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  3. config.json +7 -0
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  6. tokenizer.json +3 -0
  7. tokenizer_config.json +1517 -0
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+ ---
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+ library_name: transformers
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+ widget:
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+ - messages:
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+ - role: user
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+ content: How does the brain work?
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+ inference:
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+ parameters:
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+ max_new_tokens: 200
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+ extra_gated_heading: Access Gemma on Hugging Face
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+ extra_gated_prompt: >-
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+ To access Gemma on Hugging Face, you’re required to review and agree to
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+ Google’s usage license. To do this, please ensure you’re logged-in to Hugging
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+ Face and click below. Requests are processed immediately.
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+ extra_gated_button_content: Acknowledge license
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+ license: gemma
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+ ---
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+
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+ # Gemma Model Card
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+
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+ **Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
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+
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+ This model card corresponds to the latest 2B instruct version of the Gemma model. Here you can find other models in the Gemma family:
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+
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+ | | Base | Instruct |
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+ |----|----------------------------------------------------|----------------------------------------------------------------------|
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+ | 2B | [gemma-2b](https://huggingface.co/google/gemma-2b) | [**gemma-1.1-2b-it**](https://huggingface.co/google/gemma-1.1-2b-it) |
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+ | 7B | [gemma-7b](https://huggingface.co/google/gemma-7b) | [gemma-1.1-7b-it](https://huggingface.co/google/gemma-1.1-7b-it) |
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+
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+
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+ **Release Notes**
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+
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+ This is Gemma 1.1 2B (IT), an update over the original instruction-tuned Gemma release.
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+
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+ Gemma 1.1 was trained using a novel RLHF method, leading to substantial gains on quality, coding capabilities, factuality, instruction following and multi-turn conversation quality. We also fixed a bug in multi-turn conversations, and made sure that model responses don't always start with `"Sure,"`.
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+
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+ We believe this release represents an improvement for most use cases, but we encourage users to test in their particular applications. The previous model [will continue to be available in the same repo](https://huggingface.co/google/gemma-2b-it). We appreciate the enthusiastic adoption of Gemma, and we continue to welcome all feedback from the community.
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+
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+
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+ **Resources and Technical Documentation**:
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+
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+ * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
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+ * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
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+ * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
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+
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+ **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
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+
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+ **Authors**: Google
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+
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+ ## Model Information
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+
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+ Summary description and brief definition of inputs and outputs.
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+
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+ ### Description
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+
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+ Gemma is a family of lightweight, state-of-the-art open models from Google,
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+ built from the same research and technology used to create the Gemini models.
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+ They are text-to-text, decoder-only large language models, available in English,
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+ with open weights, pre-trained variants, and instruction-tuned variants. Gemma
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+ models are well-suited for a variety of text generation tasks, including
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+ question answering, summarization, and reasoning. Their relatively small size
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+ makes it possible to deploy them in environments with limited resources such as
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+ a laptop, desktop or your own cloud infrastructure, democratizing access to
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+ state of the art AI models and helping foster innovation for everyone.
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+
66
+ ### Usage
67
+
68
+ Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
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+
70
+ #### Running the model on a CPU
71
+
72
+ As explained below, we recommend `torch.bfloat16` as the default dtype. You can use [a different precision](#precisions) if necessary.
73
+
74
+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
76
+ import torch
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+
78
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
79
+ model = AutoModelForCausalLM.from_pretrained(
80
+ "google/gemma-1.1-2b-it",
81
+ torch_dtype=torch.bfloat16
82
+ )
83
+
84
+ input_text = "Write me a poem about Machine Learning."
85
+ input_ids = tokenizer(input_text, return_tensors="pt")
86
+
87
+ outputs = model.generate(**input_ids, max_new_tokens=50)
88
+ print(tokenizer.decode(outputs[0]))
89
+ ```
90
+
91
+ #### Running the model on a single / multi GPU
92
+
93
+
94
+ ```python
95
+ # pip install accelerate
96
+ from transformers import AutoTokenizer, AutoModelForCausalLM
97
+ import torch
98
+
99
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
100
+ model = AutoModelForCausalLM.from_pretrained(
101
+ "google/gemma-1.1-2b-it",
102
+ device_map="auto",
103
+ torch_dtype=torch.bfloat16
104
+ )
105
+
106
+ input_text = "Write me a poem about Machine Learning."
107
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
108
+
109
+ outputs = model.generate(**input_ids)
110
+ print(tokenizer.decode(outputs[0]))
111
+ ```
112
+
113
+ <a name="precisions"></a>
114
+ #### Running the model on a GPU using different precisions
115
+
116
+ The native weights of this model were exported in `bfloat16` precision. You can use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that precision.
117
+
118
+ You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below.
119
+
120
+ * _Using `torch.float16`_
121
+
122
+ ```python
123
+ # pip install accelerate
124
+ from transformers import AutoTokenizer, AutoModelForCausalLM
125
+ import torch
126
+
127
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
128
+ model = AutoModelForCausalLM.from_pretrained(
129
+ "google/gemma-1.1-2b-it",
130
+ device_map="auto",
131
+ torch_dtype=torch.float16,
132
+ revision="float16",
133
+ )
134
+
135
+ input_text = "Write me a poem about Machine Learning."
136
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
137
+
138
+ outputs = model.generate(**input_ids)
139
+ print(tokenizer.decode(outputs[0]))
140
+ ```
141
+
142
+ * _Using `torch.bfloat16`_
143
+
144
+ ```python
145
+ # pip install accelerate
146
+ from transformers import AutoTokenizer, AutoModelForCausalLM
147
+
148
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
149
+ model = AutoModelForCausalLM.from_pretrained(
150
+ "google/gemma-1.1-2b-it",
151
+ device_map="auto",
152
+ torch_dtype=torch.bfloat16
153
+ )
154
+
155
+ input_text = "Write me a poem about Machine Learning."
156
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
157
+
158
+ outputs = model.generate(**input_ids)
159
+ print(tokenizer.decode(outputs[0]))
160
+ ```
161
+
162
+
163
+ * _Upcasting to `torch.float32`_
164
+
165
+ ```python
166
+ # pip install accelerate
167
+ from transformers import AutoTokenizer, AutoModelForCausalLM
168
+
169
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
170
+ model = AutoModelForCausalLM.from_pretrained(
171
+ "google/gemma-1.1-2b-it",
172
+ device_map="auto"
173
+ )
174
+
175
+ input_text = "Write me a poem about Machine Learning."
176
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
177
+
178
+ outputs = model.generate(**input_ids)
179
+ print(tokenizer.decode(outputs[0]))
180
+ ```
181
+
182
+ #### Quantized Versions through `bitsandbytes`
183
+
184
+ * _Using 8-bit precision (int8)_
185
+
186
+ ```python
187
+ # pip install bitsandbytes accelerate
188
+ from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
189
+
190
+ quantization_config = BitsAndBytesConfig(load_in_8bit=True)
191
+
192
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
193
+ model = AutoModelForCausalLM.from_pretrained(
194
+ "google/gemma-1.1-2b-it",
195
+ quantization_config=quantization_config
196
+ )
197
+
198
+ input_text = "Write me a poem about Machine Learning."
199
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
200
+
201
+ outputs = model.generate(**input_ids)
202
+ print(tokenizer.decode(outputs[0]))
203
+ ```
204
+
205
+ * _Using 4-bit precision_
206
+
207
+ ```python
208
+ # pip install bitsandbytes accelerate
209
+ from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
210
+
211
+ quantization_config = BitsAndBytesConfig(load_in_4bit=True)
212
+
213
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
214
+ model = AutoModelForCausalLM.from_pretrained(
215
+ "google/gemma-1.1-2b-it",
216
+ quantization_config=quantization_config
217
+ )
218
+
219
+ input_text = "Write me a poem about Machine Learning."
220
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
221
+
222
+ outputs = model.generate(**input_ids)
223
+ print(tokenizer.decode(outputs[0]))
224
+ ```
225
+
226
+
227
+ #### Other optimizations
228
+
229
+ * _Flash Attention 2_
230
+
231
+ First make sure to install `flash-attn` in your environment `pip install flash-attn`
232
+
233
+ ```diff
234
+ model = AutoModelForCausalLM.from_pretrained(
235
+ model_id,
236
+ torch_dtype=torch.float16,
237
+ + attn_implementation="flash_attention_2"
238
+ ).to(0)
239
+ ```
240
+
241
+ #### Running the model in JAX / Flax
242
+
243
+ Use the `flax` branch of the repository:
244
+
245
+ ```python
246
+ import jax.numpy as jnp
247
+ from transformers import AutoTokenizer, FlaxGemmaForCausalLM
248
+
249
+ model_id = "google/gemma-1.1-2b-it"
250
+
251
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
252
+ tokenizer.padding_side = "left"
253
+
254
+ model, params = FlaxGemmaForCausalLM.from_pretrained(
255
+ model_id,
256
+ dtype=jnp.bfloat16,
257
+ revision="flax",
258
+ _do_init=False,
259
+ )
260
+
261
+ inputs = tokenizer("Valencia and Málaga are", return_tensors="np", padding=True)
262
+ output = model.generate(**inputs, params=params, max_new_tokens=20, do_sample=False)
263
+ output_text = tokenizer.batch_decode(output.sequences, skip_special_tokens=True)
264
+ ```
265
+
266
+ [Check this notebook](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/jax_gemma.ipynb) for a comprehensive walkthrough on how to parallelize JAX inference.
267
+
268
+
269
+ ### Chat Template
270
+
271
+ The instruction-tuned models use a chat template that must be adhered to for conversational use.
272
+ The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
273
+
274
+ Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
275
+
276
+ ```py
277
+ from transformers import AutoTokenizer, AutoModelForCausalLM
278
+ import transformers
279
+ import torch
280
+
281
+ model_id = "google/gemma-1.1-2b-it"
282
+ dtype = torch.bfloat16
283
+
284
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
285
+ model = AutoModelForCausalLM.from_pretrained(
286
+ model_id,
287
+ device_map="cuda",
288
+ torch_dtype=dtype,
289
+ )
290
+
291
+ chat = [
292
+ { "role": "user", "content": "Write a hello world program" },
293
+ ]
294
+ prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
295
+ ```
296
+
297
+ At this point, the prompt contains the following text:
298
+
299
+ ```
300
+ <bos><start_of_turn>user
301
+ Write a hello world program<end_of_turn>
302
+ <start_of_turn>model
303
+ ```
304
+
305
+ As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
306
+ (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
307
+ the `<end_of_turn>` token.
308
+
309
+ You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
310
+ chat template.
311
+
312
+ After the prompt is ready, generation can be performed like this:
313
+
314
+ ```py
315
+ inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
316
+ outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
317
+ ```
318
+
319
+ ### Fine-tuning
320
+
321
+ You can find some fine-tuning scripts under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt them to this model, simply change the model-id to `google/gemma-1.1-2b-it`.
322
+
323
+ We provide:
324
+
325
+ * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
326
+ * A script to perform SFT using FSDP on TPU devices
327
+ * A notebook that you can run on a free-tier Google Colab instance to perform SFT on the English quotes dataset
328
+
329
+ ### Inputs and outputs
330
+
331
+ * **Input:** Text string, such as a question, a prompt, or a document to be
332
+ summarized.
333
+ * **Output:** Generated English-language text in response to the input, such
334
+ as an answer to a question, or a summary of a document.
335
+
336
+ ## Model Data
337
+
338
+ Data used for model training and how the data was processed.
339
+
340
+ ### Training Dataset
341
+
342
+ These models were trained on a dataset of text data that includes a wide variety
343
+ of sources, totaling 6 trillion tokens. Here are the key components:
344
+
345
+ * Web Documents: A diverse collection of web text ensures the model is exposed
346
+ to a broad range of linguistic styles, topics, and vocabulary. Primarily
347
+ English-language content.
348
+ * Code: Exposing the model to code helps it to learn the syntax and patterns of
349
+ programming languages, which improves its ability to generate code or
350
+ understand code-related questions.
351
+ * Mathematics: Training on mathematical text helps the model learn logical
352
+ reasoning, symbolic representation, and to address mathematical queries.
353
+
354
+ The combination of these diverse data sources is crucial for training a powerful
355
+ language model that can handle a wide variety of different tasks and text
356
+ formats.
357
+
358
+ ### Data Preprocessing
359
+
360
+ Here are the key data cleaning and filtering methods applied to the training
361
+ data:
362
+
363
+ * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
364
+ applied at multiple stages in the data preparation process to ensure the
365
+ exclusion of harmful and illegal content
366
+ * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
367
+ reliable, automated techniques were used to filter out certain personal
368
+ information and other sensitive data from training sets.
369
+ * Additional methods: Filtering based on content quality and safely in line with
370
+ [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
371
+
372
+ ## Implementation Information
373
+
374
+ Details about the model internals.
375
+
376
+ ### Hardware
377
+
378
+ Gemma was trained using the latest generation of
379
+ [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
380
+
381
+ Training large language models requires significant computational power. TPUs,
382
+ designed specifically for matrix operations common in machine learning, offer
383
+ several advantages in this domain:
384
+
385
+ * Performance: TPUs are specifically designed to handle the massive computations
386
+ involved in training LLMs. They can speed up training considerably compared to
387
+ CPUs.
388
+ * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
389
+ for the handling of large models and batch sizes during training. This can
390
+ lead to better model quality.
391
+ * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
392
+ handling the growing complexity of large foundation models. You can distribute
393
+ training across multiple TPU devices for faster and more efficient processing.
394
+ * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
395
+ solution for training large models compared to CPU-based infrastructure,
396
+ especially when considering the time and resources saved due to faster
397
+ training.
398
+ * These advantages are aligned with
399
+ [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
400
+
401
+ ### Software
402
+
403
+ Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways).
404
+
405
+ JAX allows researchers to take advantage of the latest generation of hardware,
406
+ including TPUs, for faster and more efficient training of large models.
407
+
408
+ ML Pathways is Google's latest effort to build artificially intelligent systems
409
+ capable of generalizing across multiple tasks. This is specially suitable for
410
+ [foundation models](https://ai.google/discover/foundation-models/), including large language models like
411
+ these ones.
412
+
413
+ Together, JAX and ML Pathways are used as described in the
414
+ [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
415
+ controller' programming model of Jax and Pathways allows a single Python
416
+ process to orchestrate the entire training run, dramatically simplifying the
417
+ development workflow."
418
+
419
+ ## Evaluation
420
+
421
+ Model evaluation metrics and results.
422
+
423
+ ### Benchmark Results
424
+
425
+ The pre-trained base models were evaluated against a large collection of different datasets and
426
+ metrics to cover different aspects of text generation:
427
+
428
+ | Benchmark | Metric | 2B Params | 7B Params |
429
+ | ------------------------------ | ------------- | ----------- | --------- |
430
+ | [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
431
+ | [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
432
+ | [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
433
+ | [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 |
434
+ | [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
435
+ | [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
436
+ | [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
437
+ | [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
438
+ | [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
439
+ | [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
440
+ | [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
441
+ | [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23 |
442
+ | [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
443
+ | [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
444
+ | [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
445
+ | [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
446
+ | [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
447
+ | [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
448
+ | ------------------------------ | ------------- | ----------- | --------- |
449
+ | **Average** | | **45.0** | **56.9** |
450
+
451
+ ## Ethics and Safety
452
+
453
+ Ethics and safety evaluation approach and results.
454
+
455
+ ### Evaluation Approach
456
+
457
+ Our evaluation methods include structured evaluations and internal red-teaming
458
+ testing of relevant content policies. Red-teaming was conducted by a number of
459
+ different teams, each with different goals and human evaluation metrics. These
460
+ models were evaluated against a number of different categories relevant to
461
+ ethics and safety, including:
462
+
463
+ * Text-to-Text Content Safety: Human evaluation on prompts covering safety
464
+ policies including child sexual abuse and exploitation, harassment, violence
465
+ and gore, and hate speech.
466
+ * Text-to-Text Representational Harms: Benchmark against relevant academic
467
+ datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
468
+ * Memorization: Automated evaluation of memorization of training data, including
469
+ the risk of personally identifiable information exposure.
470
+ * Large-scale harm: Tests for "dangerous capabilities," such as chemical,
471
+ biological, radiological, and nuclear (CBRN) risks.
472
+
473
+ ### Evaluation Results
474
+
475
+ The results of ethics and safety evaluations are within acceptable thresholds
476
+ for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
477
+ safety, content safety, representational harms, memorization, large-scale harms.
478
+ On top of robust internal evaluations, the results of well known safety
479
+ benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
480
+ are shown here.
481
+
482
+ #### Gemma 1.0
483
+
484
+ | Benchmark | Metric | Gemma 1.0 IT 2B | Gemma 1.0 IT 7B |
485
+ | ------------------------ | ------------- | --------------- | --------------- |
486
+ | [RealToxicity][realtox] | average | 6.86 | 7.90 |
487
+ | [BOLD][bold] | | 45.57 | 49.08 |
488
+ | [CrowS-Pairs][crows] | top-1 | 45.82 | 51.33 |
489
+ | [BBQ Ambig][bbq] | 1-shot, top-1 | 62.58 | 92.54 |
490
+ | [BBQ Disambig][bbq] | top-1 | 54.62 | 71.99 |
491
+ | [Winogender][winogender] | top-1 | 51.25 | 54.17 |
492
+ | [TruthfulQA][truthfulqa] | | 44.84 | 31.81 |
493
+ | [Winobias 1_2][winobias] | | 56.12 | 59.09 |
494
+ | [Winobias 2_2][winobias] | | 91.10 | 92.23 |
495
+ | [Toxigen][toxigen] | | 29.77 | 39.59 |
496
+ | ------------------------ | ------------- | --------------- | --------------- |
497
+
498
+ #### Gemma 1.1
499
+
500
+ | Benchmark | Metric | Gemma 1.1 IT 2B | Gemma 1.1 IT 7B |
501
+ | ------------------------ | ------------- | --------------- | --------------- |
502
+ | [RealToxicity][realtox] | average | 7.03 | 8.04 |
503
+ | [BOLD][bold] | | 47.76 | |
504
+ | [CrowS-Pairs][crows] | top-1 | 45.89 | 49.67 |
505
+ | [BBQ Ambig][bbq] | 1-shot, top-1 | 58.97 | 86.06 |
506
+ | [BBQ Disambig][bbq] | top-1 | 53.90 | 85.08 |
507
+ | [Winogender][winogender] | top-1 | 50.14 | 57.64 |
508
+ | [TruthfulQA][truthfulqa] | | 44.24 | 45.34 |
509
+ | [Winobias 1_2][winobias] | | 55.93 | 59.22 |
510
+ | [Winobias 2_2][winobias] | | 89.46 | 89.2 |
511
+ | [Toxigen][toxigen] | | 29.64 | 38.75 |
512
+ | ------------------------ | ------------- | --------------- | --------------- |
513
+
514
+
515
+ ## Usage and Limitations
516
+
517
+ These models have certain limitations that users should be aware of.
518
+
519
+ ### Intended Usage
520
+
521
+ Open Large Language Models (LLMs) have a wide range of applications across
522
+ various industries and domains. The following list of potential uses is not
523
+ comprehensive. The purpose of this list is to provide contextual information
524
+ about the possible use-cases that the model creators considered as part of model
525
+ training and development.
526
+
527
+ * Content Creation and Communication
528
+ * Text Generation: These models can be used to generate creative text formats
529
+ such as poems, scripts, code, marketing copy, and email drafts.
530
+ * Chatbots and Conversational AI: Power conversational interfaces for customer
531
+ service, virtual assistants, or interactive applications.
532
+ * Text Summarization: Generate concise summaries of a text corpus, research
533
+ papers, or reports.
534
+ * Research and Education
535
+ * Natural Language Processing (NLP) Research: These models can serve as a
536
+ foundation for researchers to experiment with NLP techniques, develop
537
+ algorithms, and contribute to the advancement of the field.
538
+ * Language Learning Tools: Support interactive language learning experiences,
539
+ aiding in grammar correction or providing writing practice.
540
+ * Knowledge Exploration: Assist researchers in exploring large bodies of text
541
+ by generating summaries or answering questions about specific topics.
542
+
543
+ ### Limitations
544
+
545
+ * Training Data
546
+ * The quality and diversity of the training data significantly influence the
547
+ model's capabilities. Biases or gaps in the training data can lead to
548
+ limitations in the model's responses.
549
+ * The scope of the training dataset determines the subject areas the model can
550
+ handle effectively.
551
+ * Context and Task Complexity
552
+ * LLMs are better at tasks that can be framed with clear prompts and
553
+ instructions. Open-ended or highly complex tasks might be challenging.
554
+ * A model's performance can be influenced by the amount of context provided
555
+ (longer context generally leads to better outputs, up to a certain point).
556
+ * Language Ambiguity and Nuance
557
+ * Natural language is inherently complex. LLMs might struggle to grasp subtle
558
+ nuances, sarcasm, or figurative language.
559
+ * Factual Accuracy
560
+ * LLMs generate responses based on information they learned from their
561
+ training datasets, but they are not knowledge bases. They may generate
562
+ incorrect or outdated factual statements.
563
+ * Common Sense
564
+ * LLMs rely on statistical patterns in language. They might lack the ability
565
+ to apply common sense reasoning in certain situations.
566
+
567
+ ### Ethical Considerations and Risks
568
+
569
+ The development of large language models (LLMs) raises several ethical concerns.
570
+ In creating an open model, we have carefully considered the following:
571
+
572
+ * Bias and Fairness
573
+ * LLMs trained on large-scale, real-world text data can reflect socio-cultural
574
+ biases embedded in the training material. These models underwent careful
575
+ scrutiny, input data pre-processing described and posterior evaluations
576
+ reported in this card.
577
+ * Misinformation and Misuse
578
+ * LLMs can be misused to generate text that is false, misleading, or harmful.
579
+ * Guidelines are provided for responsible use with the model, see the
580
+ [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
581
+ * Transparency and Accountability:
582
+ * This model card summarizes details on the models' architecture,
583
+ capabilities, limitations, and evaluation processes.
584
+ * A responsibly developed open model offers the opportunity to share
585
+ innovation by making LLM technology accessible to developers and researchers
586
+ across the AI ecosystem.
587
+
588
+ Risks identified and mitigations:
589
+
590
+ * Perpetuation of biases: It's encouraged to perform continuous monitoring
591
+ (using evaluation metrics, human review) and the exploration of de-biasing
592
+ techniques during model training, fine-tuning, and other use cases.
593
+ * Generation of harmful content: Mechanisms and guidelines for content safety
594
+ are essential. Developers are encouraged to exercise caution and implement
595
+ appropriate content safety safeguards based on their specific product policies
596
+ and application use cases.
597
+ * Misuse for malicious purposes: Technical limitations and developer and
598
+ end-user education can help mitigate against malicious applications of LLMs.
599
+ Educational resources and reporting mechanisms for users to flag misuse are
600
+ provided. Prohibited uses of Gemma models are outlined in the
601
+ [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
602
+ * Privacy violations: Models were trained on data filtered for removal of PII
603
+ (Personally Identifiable Information). Developers are encouraged to adhere to
604
+ privacy regulations with privacy-preserving techniques.
605
+
606
+ ### Benefits
607
+
608
+ At the time of release, this family of models provides high-performance open
609
+ large language model implementations designed from the ground up for Responsible
610
+ AI development compared to similarly sized models.
611
+
612
+ Using the benchmark evaluation metrics described in this document, these models
613
+ have shown to provide superior performance to other, comparably-sized open model
614
+ alternatives.
615
+
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+ "173": {
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+ "content": "<tfoot>",
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+ "single_word": false,
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+ "special": false
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+ },
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+ "174": {
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+ "content": "<tr>",
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+ "special": false
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+ "175": {
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+ "content": "<th>",
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+ "rstrip": false,
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+ "single_word": false,
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+ },
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+ "176": {
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+ "content": "<td>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ },
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+ "177": {
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+ "content": "</table>",
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+ "special": false
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+ "178": {
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+ "content": "</caption>",
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+ "179": {
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+ "181": {
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+ "182": {
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+ "183": {
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+ "185": {
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+ "special": false
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+ "187": {
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+ "188": {
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+ "single_word": false,
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+ "special": false
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+ "189": {
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+ "single_word": false,
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+ "special": false
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+ "190": {
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+ "content": "<h6>",
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ "191": {
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+ "content": "<blockquote>",
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+ "198": {
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+ "content": "</blockquote>",
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ "199": {
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+ "content": "<strong>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
1362
+ "single_word": false,
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+ "special": false
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+ },
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+ "200": {
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+ "content": "<em>",
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+ "lstrip": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ },
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+ "201": {
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+ "content": "<b>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ "202": {
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+ "content": "<i>",
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+ "lstrip": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ },
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+ "203": {
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+ "content": "<u>",
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ },
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+ "204": {
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+ "content": "<s>",
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+ "205": {
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+ "content": "<sub>",
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+ "rstrip": false,
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+ "single_word": false,
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+ "206": {
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+ "content": "<sup>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ },
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+ "207": {
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+ "content": "<code>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ "208": {
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+ "content": "</strong>",
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+ "special": false
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+ },
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+ "209": {
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+ "211": {
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+ "single_word": false,
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+ "special": false
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+ },
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+ "212": {
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+ "single_word": false,
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+ "special": false
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+ },
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+ "213": {
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+ "single_word": false,
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+ },
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+ "214": {
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1482
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+ "special": false
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+ },
1485
+ "215": {
1486
+ "content": "</sup>",
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+ "lstrip": false,
1488
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+ "216": {
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+ "content": "</code>",
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1497
+ "rstrip": false,
1498
+ "single_word": false,
1499
+ "special": false
1500
+ }
1501
+ },
1502
+ "additional_special_tokens": [
1503
+ "<start_of_turn>",
1504
+ "<end_of_turn>"
1505
+ ],
1506
+ "bos_token": "<bos>",
1507
+ "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
1508
+ "clean_up_tokenization_spaces": false,
1509
+ "eos_token": "<eos>",
1510
+ "model_max_length": 1000000000000000019884624838656,
1511
+ "pad_token": "<pad>",
1512
+ "sp_model_kwargs": {},
1513
+ "spaces_between_special_tokens": false,
1514
+ "tokenizer_class": "GemmaTokenizer",
1515
+ "unk_token": "<unk>",
1516
+ "use_default_system_prompt": false
1517
+ }
vocabulary.json ADDED
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