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1 |
+
---
|
2 |
+
language:
|
3 |
+
- en
|
4 |
+
license: mit
|
5 |
+
library_name: transformers
|
6 |
+
inference:
|
7 |
+
parameters:
|
8 |
+
max_new_tokens: 64
|
9 |
+
do_sample: true
|
10 |
+
temperature: 0.1
|
11 |
+
repetition_penalty: 10
|
12 |
+
no_repeat_ngram_size: 4
|
13 |
+
eta_cutoff: 0.0006
|
14 |
+
renormalize_logits: true
|
15 |
+
widget:
|
16 |
+
- text: My name is El Microondas the Wise, and
|
17 |
+
example_title: El Microondas
|
18 |
+
- text: Kennesaw State University is a public
|
19 |
+
example_title: Kennesaw State University
|
20 |
+
- text: >-
|
21 |
+
Bungie Studios is an American video game developer. They are most famous for
|
22 |
+
developing the award winning Halo series of video games. They also made
|
23 |
+
Destiny. The studio was founded
|
24 |
+
example_title: Bungie
|
25 |
+
- text: The Mona Lisa is a world-renowned painting created by
|
26 |
+
example_title: Mona Lisa
|
27 |
+
- text: >-
|
28 |
+
The Harry Potter series, written by J.K. Rowling, begins with the book
|
29 |
+
titled
|
30 |
+
example_title: Harry Potter Series
|
31 |
+
- text: >-
|
32 |
+
Question: I have cities, but no houses. I have mountains, but no trees. I
|
33 |
+
have water, but no fish. What am I?
|
34 |
+
|
35 |
+
Answer:
|
36 |
+
example_title: Riddle
|
37 |
+
- text: The process of photosynthesis involves the conversion of
|
38 |
+
example_title: Photosynthesis
|
39 |
+
- text: >-
|
40 |
+
Jane went to the store to buy some groceries. She picked up apples, oranges,
|
41 |
+
and a loaf of bread. When she got home, she realized she forgot
|
42 |
+
example_title: Story Continuation
|
43 |
+
- text: >-
|
44 |
+
Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and
|
45 |
+
another train leaves Station B at 10:00 AM and travels at 80 mph, when will
|
46 |
+
they meet if the distance between the stations is 300 miles?
|
47 |
+
|
48 |
+
To determine
|
49 |
+
example_title: Math Problem
|
50 |
+
- text: In the context of computer programming, an algorithm is
|
51 |
+
example_title: Algorithm Definition
|
52 |
+
pipeline_tag: text-generation
|
53 |
+
model-index:
|
54 |
+
- name: nano-phi-115M-v0.1
|
55 |
+
results:
|
56 |
+
- task:
|
57 |
+
type: text-generation
|
58 |
+
name: Text Generation
|
59 |
+
dataset:
|
60 |
+
name: AI2 Reasoning Challenge (25-Shot)
|
61 |
+
type: ai2_arc
|
62 |
+
config: ARC-Challenge
|
63 |
+
split: test
|
64 |
+
args:
|
65 |
+
num_few_shot: 25
|
66 |
+
metrics:
|
67 |
+
- type: acc_norm
|
68 |
+
value: 24.15
|
69 |
+
name: normalized accuracy
|
70 |
+
source:
|
71 |
+
url: >-
|
72 |
+
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
|
73 |
+
name: Open LLM Leaderboard
|
74 |
+
- task:
|
75 |
+
type: text-generation
|
76 |
+
name: Text Generation
|
77 |
+
dataset:
|
78 |
+
name: HellaSwag (10-Shot)
|
79 |
+
type: hellaswag
|
80 |
+
split: validation
|
81 |
+
args:
|
82 |
+
num_few_shot: 10
|
83 |
+
metrics:
|
84 |
+
- type: acc_norm
|
85 |
+
value: 29.99
|
86 |
+
name: normalized accuracy
|
87 |
+
source:
|
88 |
+
url: >-
|
89 |
+
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
|
90 |
+
name: Open LLM Leaderboard
|
91 |
+
- task:
|
92 |
+
type: text-generation
|
93 |
+
name: Text Generation
|
94 |
+
dataset:
|
95 |
+
name: MMLU (5-Shot)
|
96 |
+
type: cais/mmlu
|
97 |
+
config: all
|
98 |
+
split: test
|
99 |
+
args:
|
100 |
+
num_few_shot: 5
|
101 |
+
metrics:
|
102 |
+
- type: acc
|
103 |
+
value: 25.46
|
104 |
+
name: accuracy
|
105 |
+
source:
|
106 |
+
url: >-
|
107 |
+
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
|
108 |
+
name: Open LLM Leaderboard
|
109 |
+
- task:
|
110 |
+
type: text-generation
|
111 |
+
name: Text Generation
|
112 |
+
dataset:
|
113 |
+
name: TruthfulQA (0-shot)
|
114 |
+
type: truthful_qa
|
115 |
+
config: multiple_choice
|
116 |
+
split: validation
|
117 |
+
args:
|
118 |
+
num_few_shot: 0
|
119 |
+
metrics:
|
120 |
+
- type: mc2
|
121 |
+
value: 44.3
|
122 |
+
source:
|
123 |
+
url: >-
|
124 |
+
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
|
125 |
+
name: Open LLM Leaderboard
|
126 |
+
- task:
|
127 |
+
type: text-generation
|
128 |
+
name: Text Generation
|
129 |
+
dataset:
|
130 |
+
name: Winogrande (5-shot)
|
131 |
+
type: winogrande
|
132 |
+
config: winogrande_xl
|
133 |
+
split: validation
|
134 |
+
args:
|
135 |
+
num_few_shot: 5
|
136 |
+
metrics:
|
137 |
+
- type: acc
|
138 |
+
value: 51.45
|
139 |
+
name: accuracy
|
140 |
+
source:
|
141 |
+
url: >-
|
142 |
+
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
|
143 |
+
name: Open LLM Leaderboard
|
144 |
+
- task:
|
145 |
+
type: text-generation
|
146 |
+
name: Text Generation
|
147 |
+
dataset:
|
148 |
+
name: GSM8k (5-shot)
|
149 |
+
type: gsm8k
|
150 |
+
config: main
|
151 |
+
split: test
|
152 |
+
args:
|
153 |
+
num_few_shot: 5
|
154 |
+
metrics:
|
155 |
+
- type: acc
|
156 |
+
value: 0
|
157 |
+
name: accuracy
|
158 |
+
source:
|
159 |
+
url: >-
|
160 |
+
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
|
161 |
+
name: Open LLM Leaderboard
|
162 |
+
datasets:
|
163 |
+
- kenhktsui/minipile_quality_score_v1
|
164 |
+
- kenhktsui/simple_wikipedia_LM_quality_score_v1
|
165 |
+
- kenhktsui/refinedweb-3m_quality_score_v1
|
166 |
+
- kenhktsui/TM-DATA_quality_score_v1
|
167 |
+
- kenhktsui/openwebtext_quality_score_v1
|
168 |
+
- HuggingFaceTB/cosmopedia
|
169 |
+
---
|
170 |
+
|
171 |
+
|
172 |
+
# Model Card for nano-phi-192M-v0.1
|
173 |
+
This is a continual effort from [kenhktsui/nano-phi-115M-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-v0.1).
|
174 |
+
The model is not aligned.
|
175 |
+
|
176 |
+
Major differences:
|
177 |
+
- bigger tokenizer's vocab size
|
178 |
+
- addition of [HuggingFaceTB/cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) as training dataset
|
179 |
+
- training token: 19B vs 7B
|
180 |
+
|
181 |
+
|
182 |
+
## How to use
|
183 |
+
To use the model, you will need transformer version >= 4.37.2
|
184 |
+
```
|
185 |
+
pip install transformers>=4.37.2
|
186 |
+
```
|
187 |
+
|
188 |
+
```
|
189 |
+
# Use a pipeline as a high-level helper
|
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+
from transformers import pipeline
|
191 |
+
|
192 |
+
pipe = pipeline("text-generation", model="kenhktsui/nano-phi-192M-v0.1")
|
193 |
+
pipe("I am a machine learning researcher. I work on", max_new_tokens=50, repetition_penalty=10.0)
|
194 |
+
```
|
195 |
+
|
196 |
+
## Some metrics
|
197 |
+
- model
|
198 |
+
- hidden_size: 768
|
199 |
+
- num_key_value_heads: 8 (grouped query attention)
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200 |
+
- num_attention_heads: 24
|
201 |
+
- num_hidden_layers: 6
|
202 |
+
- context length: 1024
|
203 |
+
- total params: 192M
|
204 |
+
- training:
|
205 |
+
- global steps: 36,000
|
206 |
+
|
207 |
+
|
208 |
+
|
209 |
+
## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|
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+
|
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+
|
212 |
+
| 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)|
|
213 |
+
|-----------------------|---------------------------|---------------------------|---------------------------|
|
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+
| Avg. |29.24 | 28.68 |61.53 |
|
215 |
+
| ARC (25-shot) |24.15 | 21.93 |61.52 |
|
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+
| HellaSwag (10-shot) | 29.99 | 27.87 |75.13 |
|
217 |
+
| MMLU (5-shot) |25.46 | 25.30 |58.23 |
|
218 |
+
| TruthfulQA (0-shot) |44.30 | 46.01 |44.46 |
|
219 |
+
| Winogrande (5-shot) |51.54 | 50.99 |74.51 |
|
220 |
+
| GSM8K (5-shot) |0.0 | 0.0 |55.34 |
|
221 |
+
|
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+
Details:
|
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+
|
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+
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
|
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+
| Task |Version| Metric |Value | |Stderr|
|
226 |
+
|--------|------:|--------|-----:|---|-----:|
|
227 |
+
|arc_easy| 0|acc |0.4596|± |0.0102|
|
228 |
+
| | |acc_norm|0.4070|± |0.0101|
|
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+
|
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+
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
|
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+
| Task |Version| Metric |Value | |Stderr|
|
232 |
+
|-------------|------:|--------|-----:|---|-----:|
|
233 |
+
|arc_challenge| 0|acc |0.1911|± |0.0115|
|
234 |
+
| | |acc_norm|0.2415|± |0.0125|
|
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+
|
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+
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
|
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+
| Task |Version| Metric |Value | |Stderr|
|
238 |
+
|---------|------:|--------|-----:|---|-----:|
|
239 |
+
|hellaswag| 0|acc |0.2833|± |0.0045|
|
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+
| | |acc_norm|0.2999|± |0.0046|
|
241 |
+
|
242 |
+
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
|
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+
| Task |Version|Metric|Value | |Stderr|
|
244 |
+
|-------------|------:|------|-----:|---|-----:|
|
245 |
+
|truthfulqa_mc| 1|mc1 |0.2583|± |0.0153|
|
246 |
+
| | |mc2 |0.4430|± |0.0152|
|
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+
|
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+
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
|
249 |
+
| Task |Version| Metric |Value | |Stderr|
|
250 |
+
|-------------------------------------------------|------:|--------|-----:|---|-----:|
|
251 |
+
|hendrycksTest-abstract_algebra | 1|acc |0.2200|± |0.0416|
|
252 |
+
| | |acc_norm|0.2200|± |0.0416|
|
253 |
+
|hendrycksTest-anatomy | 1|acc |0.2593|± |0.0379|
|
254 |
+
| | |acc_norm|0.2593|± |0.0379|
|
255 |
+
|hendrycksTest-astronomy | 1|acc |0.1711|± |0.0306|
|
256 |
+
| | |acc_norm|0.1711|± |0.0306|
|
257 |
+
|hendrycksTest-business_ethics | 1|acc |0.2400|± |0.0429|
|
258 |
+
| | |acc_norm|0.2400|± |0.0429|
|
259 |
+
|hendrycksTest-clinical_knowledge | 1|acc |0.2566|± |0.0269|
|
260 |
+
| | |acc_norm|0.2566|± |0.0269|
|
261 |
+
|hendrycksTest-college_biology | 1|acc |0.2639|± |0.0369|
|
262 |
+
| | |acc_norm|0.2639|± |0.0369|
|
263 |
+
|hendrycksTest-college_chemistry | 1|acc |0.1800|± |0.0386|
|
264 |
+
| | |acc_norm|0.1800|± |0.0386|
|
265 |
+
|hendrycksTest-college_computer_science | 1|acc |0.3300|± |0.0473|
|
266 |
+
| | |acc_norm|0.3300|± |0.0473|
|
267 |
+
|hendrycksTest-college_mathematics | 1|acc |0.3000|± |0.0461|
|
268 |
+
| | |acc_norm|0.3000|± |0.0461|
|
269 |
+
|hendrycksTest-college_medicine | 1|acc |0.2023|± |0.0306|
|
270 |
+
| | |acc_norm|0.2023|± |0.0306|
|
271 |
+
|hendrycksTest-college_physics | 1|acc |0.2843|± |0.0449|
|
272 |
+
| | |acc_norm|0.2843|± |0.0449|
|
273 |
+
|hendrycksTest-computer_security | 1|acc |0.2200|± |0.0416|
|
274 |
+
| | |acc_norm|0.2200|± |0.0416|
|
275 |
+
|hendrycksTest-conceptual_physics | 1|acc |0.2511|± |0.0283|
|
276 |
+
| | |acc_norm|0.2511|± |0.0283|
|
277 |
+
|hendrycksTest-econometrics | 1|acc |0.2807|± |0.0423|
|
278 |
+
| | |acc_norm|0.2807|± |0.0423|
|
279 |
+
|hendrycksTest-electrical_engineering | 1|acc |0.2897|± |0.0378|
|
280 |
+
| | |acc_norm|0.2897|± |0.0378|
|
281 |
+
|hendrycksTest-elementary_mathematics | 1|acc |0.2804|± |0.0231|
|
282 |
+
| | |acc_norm|0.2804|± |0.0231|
|
283 |
+
|hendrycksTest-formal_logic | 1|acc |0.2143|± |0.0367|
|
284 |
+
| | |acc_norm|0.2143|± |0.0367|
|
285 |
+
|hendrycksTest-global_facts | 1|acc |0.1700|± |0.0378|
|
286 |
+
| | |acc_norm|0.1700|± |0.0378|
|
287 |
+
|hendrycksTest-high_school_biology | 1|acc |0.3226|± |0.0266|
|
288 |
+
| | |acc_norm|0.3226|± |0.0266|
|
289 |
+
|hendrycksTest-high_school_chemistry | 1|acc |0.2759|± |0.0314|
|
290 |
+
| | |acc_norm|0.2759|± |0.0314|
|
291 |
+
|hendrycksTest-high_school_computer_science | 1|acc |0.2700|± |0.0446|
|
292 |
+
| | |acc_norm|0.2700|± |0.0446|
|
293 |
+
|hendrycksTest-high_school_european_history | 1|acc |0.2606|± |0.0343|
|
294 |
+
| | |acc_norm|0.2606|± |0.0343|
|
295 |
+
|hendrycksTest-high_school_geography | 1|acc |0.3081|± |0.0329|
|
296 |
+
| | |acc_norm|0.3081|± |0.0329|
|
297 |
+
|hendrycksTest-high_school_government_and_politics| 1|acc |0.3627|± |0.0347|
|
298 |
+
| | |acc_norm|0.3627|± |0.0347|
|
299 |
+
|hendrycksTest-high_school_macroeconomics | 1|acc |0.2641|± |0.0224|
|
300 |
+
| | |acc_norm|0.2641|± |0.0224|
|
301 |
+
|hendrycksTest-high_school_mathematics | 1|acc |0.2630|± |0.0268|
|
302 |
+
| | |acc_norm|0.2630|± |0.0268|
|
303 |
+
|hendrycksTest-high_school_microeconomics | 1|acc |0.3403|± |0.0308|
|
304 |
+
| | |acc_norm|0.3403|± |0.0308|
|
305 |
+
|hendrycksTest-high_school_physics | 1|acc |0.3113|± |0.0378|
|
306 |
+
| | |acc_norm|0.3113|± |0.0378|
|
307 |
+
|hendrycksTest-high_school_psychology | 1|acc |0.2716|± |0.0191|
|
308 |
+
| | |acc_norm|0.2716|± |0.0191|
|
309 |
+
|hendrycksTest-high_school_statistics | 1|acc |0.4491|± |0.0339|
|
310 |
+
| | |acc_norm|0.4491|± |0.0339|
|
311 |
+
|hendrycksTest-high_school_us_history | 1|acc |0.2402|± |0.0300|
|
312 |
+
| | |acc_norm|0.2402|± |0.0300|
|
313 |
+
|hendrycksTest-high_school_world_history | 1|acc |0.2363|± |0.0277|
|
314 |
+
| | |acc_norm|0.2363|± |0.0277|
|
315 |
+
|hendrycksTest-human_aging | 1|acc |0.2197|± |0.0278|
|
316 |
+
| | |acc_norm|0.2197|± |0.0278|
|
317 |
+
|hendrycksTest-human_sexuality | 1|acc |0.2824|± |0.0395|
|
318 |
+
| | |acc_norm|0.2824|± |0.0395|
|
319 |
+
|hendrycksTest-international_law | 1|acc |0.2479|± |0.0394|
|
320 |
+
| | |acc_norm|0.2479|± |0.0394|
|
321 |
+
|hendrycksTest-jurisprudence | 1|acc |0.2037|± |0.0389|
|
322 |
+
| | |acc_norm|0.2037|± |0.0389|
|
323 |
+
|hendrycksTest-logical_fallacies | 1|acc |0.2393|± |0.0335|
|
324 |
+
| | |acc_norm|0.2393|± |0.0335|
|
325 |
+
|hendrycksTest-machine_learning | 1|acc |0.1875|± |0.0370|
|
326 |
+
| | |acc_norm|0.1875|± |0.0370|
|
327 |
+
|hendrycksTest-management | 1|acc |0.2039|± |0.0399|
|
328 |
+
| | |acc_norm|0.2039|± |0.0399|
|
329 |
+
|hendrycksTest-marketing | 1|acc |0.1795|± |0.0251|
|
330 |
+
| | |acc_norm|0.1795|± |0.0251|
|
331 |
+
|hendrycksTest-medical_genetics | 1|acc |0.3000|± |0.0461|
|
332 |
+
| | |acc_norm|0.3000|± |0.0461|
|
333 |
+
|hendrycksTest-miscellaneous | 1|acc |0.2644|± |0.0158|
|
334 |
+
| | |acc_norm|0.2644|± |0.0158|
|
335 |
+
|hendrycksTest-moral_disputes | 1|acc |0.2225|± |0.0224|
|
336 |
+
| | |acc_norm|0.2225|± |0.0224|
|
337 |
+
|hendrycksTest-moral_scenarios | 1|acc |0.2726|± |0.0149|
|
338 |
+
| | |acc_norm|0.2726|± |0.0149|
|
339 |
+
|hendrycksTest-nutrition | 1|acc |0.2353|± |0.0243|
|
340 |
+
| | |acc_norm|0.2353|± |0.0243|
|
341 |
+
|hendrycksTest-philosophy | 1|acc |0.2283|± |0.0238|
|
342 |
+
| | |acc_norm|0.2283|± |0.0238|
|
343 |
+
|hendrycksTest-prehistory | 1|acc |0.2099|± |0.0227|
|
344 |
+
| | |acc_norm|0.2099|± |0.0227|
|
345 |
+
|hendrycksTest-professional_accounting | 1|acc |0.2411|± |0.0255|
|
346 |
+
| | |acc_norm|0.2411|± |0.0255|
|
347 |
+
|hendrycksTest-professional_law | 1|acc |0.2458|± |0.0110|
|
348 |
+
| | |acc_norm|0.2458|± |0.0110|
|
349 |
+
|hendrycksTest-professional_medicine | 1|acc |0.3897|± |0.0296|
|
350 |
+
| | |acc_norm|0.3897|± |0.0296|
|
351 |
+
|hendrycksTest-professional_psychology | 1|acc |0.2141|± |0.0166|
|
352 |
+
| | |acc_norm|0.2141|± |0.0166|
|
353 |
+
|hendrycksTest-public_relations | 1|acc |0.1818|± |0.0369|
|
354 |
+
| | |acc_norm|0.1818|± |0.0369|
|
355 |
+
|hendrycksTest-security_studies | 1|acc |0.2490|± |0.0277|
|
356 |
+
| | |acc_norm|0.2490|± |0.0277|
|
357 |
+
|hendrycksTest-sociology | 1|acc |0.2537|± |0.0308|
|
358 |
+
| | |acc_norm|0.2537|± |0.0308|
|
359 |
+
|hendrycksTest-us_foreign_policy | 1|acc |0.2900|± |0.0456|
|
360 |
+
| | |acc_norm|0.2900|± |0.0456|
|
361 |
+
|hendrycksTest-virology | 1|acc |0.1807|± |0.0300|
|
362 |
+
| | |acc_norm|0.1807|± |0.0300|
|
363 |
+
|hendrycksTest-world_religions | 1|acc |0.1813|± |0.0295|
|
364 |
+
| | |acc_norm|0.1813|± |0.0295|
|
365 |
+
|
366 |
+
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
|
367 |
+
| Task |Version|Metric|Value | |Stderr|
|
368 |
+
|----------|------:|------|-----:|---|-----:|
|
369 |
+
|winogrande| 0|acc |0.5154|± | 0.014|
|
370 |
+
|
371 |
+
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
|
372 |
+
|Task |Version|Metric|Value| |Stderr|
|
373 |
+
|-----|------:|------|----:|---|-----:|
|
374 |
+
|gsm8k| 0|acc | 0|± | 0|
|