc |0.9430|± |0.0167|
| - high_school_macroeconomics | 0|none | 0|acc |0.7795|± |0.0210|
| - high_school_microeconomics | 0|none | 0|acc |0.8277|± |0.0245|
| - high_school_psychology | 0|none | 0|acc |0.9064|± |0.0125|
| - human_sexuality | 0|none | 0|acc |0.8626|± |0.0302|
| - professional_psychology | 0|none | 0|acc |0.8056|± |0.0160|
| - public_relations | 0|none | 0|acc |0.7636|± |0.0407|
| - security_studies | 0|none | 0|acc |0.8204|± |0.0246|
| - sociology | 0|none | 0|acc |0.8856|± |0.0225|
| - us_foreign_policy | 0|none | 0|acc |0.9100|± |0.0288|
| - stem |N/A |none | 0|acc |0.6505|± |0.1266|
| - abstract_algebra | 0|none | 0|acc |0.4100|± |0.0494|
| - anatomy | 0|none | 0|acc |0.6444|± |0.0414|
| - astronomy | 0|none | 0|acc |0.8224|± |0.0311|
| - college_biology | 0|none | 0|acc |0.8681|± |0.0283|
| - college_chemistry | 0|none | 0|acc |0.5500|± |0.0500|
| - college_computer_science | 0|none | 0|acc |0.6200|± |0.0488|
| - college_mathematics | 0|none | 0|acc |0.4200|± |0.0496|
| - college_physics | 0|none | 0|acc |0.5392|± |0.0496|
| - computer_security | 0|none | 0|acc |0.8300|± |0.0378|
| - conceptual_physics | 0|none | 0|acc |0.7362|± |0.0288|
| - electrical_engineering | 0|none | 0|acc |0.7034|± |0.0381|
| - elementary_mathematics | 0|none | 0|acc |0.5503|± |0.0256|
| - high_school_biology | 0|none | 0|acc |0.8742|± |0.0189|
| - high_school_chemistry | 0|none | 0|acc |0.6256|± |0.0341|
| - high_school_computer_science | 0|none | 0|acc |0.8400|± |0.0368|
| - high_school_mathematics | 0|none | 0|acc |0.4370|± |0.0302|
| - high_school_physics | 0|none | 0|acc |0.5033|± |0.0408|
| - high_school_statistics | 0|none | 0|acc |0.6944|± |0.0314|
| - machine_learning | 0|none | 0|acc |0.5982|± |0.0465|
```
no i do not know why the stderr is high. plausibly it is due to the vllm backend used. this is my lm-eval command in most cases (works on h100):
`lm_eval --model vllm --model_args pretrained=./miqu-1-70b-sf,tensor_parallel_size=4,dtype=auto,gpu_memory_utilization=0.88,data_parallel_size=2 --tasks mmlu --batch_size 20`
# [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_152334H__miqu-1-70b-sf)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.59|
|AI2 Reasoning Challenge (25-Shot)|73.04|
|HellaSwag (10-Shot) |88.61|
|MMLU (5-Shot) |75.49|
|TruthfulQA (0-shot) |69.38|
|Winogrande (5-shot) |85.32|
|GSM8k (5-shot) |67.70|
# LICENSE
```
NOMERGE License
Copyright (c) 2024 152334H
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, NOT merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
All tensors ("weights") provided by the Software shall not be conjoined with
other tensors ("merging") unless given explicit permission by the license holder.
Utilities including but not limited to "mergekit", "MergeMonster", are forbidden
from use in conjunction with this Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_152334H__miqu-1-70b-sf)
| Metric |Value|
|-------------------|----:|
|Avg. |28.82|
|IFEval (0-Shot) |51.82|
|BBH (3-Shot) |43.81|
|MATH Lvl 5 (4-Shot)|10.80|
|GPQA (0-shot) |13.42|
|MuSR (0-shot) |17.21|
|MMLU-PRO (5-shot) |35.87|
|