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+ ---
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+ library_name: transformers
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+ tags: []
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+ widget:
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+ - text: |
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+ <start_of_turn>user
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+ How does the brain work?<end_of_turn>
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+ <start_of_turn>model
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+
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+ ---
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+ # Reupload of Gemma 2b it - Original readme below
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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 2B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [7B instruct model](https://huggingface.co/google/gemma-7b-it).
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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?version=gemma-2b-it-gg-hf)
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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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+
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+ #### Running the model on a CPU
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+
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
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+ model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
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+
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+ input_text = "Write me a poem about Machine Learning."
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+ input_ids = tokenizer(**input_text, return_tensors="pt")
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+
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+ outputs = model.generate(input_ids)
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+ print(tokenizer.decode(outputs[0]))
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+ ```
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+
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+
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+ #### Running the model on a single / multi GPU
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+
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+
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+ ```python
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+ # pip install accelerate
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
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+ model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto")
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+
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+ input_text = "Write me a poem about Machine Learning."
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+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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+
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+ outputs = model.generate(**input_ids)
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+ print(tokenizer.decode(outputs[0]))
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+ ```
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+
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+
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+ #### Running the model on a GPU using different precisions
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+
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+ * _Using `torch.float16`_
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+
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+ ```python
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+ # pip install accelerate
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
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+ model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.float16)
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+
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+ input_text = "Write me a poem about Machine Learning."
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+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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+
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+ outputs = model.generate(**input_ids)
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+ print(tokenizer.decode(outputs[0]))
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+ ```
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+
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+ * _Using `torch.bfloat16`_
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+
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+ ```python
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+ # pip install accelerate
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
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+ model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.bfloat16)
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+
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+ input_text = "Write me a poem about Machine Learning."
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+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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+
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+ outputs = model.generate(**input_ids)
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+ print(tokenizer.decode(outputs[0]))
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+ ```
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+
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+ #### Quantized Versions through `bitsandbytes`
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+
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+ * _Using 8-bit precision (int8)_
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+
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+ ```python
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+ # pip install bitsandbytes accelerate
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+ from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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+
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+ quantization_config = BitsAndBytesConfig(load_in_8bit=True)
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
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+ model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config)
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+
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+ input_text = "Write me a poem about Machine Learning."
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+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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+
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+ outputs = model.generate(**input_ids)
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+ print(tokenizer.decode(outputs[0]))
137
+ ```
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+
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+ * _Using 4-bit precision_
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+
141
+ ```python
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+ # pip install bitsandbytes accelerate
143
+ from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
144
+
145
+ quantization_config = BitsAndBytesConfig(load_in_4bit=True)
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+
147
+ tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
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+ model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config)
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+
150
+ input_text = "Write me a poem about Machine Learning."
151
+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
152
+
153
+ outputs = model.generate(**input_ids)
154
+ print(tokenizer.decode(outputs[0]))
155
+ ```
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+
157
+
158
+ #### Other optimizations
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+
160
+ * _Flash Attention 2_
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+
162
+ First make sure to install `flash-attn` in your environment `pip install flash-attn`
163
+
164
+ ```diff
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+ model = AutoModelForCausalLM.from_pretrained(
166
+ model_id,
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+ torch_dtype=torch.float16,
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+ + attn_implementation="flash_attention_2"
169
+ ).to(0)
170
+ ```
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+
172
+ ### Chat Template
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+
174
+ The instruction-tuned models use a chat template that must be adhered to for conversational use.
175
+ The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
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+
177
+ Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
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+
179
+ ```py
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
181
+ import transformers
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+ import torch
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+
184
+ model_id = "gg-hf/gemma-2b-it"
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+ dtype = torch.bfloat16
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_id,
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+ device_map="cuda",
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+ torch_dtype=dtype,
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+ )
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+
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+ chat = [
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+ { "role": "user", "content": "Write a hello world program" },
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+ ]
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+ prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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+ ```
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+
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+ At this point, the prompt contains the following text:
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+
202
+ ```
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+ <start_of_turn>user
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+ Write a hello world program<end_of_turn>
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+ <start_of_turn>model
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+ ```
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+
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+ As you can see, each turn is preceeded by a `<start_of_turn>` delimiter and then the role of the entity
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+ (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
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+ the `<end_of_turn>` token.
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+
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+ You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
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+ chat template.
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+
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+ After the prompt is ready, generation can be performed like this:
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+
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+ ```py
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+ inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt")
219
+ outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
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+ ```
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+
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+ ### Inputs and outputs
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+
224
+ * **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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+
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+ ## Model Data
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+
231
+ Data used for model training and how the data was processed.
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+
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+ ### Training Dataset
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+
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+ 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.
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+
247
+ The combination of these diverse data sources is crucial for training a powerful
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+ language model that can handle a wide variety of different tasks and text
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+ formats.
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+
251
+ ### Data Preprocessing
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+
253
+ Here are the key data cleaning and filtering methods applied to the training
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+ data:
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+
256
+ * 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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+
265
+ ## Implementation Information
266
+
267
+ Details about the model internals.
268
+
269
+ ### Hardware
270
+
271
+ Gemma was trained using the latest generation of
272
+ [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
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+
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+ 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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+
278
+ * 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
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+ for the handling of large models and batch sizes during training. This can
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+ lead to better model quality.
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+ * 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.
287
+ * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
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+ solution for training large models compared to CPU-based infrastructure,
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+ especially when considering the time and resources saved due to faster
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+ training.
291
+ * These advantages are aligned with
292
+ [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
293
+
294
+ ### Software
295
+
296
+ 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).
297
+
298
+ JAX allows researchers to take advantage of the latest generation of hardware,
299
+ including TPUs, for faster and more efficient training of large models.
300
+
301
+ ML Pathways is Google's latest effort to build artificially intelligent systems
302
+ capable of generalizing across multiple tasks. This is specially suitable for
303
+ [foundation models](https://ai.google/discover/foundation-models/), including large language models like
304
+ these ones.
305
+
306
+ Together, JAX and ML Pathways are used as described in the
307
+ [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
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+ controller' programming model of Jax and Pathways allows a single Python
309
+ process to orchestrate the entire training run, dramatically simplifying the
310
+ development workflow."
311
+
312
+ ## Evaluation
313
+
314
+ Model evaluation metrics and results.
315
+
316
+ ### Benchmark Results
317
+
318
+ These models were evaluated against a large collection of different datasets and
319
+ metrics to cover different aspects of text generation:
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+
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+ | Benchmark | Metric | 2B Params | 7B Params |
322
+ | ------------------------------ | ------------- | ----------- | --------- |
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+ | [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 |
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+ | [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
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+ | [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 59.7 | 51.8 |
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+ | [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
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+ | [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
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+ | [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
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+ | [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
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+ | [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
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+ | [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
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+ | [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
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+ | [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | - | 23 |
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+ | [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
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+ | [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
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+ | [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
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+ | [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
339
+ | [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
340
+ | [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
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+ | ------------------------------ | ------------- | ----------- | --------- |
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+ | **Average** | | **54.0** | **56.4** |
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+
344
+ ## Ethics and Safety
345
+
346
+ Ethics and safety evaluation approach and results.
347
+
348
+ ### Evaluation Approach
349
+
350
+ Our evaluation methods include structured evaluations and internal red-teaming
351
+ testing of relevant content policies. Red-teaming was conducted by a number of
352
+ different teams, each with different goals and human evaluation metrics. These
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+ models were evaluated against a number of different categories relevant to
354
+ ethics and safety, including:
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+
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+ * Text-to-Text Content Safety: Human evaluation on prompts covering safety
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+ policies including child sexual abuse and exploitation, harassment, violence
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+ and gore, and hate speech.
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+ * Text-to-Text Representational Harms: Benchmark against relevant academic
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+ datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
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+ * Memorization: Automated evaluation of memorization of training data, including
362
+ the risk of personally identifiable information exposure.
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+ * Large-scale harm: Tests for "dangerous capabilities," such as chemical,
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+ biological, radiological, and nuclear (CBRN) risks.
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+
366
+ ### Evaluation Results
367
+
368
+ The results of ethics and safety evaluations are within acceptable thresholds
369
+ 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
370
+ safety, content safety, representational harms, memorization, large-scale harms.
371
+ On top of robust internal evaluations, the results of well known safety
372
+ benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
373
+ are shown here.
374
+
375
+ | Benchmark | Metric | 2B Params | 7B Params |
376
+ | ------------------------------ | ------------- | ----------- | --------- |
377
+ | [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
378
+ | [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
379
+ | [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
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+ | [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
381
+ | [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
382
+ | [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
383
+ | [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 |
384
+ | [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
385
+ | [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
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+ | [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
387
+ | ------------------------------ | ------------- | ----------- | --------- |
388
+
389
+
390
+ ## Usage and Limitations
391
+
392
+ These models have certain limitations that users should be aware of.
393
+
394
+ ### Intended Usage
395
+
396
+ Open Large Language Models (LLMs) have a wide range of applications across
397
+ various industries and domains. The following list of potential uses is not
398
+ comprehensive. The purpose of this list is to provide contextual information
399
+ about the possible use-cases that the model creators considered as part of model
400
+ training and development.
401
+
402
+ * Content Creation and Communication
403
+ * Text Generation: These models can be used to generate creative text formats
404
+ such as poems, scripts, code, marketing copy, and email drafts.
405
+ * Chatbots and Conversational AI: Power conversational interfaces for customer
406
+ service, virtual assistants, or interactive applications.
407
+ * Text Summarization: Generate concise summaries of a text corpus, research
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+ papers, or reports.
409
+ * Research and Education
410
+ * Natural Language Processing (NLP) Research: These models can serve as a
411
+ foundation for researchers to experiment with NLP techniques, develop
412
+ algorithms, and contribute to the advancement of the field.
413
+ * Language Learning Tools: Support interactive language learning experiences,
414
+ aiding in grammar correction or providing writing practice.
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+ * Knowledge Exploration: Assist researchers in exploring large bodies of text
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+ by generating summaries or answering questions about specific topics.
417
+
418
+ ### Limitations
419
+
420
+ * Training Data
421
+ * The quality and diversity of the training data significantly influence the
422
+ model's capabilities. Biases or gaps in the training data can lead to
423
+ limitations in the model's responses.
424
+ * The scope of the training dataset determines the subject areas the model can
425
+ handle effectively.
426
+ * Context and Task Complexity
427
+ * LLMs are better at tasks that can be framed with clear prompts and
428
+ instructions. Open-ended or highly complex tasks might be challenging.
429
+ * A model's performance can be influenced by the amount of context provided
430
+ (longer context generally leads to better outputs, up to a certain point).
431
+ * Language Ambiguity and Nuance
432
+ * Natural language is inherently complex. LLMs might struggle to grasp subtle
433
+ nuances, sarcasm, or figurative language.
434
+ * Factual Accuracy
435
+ * LLMs generate responses based on information they learned from their
436
+ training datasets, but they are not knowledge bases. They may generate
437
+ incorrect or outdated factual statements.
438
+ * Common Sense
439
+ * LLMs rely on statistical patterns in language. They might lack the ability
440
+ to apply common sense reasoning in certain situations.
441
+
442
+ ### Ethical Considerations and Risks
443
+
444
+ The development of large language models (LLMs) raises several ethical concerns.
445
+ In creating an open model, we have carefully considered the following:
446
+
447
+ * Bias and Fairness
448
+ * LLMs trained on large-scale, real-world text data can reflect socio-cultural
449
+ biases embedded in the training material. These models underwent careful
450
+ scrutiny, input data pre-processing described and posterior evaluations
451
+ reported in this card.
452
+ * Misinformation and Misuse
453
+ * LLMs can be misused to generate text that is false, misleading, or harmful.
454
+ * Guidelines are provided for responsible use with the model, see the
455
+ [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
456
+ * Transparency and Accountability:
457
+ * This model card summarizes details on the models' architecture,
458
+ capabilities, limitations, and evaluation processes.
459
+ * A responsibly developed open model offers the opportunity to share
460
+ innovation by making LLM technology accessible to developers and researchers
461
+ across the AI ecosystem.
462
+
463
+ Risks identified and mitigations:
464
+
465
+ * Perpetuation of biases: It's encouraged to perform continuous monitoring
466
+ (using evaluation metrics, human review) and the exploration of de-biasing
467
+ techniques during model training, fine-tuning, and other use cases.
468
+ * Generation of harmful content: Mechanisms and guidelines for content safety
469
+ are essential. Developers are encouraged to exercise caution and implement
470
+ appropriate content safety safeguards based on their specific product policies
471
+ and application use cases.
472
+ * Misuse for malicious purposes: Technical limitations and developer and
473
+ end-user education can help mitigate against malicious applications of LLMs.
474
+ Educational resources and reporting mechanisms for users to flag misuse are
475
+ provided. Prohibited uses of Gemma models are outlined in the
476
+ [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
477
+ * Privacy violations: Models were trained on data filtered for removal of PII
478
+ (Personally Identifiable Information). Developers are encouraged to adhere to
479
+ privacy regulations with privacy-preserving techniques.
480
+
481
+ ### Benefits
482
+
483
+ At the time of release, this family of models provides high-performance open
484
+ large language model implementations designed from the ground up for Responsible
485
+ AI development compared to similarly sized models.
486
+
487
+ Using the benchmark evaluation metrics described in this document, these models
488
+ have shown to provide superior performance to other, comparably-sized open model
489
+ alternatives.
490
+
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