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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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+ GPTQ quantized version of gemma-1.1-7b-it model.
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+
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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 7B 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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+ **Release Notes**
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+
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+ This is Gemma 1.1 7B (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-7b-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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+ **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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+
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+ ### Usage
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+
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+ 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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+
72
+ #### Running the model on a CPU
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+
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+ As explained below, we recommend `torch.bfloat16` as the default dtype. You can use [a different precision](#precisions) if necessary.
75
+
76
+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it")
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+ model = AutoModelForCausalLM.from_pretrained(
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+ "google/gemma-1.1-7b-it",
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+ torch_dtype=torch.bfloat16
84
+ )
85
+
86
+ input_text = "Write me a poem about Machine Learning."
87
+ input_ids = tokenizer(input_text, return_tensors="pt")
88
+
89
+ outputs = model.generate(**input_ids, max_new_tokens=50)
90
+ print(tokenizer.decode(outputs[0]))
91
+ ```
92
+
93
+ #### Running the model on a single / multi GPU
94
+
95
+
96
+ ```python
97
+ # pip install accelerate
98
+ from transformers import AutoTokenizer, AutoModelForCausalLM
99
+ import torch
100
+
101
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it")
102
+ model = AutoModelForCausalLM.from_pretrained(
103
+ "google/gemma-1.1-7b-it",
104
+ device_map="auto",
105
+ torch_dtype=torch.bfloat16
106
+ )
107
+
108
+ input_text = "Write me a poem about Machine Learning."
109
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
110
+
111
+ outputs = model.generate(**input_ids)
112
+ print(tokenizer.decode(outputs[0]))
113
+ ```
114
+
115
+ <a name="precisions"></a>
116
+ #### Running the model on a GPU using different precisions
117
+
118
+ 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.
119
+
120
+ 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.
121
+
122
+ * _Using `torch.float16`_
123
+
124
+ ```python
125
+ # pip install accelerate
126
+ from transformers import AutoTokenizer, AutoModelForCausalLM
127
+ import torch
128
+
129
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it")
130
+ model = AutoModelForCausalLM.from_pretrained(
131
+ "google/gemma-1.1-7b-it",
132
+ device_map="auto",
133
+ torch_dtype=torch.float16,
134
+ revision="float16",
135
+ )
136
+
137
+ input_text = "Write me a poem about Machine Learning."
138
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
139
+
140
+ outputs = model.generate(**input_ids)
141
+ print(tokenizer.decode(outputs[0]))
142
+ ```
143
+
144
+ * _Using `torch.bfloat16`_
145
+
146
+ ```python
147
+ # pip install accelerate
148
+ from transformers import AutoTokenizer, AutoModelForCausalLM
149
+ import torch
150
+
151
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
152
+ model = AutoModelForCausalLM.from_pretrained(
153
+ "google/gemma-1.1-7b-it",
154
+ device_map="auto",
155
+ torch_dtype=torch.bfloat16
156
+ )
157
+
158
+ input_text = "Write me a poem about Machine Learning."
159
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
160
+
161
+ outputs = model.generate(**input_ids)
162
+ print(tokenizer.decode(outputs[0]))
163
+ ```
164
+
165
+ * _Upcasting to `torch.float32`_
166
+
167
+ ```python
168
+ # pip install accelerate
169
+ from transformers import AutoTokenizer, AutoModelForCausalLM
170
+
171
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it")
172
+ model = AutoModelForCausalLM.from_pretrained(
173
+ "google/gemma-1.1-7b-it",
174
+ device_map="auto"
175
+ )
176
+
177
+ input_text = "Write me a poem about Machine Learning."
178
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
179
+
180
+ outputs = model.generate(**input_ids)
181
+ print(tokenizer.decode(outputs[0]))
182
+ ```
183
+
184
+ #### Quantized Versions through `bitsandbytes`
185
+
186
+ * _Using 8-bit precision (int8)_
187
+
188
+ ```python
189
+ # pip install bitsandbytes accelerate
190
+ from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
191
+
192
+ quantization_config = BitsAndBytesConfig(load_in_8bit=True)
193
+
194
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it")
195
+ model = AutoModelForCausalLM.from_pretrained(
196
+ "google/gemma-1.1-7b-it",
197
+ quantization_config=quantization_config
198
+ )
199
+
200
+ input_text = "Write me a poem about Machine Learning."
201
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
202
+
203
+ outputs = model.generate(**input_ids)
204
+ print(tokenizer.decode(outputs[0]))
205
+ ```
206
+
207
+ * _Using 4-bit precision_
208
+
209
+ ```python
210
+ # pip install bitsandbytes accelerate
211
+ from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
212
+
213
+ quantization_config = BitsAndBytesConfig(load_in_4bit=True)
214
+
215
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it")
216
+ model = AutoModelForCausalLM.from_pretrained(
217
+ "google/gemma-1.1-7b-it",
218
+ quantization_config=quantization_config
219
+ )
220
+
221
+ input_text = "Write me a poem about Machine Learning."
222
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
223
+
224
+ outputs = model.generate(**input_ids)
225
+ print(tokenizer.decode(outputs[0]))
226
+ ```
227
+
228
+
229
+ #### Other optimizations
230
+
231
+ * _Flash Attention 2_
232
+
233
+ First make sure to install `flash-attn` in your environment `pip install flash-attn`
234
+
235
+ ```diff
236
+ model = AutoModelForCausalLM.from_pretrained(
237
+ model_id,
238
+ torch_dtype=torch.float16,
239
+ + attn_implementation="flash_attention_2"
240
+ ).to(0)
241
+ ```
242
+
243
+ #### Running the model in JAX / Flax
244
+
245
+ Use the `flax` branch of the repository:
246
+
247
+ ```python
248
+ import jax.numpy as jnp
249
+ from transformers import AutoTokenizer, FlaxGemmaForCausalLM
250
+
251
+ model_id = "google/gemma-1.1-7b-it"
252
+
253
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
254
+ tokenizer.padding_side = "left"
255
+
256
+ model, params = FlaxGemmaForCausalLM.from_pretrained(
257
+ model_id,
258
+ dtype=jnp.bfloat16,
259
+ revision="flax",
260
+ _do_init=False,
261
+ )
262
+
263
+ inputs = tokenizer("Valencia and Málaga are", return_tensors="np", padding=True)
264
+ output = model.generate(**inputs, params=params, max_new_tokens=20, do_sample=False)
265
+ output_text = tokenizer.batch_decode(output.sequences, skip_special_tokens=True)
266
+ ```
267
+
268
+ [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.
269
+
270
+
271
+ ### Chat Template
272
+
273
+ The instruction-tuned models use a chat template that must be adhered to for conversational use.
274
+ The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
275
+
276
+ Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
277
+
278
+ ```py
279
+ from transformers import AutoTokenizer, AutoModelForCausalLM
280
+ import transformers
281
+ import torch
282
+
283
+ model_id = "google/gemma-1.1-7b-it"
284
+ dtype = torch.bfloat16
285
+
286
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
287
+ model = AutoModelForCausalLM.from_pretrained(
288
+ model_id,
289
+ device_map="cuda",
290
+ torch_dtype=dtype,
291
+ )
292
+
293
+ chat = [
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+ { "role": "user", "content": "Write a hello world program" },
295
+ ]
296
+ prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
297
+ ```
298
+
299
+ At this point, the prompt contains the following text:
300
+
301
+ ```
302
+ <bos><start_of_turn>user
303
+ Write a hello world program<end_of_turn>
304
+ <start_of_turn>model
305
+ ```
306
+
307
+ As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
308
+ (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
309
+ the `<end_of_turn>` token.
310
+
311
+ You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
312
+ chat template.
313
+
314
+ After the prompt is ready, generation can be performed like this:
315
+
316
+ ```py
317
+ inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
318
+ outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
319
+ ```
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+
321
+ ### Fine-tuning
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+
323
+ 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-1.1-7b-it`.
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+
325
+ We provide:
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+
327
+ * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
328
+ * A script to perform SFT using FSDP on TPU devices
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+ * A notebook that you can run on a free-tier Google Colab instance to perform SFT on the English quotes dataset
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+
331
+ ### Inputs and outputs
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+
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+ * **Input:** Text string, such as a question, a prompt, or a document to be
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+ summarized.
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+ * **Output:** Generated English-language text in response to the input, such
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+ as an answer to a question, or a summary of a document.
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+
338
+ ## Model Data
339
+
340
+ Data used for model training and how the data was processed.
341
+
342
+ ### Training Dataset
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+
344
+ These models were trained on a dataset of text data that includes a wide variety
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+ of sources, totaling 6 trillion tokens. Here are the key components:
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+
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+ * Web Documents: A diverse collection of web text ensures the model is exposed
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+ to a broad range of linguistic styles, topics, and vocabulary. Primarily
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+ English-language content.
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+ * Code: Exposing the model to code helps it to learn the syntax and patterns of
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+ programming languages, which improves its ability to generate code or
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+ understand code-related questions.
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+ * Mathematics: Training on mathematical text helps the model learn logical
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+ reasoning, symbolic representation, and to address mathematical queries.
355
+
356
+ The combination of these diverse data sources is crucial for training a powerful
357
+ language model that can handle a wide variety of different tasks and text
358
+ formats.
359
+
360
+ ### Data Preprocessing
361
+
362
+ Here are the key data cleaning and filtering methods applied to the training
363
+ data:
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+
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+ * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
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+ applied at multiple stages in the data preparation process to ensure the
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+ exclusion of harmful and illegal content
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+ * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
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+ reliable, automated techniques were used to filter out certain personal
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+ information and other sensitive data from training sets.
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+ * Additional methods: Filtering based on content quality and safely in line with
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+ [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
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+
374
+ ## Implementation Information
375
+
376
+ Details about the model internals.
377
+
378
+ ### Hardware
379
+
380
+ Gemma was trained using the latest generation of
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+ [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
382
+
383
+ Training large language models requires significant computational power. TPUs,
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+ designed specifically for matrix operations common in machine learning, offer
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+ several advantages in this domain:
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+
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+ * Performance: TPUs are specifically designed to handle the massive computations
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+ involved in training LLMs. They can speed up training considerably compared to
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+ CPUs.
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+ * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
391
+ for the handling of large models and batch sizes during training. This can
392
+ lead to better model quality.
393
+ * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
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+ handling the growing complexity of large foundation models. You can distribute
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+ training across multiple TPU devices for faster and more efficient processing.
396
+ * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
397
+ solution for training large models compared to CPU-based infrastructure,
398
+ especially when considering the time and resources saved due to faster
399
+ training.
400
+ * These advantages are aligned with
401
+ [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
402
+
403
+ ### Software
404
+
405
+ 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).
406
+
407
+ JAX allows researchers to take advantage of the latest generation of hardware,
408
+ including TPUs, for faster and more efficient training of large models.
409
+
410
+ ML Pathways is Google's latest effort to build artificially intelligent systems
411
+ capable of generalizing across multiple tasks. This is specially suitable for
412
+ [foundation models](https://ai.google/discover/foundation-models/), including large language models like
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+ these ones.
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+
415
+ Together, JAX and ML Pathways are used as described in the
416
+ [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
417
+ controller' programming model of Jax and Pathways allows a single Python
418
+ process to orchestrate the entire training run, dramatically simplifying the
419
+ development workflow."
420
+
421
+ ## Evaluation
422
+
423
+ Model evaluation metrics and results.
424
+
425
+ ### Benchmark Results
426
+
427
+ The pre-trained base models were evaluated against a large collection of different datasets and
428
+ metrics to cover different aspects of text generation:
429
+
430
+ | Benchmark | Metric | Gemma PT 2B | Gemma PT 7B |
431
+ | ------------------------------ | ------------- | ----------- | ----------- |
432
+ | [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
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+ | [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot | 71.4 | 81.2 |
434
+ | [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
435
+ | [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 |
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+ | [BoolQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
437
+ | [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
438
+ | [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
439
+ | [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
440
+ | [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
441
+ | [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
442
+ | [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
443
+ | [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23.0 |
444
+ | [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
445
+ | [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
446
+ | [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
447
+ | [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
448
+ | [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
449
+ | [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
450
+ | ------------------------------ | ------------- | ----------- | ----------- |
451
+ | **Average** | | **44.9** | **56.4** |
452
+
453
+ ## Ethics and Safety
454
+
455
+ Ethics and safety evaluation approach and results.
456
+
457
+ ### Evaluation Approach
458
+
459
+ Our evaluation methods include structured evaluations and internal red-teaming
460
+ testing of relevant content policies. Red-teaming was conducted by a number of
461
+ different teams, each with different goals and human evaluation metrics. These
462
+ models were evaluated against a number of different categories relevant to
463
+ ethics and safety, including:
464
+
465
+ * Text-to-Text Content Safety: Human evaluation on prompts covering safety
466
+ policies including child sexual abuse and exploitation, harassment, violence
467
+ and gore, and hate speech.
468
+ * Text-to-Text Representational Harms: Benchmark against relevant academic
469
+ datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
470
+ * Memorization: Automated evaluation of memorization of training data, including
471
+ the risk of personally identifiable information exposure.
472
+ * Large-scale harm: Tests for "dangerous capabilities," such as chemical,
473
+ biological, radiological, and nuclear (CBRN) risks.
474
+
475
+ ### Evaluation Results
476
+
477
+ The results of ethics and safety evaluations are within acceptable thresholds
478
+ 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
479
+ safety, content safety, representational harms, memorization, large-scale harms.
480
+ On top of robust internal evaluations, the results of well known safety
481
+ benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
482
+ are shown here.
483
+
484
+ #### Gemma 1.0
485
+
486
+ | Benchmark | Metric | Gemma 1.0 IT 2B | Gemma 1.0 IT 7B |
487
+ | ------------------------ | ------------- | --------------- | --------------- |
488
+ | [RealToxicity][realtox] | average | 6.86 | 7.90 |
489
+ | [BOLD][bold] | | 45.57 | 49.08 |
490
+ | [CrowS-Pairs][crows] | top-1 | 45.82 | 51.33 |
491
+ | [BBQ Ambig][bbq] | 1-shot, top-1 | 62.58 | 92.54 |
492
+ | [BBQ Disambig][bbq] | top-1 | 54.62 | 71.99 |
493
+ | [Winogender][winogender] | top-1 | 51.25 | 54.17 |
494
+ | [TruthfulQA][truthfulqa] | | 44.84 | 31.81 |
495
+ | [Winobias 1_2][winobias] | | 56.12 | 59.09 |
496
+ | [Winobias 2_2][winobias] | | 91.10 | 92.23 |
497
+ | [Toxigen][toxigen] | | 29.77 | 39.59 |
498
+ | ------------------------ | ------------- | --------------- | --------------- |
499
+
500
+ #### Gemma 1.1
501
+
502
+ | Benchmark | Metric | Gemma 1.1 IT 2B | Gemma 1.1 IT 7B |
503
+ | ------------------------ | ------------- | --------------- | --------------- |
504
+ | [RealToxicity][realtox] | average | 7.03 | 8.04 |
505
+ | [BOLD][bold] | | 47.76 | |
506
+ | [CrowS-Pairs][crows] | top-1 | 45.89 | 49.67 |
507
+ | [BBQ Ambig][bbq] | 1-shot, top-1 | 58.97 | 86.06 |
508
+ | [BBQ Disambig][bbq] | top-1 | 53.90 | 85.08 |
509
+ | [Winogender][winogender] | top-1 | 50.14 | 57.64 |
510
+ | [TruthfulQA][truthfulqa] | | 44.24 | 45.34 |
511
+ | [Winobias 1_2][winobias] | | 55.93 | 59.22 |
512
+ | [Winobias 2_2][winobias] | | 89.46 | 89.2 |
513
+ | [Toxigen][toxigen] | | 29.64 | 38.75 |
514
+ | ------------------------ | ------------- | --------------- | --------------- |
515
+
516
+
517
+ ## Usage and Limitations
518
+
519
+ These models have certain limitations that users should be aware of.
520
+
521
+ ### Intended Usage
522
+
523
+ Open Large Language Models (LLMs) have a wide range of applications across
524
+ various industries and domains. The following list of potential uses is not
525
+ comprehensive. The purpose of this list is to provide contextual information
526
+ about the possible use-cases that the model creators considered as part of model
527
+ training and development.
528
+
529
+ * Content Creation and Communication
530
+ * Text Generation: These models can be used to generate creative text formats
531
+ such as poems, scripts, code, marketing copy, and email drafts.
532
+ * Chatbots and Conversational AI: Power conversational interfaces for customer
533
+ service, virtual assistants, or interactive applications.
534
+ * Text Summarization: Generate concise summaries of a text corpus, research
535
+ papers, or reports.
536
+ * Research and Education
537
+ * Natural Language Processing (NLP) Research: These models can serve as a
538
+ foundation for researchers to experiment with NLP techniques, develop
539
+ algorithms, and contribute to the advancement of the field.
540
+ * Language Learning Tools: Support interactive language learning experiences,
541
+ aiding in grammar correction or providing writing practice.
542
+ * Knowledge Exploration: Assist researchers in exploring large bodies of text
543
+ by generating summaries or answering questions about specific topics.
544
+
545
+ ### Limitations
546
+
547
+ * Training Data
548
+ * The quality and diversity of the training data significantly influence the
549
+ model's capabilities. Biases or gaps in the training data can lead to
550
+ limitations in the model's responses.
551
+ * The scope of the training dataset determines the subject areas the model can
552
+ handle effectively.
553
+ * Context and Task Complexity
554
+ * LLMs are better at tasks that can be framed with clear prompts and
555
+ instructions. Open-ended or highly complex tasks might be challenging.
556
+ * A model's performance can be influenced by the amount of context provided
557
+ (longer context generally leads to better outputs, up to a certain point).
558
+ * Language Ambiguity and Nuance
559
+ * Natural language is inherently complex. LLMs might struggle to grasp subtle
560
+ nuances, sarcasm, or figurative language.
561
+ * Factual Accuracy
562
+ * LLMs generate responses based on information they learned from their
563
+ training datasets, but they are not knowledge bases. They may generate
564
+ incorrect or outdated factual statements.
565
+ * Common Sense
566
+ * LLMs rely on statistical patterns in language. They might lack the ability
567
+ to apply common sense reasoning in certain situations.
568
+
569
+ ### Ethical Considerations and Risks
570
+
571
+ The development of large language models (LLMs) raises several ethical concerns.
572
+ In creating an open model, we have carefully considered the following:
573
+
574
+ * Bias and Fairness
575
+ * LLMs trained on large-scale, real-world text data can reflect socio-cultural
576
+ biases embedded in the training material. These models underwent careful
577
+ scrutiny, input data pre-processing described and posterior evaluations
578
+ reported in this card.
579
+ * Misinformation and Misuse
580
+ * LLMs can be misused to generate text that is false, misleading, or harmful.
581
+ * Guidelines are provided for responsible use with the model, see the
582
+ [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
583
+ * Transparency and Accountability:
584
+ * This model card summarizes details on the models' architecture,
585
+ capabilities, limitations, and evaluation processes.
586
+ * A responsibly developed open model offers the opportunity to share
587
+ innovation by making LLM technology accessible to developers and researchers
588
+ across the AI ecosystem.
589
+
590
+ Risks identified and mitigations:
591
+
592
+ * Perpetuation of biases: It's encouraged to perform continuous monitoring
593
+ (using evaluation metrics, human review) and the exploration of de-biasing
594
+ techniques during model training, fine-tuning, and other use cases.
595
+ * Generation of harmful content: Mechanisms and guidelines for content safety
596
+ are essential. Developers are encouraged to exercise caution and implement
597
+ appropriate content safety safeguards based on their specific product policies
598
+ and application use cases.
599
+ * Misuse for malicious purposes: Technical limitations and developer and
600
+ end-user education can help mitigate against malicious applications of LLMs.
601
+ Educational resources and reporting mechanisms for users to flag misuse are
602
+ provided. Prohibited uses of Gemma models are outlined in the
603
+ [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
604
+ * Privacy violations: Models were trained on data filtered for removal of PII
605
+ (Personally Identifiable Information). Developers are encouraged to adhere to
606
+ privacy regulations with privacy-preserving techniques.
607
+
608
+ ### Benefits
609
+
610
+ At the time of release, this family of models provides high-performance open
611
+ large language model implementations designed from the ground up for Responsible
612
+ AI development compared to similarly sized models.
613
+
614
+ Using the benchmark evaluation metrics described in this document, these models
615
+ have shown to provide superior performance to other, comparably-sized open model
616
+ alternatives.
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1411
+ "special": false
1412
+ },
1413
+ "176": {
1414
+ "content": "<td>",
1415
+ "lstrip": false,
1416
+ "normalized": false,
1417
+ "rstrip": false,
1418
+ "single_word": false,
1419
+ "special": false
1420
+ },
1421
+ "177": {
1422
+ "content": "</table>",
1423
+ "lstrip": false,
1424
+ "normalized": false,
1425
+ "rstrip": false,
1426
+ "single_word": false,
1427
+ "special": false
1428
+ },
1429
+ "178": {
1430
+ "content": "</caption>",
1431
+ "lstrip": false,
1432
+ "normalized": false,
1433
+ "rstrip": false,
1434
+ "single_word": false,
1435
+ "special": false
1436
+ },
1437
+ "179": {
1438
+ "content": "</thead>",
1439
+ "lstrip": false,
1440
+ "normalized": false,
1441
+ "rstrip": false,
1442
+ "single_word": false,
1443
+ "special": false
1444
+ },
1445
+ "180": {
1446
+ "content": "</tbody>",
1447
+ "lstrip": false,
1448
+ "normalized": false,
1449
+ "rstrip": false,
1450
+ "single_word": false,
1451
+ "special": false
1452
+ },
1453
+ "181": {
1454
+ "content": "</tfoot>",
1455
+ "lstrip": false,
1456
+ "normalized": false,
1457
+ "rstrip": false,
1458
+ "single_word": false,
1459
+ "special": false
1460
+ },
1461
+ "182": {
1462
+ "content": "</tr>",
1463
+ "lstrip": false,
1464
+ "normalized": false,
1465
+ "rstrip": false,
1466
+ "single_word": false,
1467
+ "special": false
1468
+ },
1469
+ "183": {
1470
+ "content": "</th>",
1471
+ "lstrip": false,
1472
+ "normalized": false,
1473
+ "rstrip": false,
1474
+ "single_word": false,
1475
+ "special": false
1476
+ },
1477
+ "184": {
1478
+ "content": "</td>",
1479
+ "lstrip": false,
1480
+ "normalized": false,
1481
+ "rstrip": false,
1482
+ "single_word": false,
1483
+ "special": false
1484
+ },
1485
+ "185": {
1486
+ "content": "<h1>",
1487
+ "lstrip": false,
1488
+ "normalized": false,
1489
+ "rstrip": false,
1490
+ "single_word": false,
1491
+ "special": false
1492
+ },
1493
+ "186": {
1494
+ "content": "<h2>",
1495
+ "lstrip": false,
1496
+ "normalized": false,
1497
+ "rstrip": false,
1498
+ "single_word": false,
1499
+ "special": false
1500
+ },
1501
+ "187": {
1502
+ "content": "<h3>",
1503
+ "lstrip": false,
1504
+ "normalized": false,
1505
+ "rstrip": false,
1506
+ "single_word": false,
1507
+ "special": false
1508
+ },
1509
+ "188": {
1510
+ "content": "<h4>",
1511
+ "lstrip": false,
1512
+ "normalized": false,
1513
+ "rstrip": false,
1514
+ "single_word": false,
1515
+ "special": false
1516
+ },
1517
+ "189": {
1518
+ "content": "<h5>",
1519
+ "lstrip": false,
1520
+ "normalized": false,
1521
+ "rstrip": false,
1522
+ "single_word": false,
1523
+ "special": false
1524
+ },
1525
+ "190": {
1526
+ "content": "<h6>",
1527
+ "lstrip": false,
1528
+ "normalized": false,
1529
+ "rstrip": false,
1530
+ "single_word": false,
1531
+ "special": false
1532
+ },
1533
+ "191": {
1534
+ "content": "<blockquote>",
1535
+ "lstrip": false,
1536
+ "normalized": false,
1537
+ "rstrip": false,
1538
+ "single_word": false,
1539
+ "special": false
1540
+ },
1541
+ "192": {
1542
+ "content": "</h1>",
1543
+ "lstrip": false,
1544
+ "normalized": false,
1545
+ "rstrip": false,
1546
+ "single_word": false,
1547
+ "special": false
1548
+ },
1549
+ "193": {
1550
+ "content": "</h2>",
1551
+ "lstrip": false,
1552
+ "normalized": false,
1553
+ "rstrip": false,
1554
+ "single_word": false,
1555
+ "special": false
1556
+ },
1557
+ "194": {
1558
+ "content": "</h3>",
1559
+ "lstrip": false,
1560
+ "normalized": false,
1561
+ "rstrip": false,
1562
+ "single_word": false,
1563
+ "special": false
1564
+ },
1565
+ "195": {
1566
+ "content": "</h4>",
1567
+ "lstrip": false,
1568
+ "normalized": false,
1569
+ "rstrip": false,
1570
+ "single_word": false,
1571
+ "special": false
1572
+ },
1573
+ "196": {
1574
+ "content": "</h5>",
1575
+ "lstrip": false,
1576
+ "normalized": false,
1577
+ "rstrip": false,
1578
+ "single_word": false,
1579
+ "special": false
1580
+ },
1581
+ "197": {
1582
+ "content": "</h6>",
1583
+ "lstrip": false,
1584
+ "normalized": false,
1585
+ "rstrip": false,
1586
+ "single_word": false,
1587
+ "special": false
1588
+ },
1589
+ "198": {
1590
+ "content": "</blockquote>",
1591
+ "lstrip": false,
1592
+ "normalized": false,
1593
+ "rstrip": false,
1594
+ "single_word": false,
1595
+ "special": false
1596
+ },
1597
+ "199": {
1598
+ "content": "<strong>",
1599
+ "lstrip": false,
1600
+ "normalized": false,
1601
+ "rstrip": false,
1602
+ "single_word": false,
1603
+ "special": false
1604
+ },
1605
+ "200": {
1606
+ "content": "<em>",
1607
+ "lstrip": false,
1608
+ "normalized": false,
1609
+ "rstrip": false,
1610
+ "single_word": false,
1611
+ "special": false
1612
+ },
1613
+ "201": {
1614
+ "content": "<b>",
1615
+ "lstrip": false,
1616
+ "normalized": false,
1617
+ "rstrip": false,
1618
+ "single_word": false,
1619
+ "special": false
1620
+ },
1621
+ "202": {
1622
+ "content": "<i>",
1623
+ "lstrip": false,
1624
+ "normalized": false,
1625
+ "rstrip": false,
1626
+ "single_word": false,
1627
+ "special": false
1628
+ },
1629
+ "203": {
1630
+ "content": "<u>",
1631
+ "lstrip": false,
1632
+ "normalized": false,
1633
+ "rstrip": false,
1634
+ "single_word": false,
1635
+ "special": false
1636
+ },
1637
+ "204": {
1638
+ "content": "<s>",
1639
+ "lstrip": false,
1640
+ "normalized": false,
1641
+ "rstrip": false,
1642
+ "single_word": false,
1643
+ "special": false
1644
+ },
1645
+ "205": {
1646
+ "content": "<sub>",
1647
+ "lstrip": false,
1648
+ "normalized": false,
1649
+ "rstrip": false,
1650
+ "single_word": false,
1651
+ "special": false
1652
+ },
1653
+ "206": {
1654
+ "content": "<sup>",
1655
+ "lstrip": false,
1656
+ "normalized": false,
1657
+ "rstrip": false,
1658
+ "single_word": false,
1659
+ "special": false
1660
+ },
1661
+ "207": {
1662
+ "content": "<code>",
1663
+ "lstrip": false,
1664
+ "normalized": false,
1665
+ "rstrip": false,
1666
+ "single_word": false,
1667
+ "special": false
1668
+ },
1669
+ "208": {
1670
+ "content": "</strong>",
1671
+ "lstrip": false,
1672
+ "normalized": false,
1673
+ "rstrip": false,
1674
+ "single_word": false,
1675
+ "special": false
1676
+ },
1677
+ "209": {
1678
+ "content": "</em>",
1679
+ "lstrip": false,
1680
+ "normalized": false,
1681
+ "rstrip": false,
1682
+ "single_word": false,
1683
+ "special": false
1684
+ },
1685
+ "210": {
1686
+ "content": "</b>",
1687
+ "lstrip": false,
1688
+ "normalized": false,
1689
+ "rstrip": false,
1690
+ "single_word": false,
1691
+ "special": false
1692
+ },
1693
+ "211": {
1694
+ "content": "</i>",
1695
+ "lstrip": false,
1696
+ "normalized": false,
1697
+ "rstrip": false,
1698
+ "single_word": false,
1699
+ "special": false
1700
+ },
1701
+ "212": {
1702
+ "content": "</u>",
1703
+ "lstrip": false,
1704
+ "normalized": false,
1705
+ "rstrip": false,
1706
+ "single_word": false,
1707
+ "special": false
1708
+ },
1709
+ "213": {
1710
+ "content": "</s>",
1711
+ "lstrip": false,
1712
+ "normalized": false,
1713
+ "rstrip": false,
1714
+ "single_word": false,
1715
+ "special": false
1716
+ },
1717
+ "214": {
1718
+ "content": "</sub>",
1719
+ "lstrip": false,
1720
+ "normalized": false,
1721
+ "rstrip": false,
1722
+ "single_word": false,
1723
+ "special": false
1724
+ },
1725
+ "215": {
1726
+ "content": "</sup>",
1727
+ "lstrip": false,
1728
+ "normalized": false,
1729
+ "rstrip": false,
1730
+ "single_word": false,
1731
+ "special": false
1732
+ },
1733
+ "216": {
1734
+ "content": "</code>",
1735
+ "lstrip": false,
1736
+ "normalized": false,
1737
+ "rstrip": false,
1738
+ "single_word": false,
1739
+ "special": false
1740
+ }
1741
+ },
1742
+ "additional_special_tokens": [
1743
+ "<start_of_turn>",
1744
+ "<end_of_turn>"
1745
+ ],
1746
+ "bos_token": "<bos>",
1747
+ "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 %}",
1748
+ "clean_up_tokenization_spaces": false,
1749
+ "eos_token": "<eos>",
1750
+ "model_max_length": 1000000000000000019884624838656,
1751
+ "pad_token": "<pad>",
1752
+ "sp_model_kwargs": {},
1753
+ "spaces_between_special_tokens": false,
1754
+ "tokenizer_class": "GemmaTokenizer",
1755
+ "unk_token": "<unk>",
1756
+ "use_default_system_prompt": false
1757
+ }