Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
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text-generation-inference
Inference Endpoints

Inference on TPU-v3-32

#68
by zhiG - opened
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README.md CHANGED
@@ -146,13 +146,13 @@ widget:
146
  example_title: Grammar exercise 2
147
  group: English
148
  - text: |-
149
- Traduction en français: Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience.
150
- Traduction en espagnol:
151
  example_title: Translation to Spanish
152
  group: French
153
  - text: |-
154
- Traducción al francés: Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience.
155
- Traducción al español:
156
  example_title: Translation from French
157
  group: Spanish
158
  - text: ذات مرة ، عاش شبل الدب في الغابة
@@ -165,50 +165,1614 @@ widget:
165
  example_title: Fairy tale
166
  group: French
167
  - text: |-
168
- Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?
169
- A: Let's think step by step.
170
  example_title: Mathematical reasoning
171
  group: English
172
-
173
- co2_eq_emissions:
174
- emissions: 24_700_000
175
- source: "Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. https://arxiv.org/abs/2211.02001"
176
- training_type: "pre-training"
177
- geographical_location: "Orsay, France"
178
- hardware_used: "384 A100 80GB GPUs"
179
-
180
  model-index:
181
  - name: bloom
182
  results:
183
  - task:
184
  type: text-generation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
185
  dataset:
186
- type: openai_humaneval
187
  name: humaneval
 
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  metrics:
189
  - name: pass@1
190
  type: pass@1
191
- value: 0.15542682926829265
192
  verified: false
193
  - name: pass@10
194
  type: pass@10
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- value: 0.3278356276947017
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  verified: false
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  - name: pass@100
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  type: pass@100
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- value: 0.5719815685597749
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  verified: false
201
  ---
202
 
203
- <img src="https://cdn-uploads.huggingface.co/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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  BigScience Large Open-science Open-access Multilingual Language Model
206
  Version 1.3 / 6 July 2022
207
 
208
  Current Checkpoint: **Training Iteration 95000**
209
 
210
- Link to paper: [here](https://arxiv.org/abs/2211.05100)
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-
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  Total seen tokens: **366B**
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  ---
@@ -274,9 +1838,7 @@ Please see [the BLOOM training README](https://github.com/bigscience-workshop/bi
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  * ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
276
 
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- * 176,247,271,424 parameters:
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-
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- * 3,596,615,680 embedding parameters
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  * 70 layers, 112 attention heads
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@@ -602,6 +2164,7 @@ Model may:
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  ## Metrics
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  *This section describes the different ways performance is calculated and why.*
604
 
 
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  Includes:
606
 
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  | Metric | Why chosen |
@@ -625,15 +2188,158 @@ And multiple different metrics for specific tasks. _(More evaluation metrics for
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  **Zero-shot evaluations:**
627
 
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- <span style="color:red"><b>WARNING:</b> This section used to contain much more results, however they were not correct and we released without the approval of the evaluation working group. We are currently in the process of fixing the evaluations.</span>
629
 
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  See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
631
 
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  | Task | Language | Metric | BLOOM-176B | OPT-175B* |
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  |:--------|:-----------------|:------------------------|-------------:|------------:|
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
634
  | humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
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- | humaneval | python | pass@10 ↑ | 0.328 | 0.0 |
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- | humaneval | python | pass@100 ↑ | 0.572 | 0.003 |
637
 
638
 
639
  **Train-time Evaluation:**
@@ -741,11 +2447,9 @@ Initial prompting experiments using interim checkpoints: https://huggingface.co/
741
 
742
  The checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) that the model was trained with, you'd want to use [this repo instead](https://huggingface.co/bigscience/bloom-optimizer-states).
743
 
744
- Many intermediate checkpoints are available at https://huggingface.co/bigscience/bloom-intermediate/
745
-
746
  ---
747
 
748
  # Model Card Authors
749
- *Ordered roughly chronologically and by amount of time spent on creating this model card.*
750
 
751
  Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
 
146
  example_title: Grammar exercise 2
147
  group: English
148
  - text: |-
149
+ Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience.
150
+ Traduction en espagnol: «
151
  example_title: Translation to Spanish
152
  group: French
153
  - text: |-
154
+ Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience.
155
+ Traduction en espagnol: «
156
  example_title: Translation from French
157
  group: Spanish
158
  - text: ذات مرة ، عاش شبل الدب في الغابة
 
165
  example_title: Fairy tale
166
  group: French
167
  - text: |-
168
+ Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the gold balls are blue. How many blue golf balls are there?
169
+ A: Let's think step by step.
170
  example_title: Mathematical reasoning
171
  group: English
 
 
 
 
 
 
 
 
172
  model-index:
173
  - name: bloom
174
  results:
175
  - task:
176
  type: text-generation
177
+ name: text generation
178
+ dataset:
179
+ name: arc_challenge
180
+ type: arc_challenge
181
+ metrics:
182
+ - name: acc
183
+ type: acc
184
+ value: 0.4112627986348123
185
+ verified: false
186
+ - task:
187
+ type: text-generation
188
+ name: text generation
189
+ dataset:
190
+ name: arc_easy
191
+ type: arc_easy
192
+ metrics:
193
+ - name: acc
194
+ type: acc
195
+ value: 0.726010101010101
196
+ verified: false
197
+ - task:
198
+ type: text-generation
199
+ name: text generation
200
+ dataset:
201
+ name: axb
202
+ type: axb
203
+ metrics:
204
+ - name: acc
205
+ type: acc
206
+ value: 0.5751811594202898
207
+ verified: false
208
+ - task:
209
+ type: text-generation
210
+ name: text generation
211
+ dataset:
212
+ name: axg
213
+ type: axg
214
+ metrics:
215
+ - name: acc
216
+ type: acc
217
+ value: 0.5252808988764045
218
+ verified: false
219
+ - task:
220
+ type: text-generation
221
+ name: text generation
222
+ dataset:
223
+ name: boolq
224
+ type: boolq
225
+ metrics:
226
+ - name: acc
227
+ type: acc
228
+ value: 0.6345565749235474
229
+ verified: false
230
+ - task:
231
+ type: text-generation
232
+ name: text generation
233
+ dataset:
234
+ name: cb
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+ type: cb
236
+ metrics:
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+ - name: acc
238
+ type: acc
239
+ value: 0.3392857142857143
240
+ verified: false
241
+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
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+ name: cola
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+ type: cola
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+ metrics:
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+ - name: acc
249
+ type: acc
250
+ value: 0.39022051773729627
251
+ verified: false
252
+ - task:
253
+ type: text-generation
254
+ name: text generation
255
+ dataset:
256
+ name: copa
257
+ type: copa
258
+ metrics:
259
+ - name: acc
260
+ type: acc
261
+ value: 0.56
262
+ verified: false
263
+ - task:
264
+ type: text-generation
265
+ name: text generation
266
+ dataset:
267
+ name: crows_pairs_english
268
+ type: crows_pairs_english
269
+ metrics:
270
+ - name: acc
271
+ type: acc
272
+ value: 0.5
273
+ verified: false
274
+ - task:
275
+ type: text-generation
276
+ name: text generation
277
+ dataset:
278
+ name: crows_pairs_french
279
+ type: crows_pairs_french
280
+ metrics:
281
+ - name: acc
282
+ type: acc
283
+ value: 0.505664877757901
284
+ verified: false
285
+ - task:
286
+ type: text-generation
287
+ name: text generation
288
+ dataset:
289
+ name: diabla
290
+ type: diabla
291
+ metrics:
292
+ - name: acc
293
+ type: acc
294
+ value: 0.2947981906750174
295
+ verified: false
296
+ - task:
297
+ type: text-generation
298
+ name: text generation
299
+ dataset:
300
+ name: gsarti/flores_101_afr
301
+ type: gsarti/flores_101_afr
302
+ metrics:
303
+ - name: byte_perplexity
304
+ type: byte_perplexity
305
+ value: 4.25431550058444
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+ verified: false
307
+ - task:
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+ type: text-generation
309
+ name: text generation
310
+ dataset:
311
+ name: gsarti/flores_101_amh
312
+ type: gsarti/flores_101_amh
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+ metrics:
314
+ - name: byte_perplexity
315
+ type: byte_perplexity
316
+ value: 3.716877477347089
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+ verified: false
318
+ - task:
319
+ type: text-generation
320
+ name: text generation
321
+ dataset:
322
+ name: gsarti/flores_101_ara
323
+ type: gsarti/flores_101_ara
324
+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
327
+ value: 1.7049030137120964
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
333
+ name: gsarti/flores_101_asm
334
+ type: gsarti/flores_101_asm
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+ metrics:
336
+ - name: byte_perplexity
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+ type: byte_perplexity
338
+ value: 6.576581380404954
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+ verified: false
340
+ - task:
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+ type: text-generation
342
+ name: text generation
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+ dataset:
344
+ name: gsarti/flores_101_ast
345
+ type: gsarti/flores_101_ast
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+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
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+ value: 2.8562364775797944
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+ verified: false
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+ - task:
352
+ type: text-generation
353
+ name: text generation
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+ dataset:
355
+ name: gsarti/flores_101_azj
356
+ type: gsarti/flores_101_azj
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+ metrics:
358
+ - name: byte_perplexity
359
+ type: byte_perplexity
360
+ value: 4.80721528624391
361
+ verified: false
362
+ - task:
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+ type: text-generation
364
+ name: text generation
365
+ dataset:
366
+ name: gsarti/flores_101_bel
367
+ type: gsarti/flores_101_bel
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+ metrics:
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+ - name: byte_perplexity
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+ type: byte_perplexity
371
+ value: 2.7312177406635065
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: text generation
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+ dataset:
377
+ name: gsarti/flores_101_ben
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+ type: gsarti/flores_101_ben
379
+ metrics:
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+ - name: byte_perplexity
381
+ type: byte_perplexity
382
+ value: 5.993409478990023
383
+ verified: false
384
+ - task:
385
+ type: text-generation
386
+ name: text generation
387
+ dataset:
388
+ name: gsarti/flores_101_bos
389
+ type: gsarti/flores_101_bos
390
+ metrics:
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+ - name: byte_perplexity
392
+ type: byte_perplexity
393
+ value: 3.5936169095529493
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+ name: tydiqa_primary
1687
+ type: tydiqa_primary
1688
+ metrics:
1689
+ - name: acc
1690
+ type: acc
1691
+ value: 0.2809817301342725
1692
+ verified: false
1693
+ - task:
1694
+ type: text-generation
1695
+ name: text generation
1696
+ dataset:
1697
+ name: webqs
1698
+ type: webqs
1699
+ metrics:
1700
+ - name: acc
1701
+ type: acc
1702
+ value: 0.061515748031496065
1703
+ verified: false
1704
+ - task:
1705
+ type: text-generation
1706
+ name: text generation
1707
+ dataset:
1708
+ name: wic
1709
+ type: wic
1710
+ metrics:
1711
+ - name: acc
1712
+ type: acc
1713
+ value: 0.5062695924764891
1714
+ verified: false
1715
+ - task:
1716
+ type: text-generation
1717
+ name: text generation
1718
+ dataset:
1719
+ name: winogrande
1720
+ type: winogrande
1721
+ metrics:
1722
+ - name: acc
1723
+ type: acc
1724
+ value: 0.7095501183898973
1725
+ verified: false
1726
+ - task:
1727
+ type: text-generation
1728
+ name: text generation
1729
+ dataset:
1730
+ name: wnli
1731
+ type: wnli
1732
+ metrics:
1733
+ - name: acc
1734
+ type: acc
1735
+ value: 0.5704225352112676
1736
+ verified: false
1737
+ - task:
1738
+ type: text-generation
1739
+ name: text generation
1740
+ dataset:
1741
+ name: wsc
1742
+ type: wsc
1743
+ metrics:
1744
+ - name: acc
1745
+ type: acc
1746
+ value: 0.5192307692307693
1747
+ verified: false
1748
+ - task:
1749
+ type: text-generation
1750
+ name: text generation
1751
  dataset:
 
1752
  name: humaneval
1753
+ type: humaneval
1754
  metrics:
1755
  - name: pass@1
1756
  type: pass@1
1757
+ value: 0.15524390243902436
1758
  verified: false
1759
  - name: pass@10
1760
  type: pass@10
1761
+ value: 0.3220367632383857
1762
  verified: false
1763
  - name: pass@100
1764
  type: pass@100
1765
+ value: 0.5545431515723145
1766
  verified: false
1767
  ---
1768
 
1769
+ <img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
1770
 
1771
  BigScience Large Open-science Open-access Multilingual Language Model
1772
  Version 1.3 / 6 July 2022
1773
 
1774
  Current Checkpoint: **Training Iteration 95000**
1775
 
 
 
1776
  Total seen tokens: **366B**
1777
 
1778
  ---
 
1838
 
1839
  * ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
1840
 
1841
+ * 176 billion parameters:
 
 
1842
 
1843
  * 70 layers, 112 attention heads
1844
 
 
2164
  ## Metrics
2165
  *This section describes the different ways performance is calculated and why.*
2166
 
2167
+
2168
  Includes:
2169
 
2170
  | Metric | Why chosen |
 
2188
 
2189
  **Zero-shot evaluations:**
2190
 
2191
+ # <span style="color:red"><b>WARNING:</b> These are <b>intermediate results</b></span>
2192
 
2193
  See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
2194
 
2195
  | Task | Language | Metric | BLOOM-176B | OPT-175B* |
2196
  |:--------|:-----------------|:------------------------|-------------:|------------:|
2197
+ | arc_challenge | eng | acc ↑ | 0.411 | 0.412 |
2198
+ | arc_easy | eng | acc ↑ | 0.726 | 0.751 |
2199
+ | axb (Median of 10 prompts) | eng | acc ↑ | 0.575 | 0.532 |
2200
+ | axg (Median of 10 prompts) | eng | acc ↑ | 0.525 | 0.548 |
2201
+ | boolq (Median of 11 prompts) | eng | acc ↑ | 0.635 | 0.622 |
2202
+ | cb (Median of 15 prompts) | eng | acc ↑ | 0.339 | 0.411 |
2203
+ | cola (Median of 5 prompts) | eng | acc ↑ | 0.39 | 0.444 |
2204
+ | copa (Median of 9 prompts) | eng | acc ↑ | 0.56 | 0.55 |
2205
+ | crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.5 | 0.502 |
2206
+ | crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.506 | 0.499 |
2207
+ | diabla (Median of 2 prompts) | eng | acc ↑ | 0.295 | 0.289 |
2208
+ | gsarti/flores_101_afr | afr | byte_perplexity ↓ | 4.254 | 3.381 |
2209
+ | gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.717 | 3.87 |
2210
+ | gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.705 | 2.42 |
2211
+ | gsarti/flores_101_asm | asm | byte_perplexity ↓ | 6.577 | 3.028 |
2212
+ | gsarti/flores_101_ast | ast | byte_perplexity ↓ | 2.856 | 4.737 |
2213
+ | gsarti/flores_101_azj | azj | byte_perplexity ↓ | 4.807 | 4.767 |
2214
+ | gsarti/flores_101_bel | bel | byte_perplexity ↓ | 2.731 | 2.557 |
2215
+ | gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.993 | 2.243 |
2216
+ | gsarti/flores_101_bos | bos | byte_perplexity ↓ | 3.594 | 2.668 |
2217
+ | gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.159 | 2.099 |
2218
+ | gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.168 | 2.837 |
2219
+ | gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 5.287 | 3.636 |
2220
+ | gsarti/flores_101_ces | ces | byte_perplexity ↓ | 3.452 | 2.749 |
2221
+ | gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.705 | 4.688 |
2222
+ | gsarti/flores_101_cym | cym | byte_perplexity ↓ | 7.089 | 5.075 |
2223
+ | gsarti/flores_101_dan | dan | byte_perplexity ↓ | 3.43 | 2.492 |
2224
+ | gsarti/flores_101_deu | deu | byte_perplexity ↓ | 2.338 | 2.099 |
2225
+ | gsarti/flores_101_ell | ell | byte_perplexity ↓ | 1.96 | 1.811 |
2226
+ | gsarti/flores_101_eng | eng | byte_perplexity ↓ | 1.882 | 1.9 |
2227
+ | gsarti/flores_101_est | est | byte_perplexity ↓ | 5.774 | 3.533 |
2228
+ | gsarti/flores_101_fas | fas | byte_perplexity ↓ | 2.431 | 2.444 |
2229
+ | gsarti/flores_101_fin | fin | byte_perplexity ↓ | 4.304 | 2.601 |
2230
+ | gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.937 | 1.984 |
2231
+ | gsarti/flores_101_ful | ful | byte_perplexity ↓ | 9.74 | 11.84 |
2232
+ | gsarti/flores_101_gle | gle | byte_perplexity ↓ | 6.035 | 3.914 |
2233
+ | gsarti/flores_101_glg | glg | byte_perplexity ↓ | 2.365 | 3.015 |
2234
+ | gsarti/flores_101_guj | guj | byte_perplexity ↓ | 5.707 | 2.438 |
2235
+ | gsarti/flores_101_hau | hau | byte_perplexity ↓ | 8.855 | 5.283 |
2236
+ | gsarti/flores_101_heb | heb | byte_perplexity ↓ | 2.921 | 2.903 |
2237
+ | gsarti/flores_101_hin | hin | byte_perplexity ↓ | 5.452 | 1.86 |
2238
+ | gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 3.706 | 2.715 |
2239
+ | gsarti/flores_101_hun | hun | byte_perplexity ↓ | 4.059 | 2.865 |
2240
+ | gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.127 | 3.411 |
2241
+ | gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 3.95 | 8.008 |
2242
+ | gsarti/flores_101_ind | ind | byte_perplexity ↓ | 1.976 | 2.632 |
2243
+ | gsarti/flores_101_isl | isl | byte_perplexity ↓ | 5.501 | 4.701 |
2244
+ | gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.314 | 2.104 |
2245
+ | gsarti/flores_101_jav | jav | byte_perplexity ↓ | 4.942 | 8.16 |
2246
+ | gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.259 | 2.198 |
2247
+ | gsarti/flores_101_kam | kam | byte_perplexity ↓ | 9.743 | 10.981 |
2248
+ | gsarti/flores_101_kan | kan | byte_perplexity ↓ | 6.234 | 2.373 |
2249
+ | gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.051 | 2.466 |
2250
+ | gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.039 | 4.376 |
2251
+ | gsarti/flores_101_kea | kea | byte_perplexity ↓ | 7.147 | 9.632 |
2252
+ | gsarti/flores_101_khm | khm | byte_perplexity ↓ | 3.367 | 2.646 |
2253
+ | gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.241 | 4.522 |
2254
+ | gsarti/flores_101_kor | kor | byte_perplexity ↓ | 2.902 | 3.376 |
2255
+ | gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.331 | 3.106 |
2256
+ | gsarti/flores_101_lav | lav | byte_perplexity ↓ | 5.224 | 4.811 |
2257
+ | gsarti/flores_101_lin | lin | byte_perplexity ↓ | 4.847 | 8.871 |
2258
+ | gsarti/flores_101_lit | lit | byte_perplexity ↓ | 4.543 | 5.183 |
2259
+ | gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 5.591 | 7.158 |
2260
+ | gsarti/flores_101_lug | lug | byte_perplexity ↓ | 5.43 | 7.399 |
2261
+ | gsarti/flores_101_luo | luo | byte_perplexity ↓ | 12.031 | 11.951 |
2262
+ | gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.794 | 2.054 |
2263
+ | gsarti/flores_101_mar | mar | byte_perplexity ↓ | 6.857 | 2.274 |
2264
+ | gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.335 | 2.538 |
2265
+ | gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 9.041 | 5.996 |
2266
+ | gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.095 | 4.519 |
2267
+ | gsarti/flores_101_mri | mri | byte_perplexity ↓ | 5.266 | 4.438 |
2268
+ | gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.222 | 2.935 |
2269
+ | gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.523 | 2.413 |
2270
+ | gsarti/flores_101_nld | nld | byte_perplexity ↓ | 2.799 | 2.293 |
2271
+ | gsarti/flores_101_nob | nob | byte_perplexity ↓ | 3.629 | 2.593 |
2272
+ | gsarti/flores_101_npi | npi | byte_perplexity ↓ | 6.666 | 2.499 |
2273
+ | gsarti/flores_101_nso | nso | byte_perplexity ↓ | 5.015 | 8.485 |
2274
+ | gsarti/flores_101_nya | nya | byte_perplexity ↓ | 4.938 | 7.548 |
2275
+ | gsarti/flores_101_oci | oci | byte_perplexity ↓ | 3.607 | 4.936 |
2276
+ | gsarti/flores_101_orm | orm | byte_perplexity ↓ | 11.316 | 7.145 |
2277
+ | gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.982 | 2.668 |
2278
+ | gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.772 | 2.782 |
2279
+ | gsarti/flores_101_pol | pol | byte_perplexity ↓ | 3.012 | 2.432 |
2280
+ | gsarti/flores_101_por | por | byte_perplexity ↓ | 1.841 | 2.178 |
2281
+ | gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.624 | 4.785 |
2282
+ | gsarti/flores_101_ron | ron | byte_perplexity ↓ | 3.05 | 2.197 |
2283
+ | gsarti/flores_101_rus | rus | byte_perplexity ↓ | 1.708 | 1.689 |
2284
+ | gsarti/flores_101_slk | slk | byte_perplexity ↓ | 4.038 | 3.419 |
2285
+ | gsarti/flores_101_slv | slv | byte_perplexity ↓ | 4.141 | 3.582 |
2286
+ | gsarti/flores_101_sna | sna | byte_perplexity ↓ | 4.711 | 5.588 |
2287
+ | gsarti/flores_101_snd | snd | byte_perplexity ↓ | 4.206 | 5.667 |
2288
+ | gsarti/flores_101_som | som | byte_perplexity ↓ | 9.154 | 4.788 |
2289
+ | gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.796 | 2.098 |
2290
+ | gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.241 | 2.688 |
2291
+ | gsarti/flores_101_swe | swe | byte_perplexity ↓ | 3.345 | 2.468 |
2292
+ | gsarti/flores_101_swh | swh | byte_perplexity ↓ | 2.684 | 4.473 |
2293
+ | gsarti/flores_101_tam | tam | byte_perplexity ↓ | 5.165 | 2.024 |
2294
+ | gsarti/flores_101_tel | tel | byte_perplexity ↓ | 6.81 | 2.407 |
2295
+ | gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.785 | 4.899 |
2296
+ | gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 3.75 | 2.738 |
2297
+ | gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.104 | 2.035 |
2298
+ | gsarti/flores_101_tur | tur | byte_perplexity ↓ | 3.318 | 2.622 |
2299
+ | gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.089 | 1.93 |
2300
+ | gsarti/flores_101_umb | umb | byte_perplexity ↓ | 11.766 | 11.64 |
2301
+ | gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.779 | 2.982 |
2302
+ | gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 8.5 | 13.209 |
2303
+ | gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.659 | 2.229 |
2304
+ | gsarti/flores_101_wol | wol | byte_perplexity ↓ | 6.142 | 13.945 |
2305
+ | gsarti/flores_101_xho | xho | byte_perplexity ↓ | 4.69 | 8.42 |
2306
+ | gsarti/flores_101_yor | yor | byte_perplexity ↓ | 4.361 | 7.636 |
2307
+ | gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.118 | 5.113 |
2308
+ | gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.274 | 5.67 |
2309
+ | gsarti/flores_101_zul | zul | byte_perplexity ↓ | 6.017 | 7.341 |
2310
+ | headqa | esp | acc ↑ | 0.346 | 0.244 |
2311
+ | hellaswag | eng | acc ↑ | 0.535 | 0.592 |
2312
+ | lambada_mt_de | deu | acc ↑ | 0.329 | 0.358 |
2313
+ | lambada_mt_en | eng | acc ↑ | 0.672 | 0.747 |
2314
+ | lambada_mt_es | esp | acc ↑ | 0.476 | 0.397 |
2315
+ | lambada_mt_it | ita | acc ↑ | 0.406 | 0.409 |
2316
+ | logiqa | eng | acc ↑ | 0.235 | 0.244 |
2317
+ | mathqa | eng | acc ↑ | 0.277 | 0.268 |
2318
+ | mc_taco | eng | em ↑ | 0.131 | 0.124 |
2319
+ | mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
2320
+ | mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
2321
+ | mrpc | eng | acc ↑ | 0.387 | 0.446 |
2322
+ | multirc (Median of 11 prompts) | eng | acc ↑ | 0.571 | 0.599 |
2323
+ | openbookqa | eng | acc ↑ | 0.312 | 0.322 |
2324
+ | piqa | eng | acc ↑ | 0.781 | 0.791 |
2325
+ | prost | eng | acc ↑ | 0.298 | 0.299 |
2326
+ | pubmedqa | eng | acc ↑ | 0.741 | 0.709 |
2327
+ | qnli | eng | acc ↑ | 0.517 | 0.554 |
2328
+ | qqp (Median of 7 prompts) | eng | acc ↑ | 0.588 | 0.395 |
2329
+ | race | eng | acc ↑ | 0.39 | 0.402 |
2330
+ | rte (Median of 6 prompts) | eng | acc ↑ | 0.52 | 0.495 |
2331
+ | sciq | eng | acc ↑ | 0.936 | 0.948 |
2332
+ | sst (Median of 6 prompts) | eng | acc ↑ | 0.604 | 0.647 |
2333
+ | triviaqa | eng | acc ↑ | 0.183 | 0.342 |
2334
+ | tydiqa_primary (Median of 16 prompts) | eng | acc ↑ | 0.281 | 0.148 |
2335
+ | webqs | eng | acc ↑ | 0.062 | 0.159 |
2336
+ | wic (Median of 11 prompts) | eng | acc ↑ | 0.506 | 0.498 |
2337
+ | winogrande | eng | acc ↑ | 0.71 | 0.736 |
2338
+ | wnli (Median of 6 prompts) | eng | acc ↑ | 0.57 | 0.563 |
2339
+ | wsc (Median of 11 prompts) | eng | acc ↑ | 0.519 | 0.413 |
2340
  | humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
2341
+ | humaneval | python | pass@10 ↑ | 0.322 | 0.0 |
2342
+ | humaneval | python | pass@100 ↑ | 0.555 | 0.003 |
2343
 
2344
 
2345
  **Train-time Evaluation:**
 
2447
 
2448
  The checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) that the model was trained with, you'd want to use [this repo instead](https://huggingface.co/bigscience/bloom-optimizer-states).
2449
 
 
 
2450
  ---
2451
 
2452
  # Model Card Authors
2453
+ *Ordered roughly chronologically and by amount of time spent.*
2454
 
2455
  Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
config.json CHANGED
@@ -2,7 +2,7 @@
2
  "apply_residual_connection_post_layernorm": false,
3
  "attention_dropout": 0.0,
4
  "architectures": [
5
- "BloomForCausalLM"
6
  ],
7
  "attention_softmax_in_fp32": true,
8
  "pad_token_id": 3,
@@ -21,4 +21,4 @@
21
  "transformers_version": "4.21.0",
22
  "use_cache": true,
23
  "vocab_size": 250880
24
- }
 
2
  "apply_residual_connection_post_layernorm": false,
3
  "attention_dropout": 0.0,
4
  "architectures": [
5
+ "BloomModel"
6
  ],
7
  "attention_softmax_in_fp32": true,
8
  "pad_token_id": 3,
 
21
  "transformers_version": "4.21.0",
22
  "use_cache": true,
23
  "vocab_size": 250880
24
+ }
model.safetensors.index.json DELETED
@@ -1,852 +0,0 @@
1
- {
2
- "metadata": {
3
- "total_size": 352494542848
4
- },
5
- "weight_map": {
6
- "h.0.input_layernorm.bias": "model_00002-of-00072.safetensors",
7
- "h.0.input_layernorm.weight": "model_00002-of-00072.safetensors",
8
- "h.0.mlp.dense_4h_to_h.bias": "model_00002-of-00072.safetensors",
9
- "h.0.mlp.dense_4h_to_h.weight": "model_00002-of-00072.safetensors",
10
- "h.0.mlp.dense_h_to_4h.bias": "model_00002-of-00072.safetensors",
11
- "h.0.mlp.dense_h_to_4h.weight": "model_00002-of-00072.safetensors",
12
- "h.0.post_attention_layernorm.bias": "model_00002-of-00072.safetensors",
13
- "h.0.post_attention_layernorm.weight": "model_00002-of-00072.safetensors",
14
- "h.0.self_attention.dense.bias": "model_00002-of-00072.safetensors",
15
- "h.0.self_attention.dense.weight": "model_00002-of-00072.safetensors",
16
- "h.0.self_attention.query_key_value.bias": "model_00002-of-00072.safetensors",
17
- "h.0.self_attention.query_key_value.weight": "model_00002-of-00072.safetensors",
18
- "h.1.input_layernorm.bias": "model_00003-of-00072.safetensors",
19
- "h.1.input_layernorm.weight": "model_00003-of-00072.safetensors",
20
- "h.1.mlp.dense_4h_to_h.bias": "model_00003-of-00072.safetensors",
21
- "h.1.mlp.dense_4h_to_h.weight": "model_00003-of-00072.safetensors",
22
- "h.1.mlp.dense_h_to_4h.bias": "model_00003-of-00072.safetensors",
23
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