Gemma-2B-10M

The Gemma 2B model supports 10M sequence length, optimizes memory usage, and is suitable for large-scale language model applications.

CommonProductProgrammingLanguage ModelAttention Mechanism

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The Gemma 2B - 10M Context is a large-scale language model that, through innovative attention mechanism optimization, can process sequences up to 10M long with memory usage less than 32GB. The model employs recurrent localized attention technology, inspired by the Transformer-XL paper, making it a powerful tool for handling large-scale language tasks.

Supports text processing capability with 10M sequence length
Operates under 32GB memory
optimizing resource usage
Native inference performance optimized for CUDA
Recurrent localized attention achieving O(N) memory complexity
200 early checkpoints
planning to train more tokens to improve performance
Utilizes AutoTokenizer and GemmaForCausalLM for text generation

Suited for researchers and developers who need to handle large volumes of text data
Ideal for long text generation
summarization
translation
and other language tasks

Generate summaries for the 'Harry Potter' series books using Gemma 2B - 10M Context
Automatically generate abstracts for academic papers in the field of education
Automatically generate text content for product descriptions and market analysis in the business field

Step 1: Install the model and download the Gemma 2B - 10M Context model from huggingface
Step 2: Modify the inference code in main.py to fit specific prompt text
Step 3: Load the model tokenizer using AutoTokenizer.from_pretrained
Step 4: Load the model and specify data type as torch.bfloat16 using GemmaForCausalLM.from_pretrained
Step 5: Set the prompt text

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Gemma-2B-10M Visit Over Time

Monthly Visits

19075321

Bounce Rate

45.07%

Page per Visit

5.5

Visit Duration

00:05:32

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Gemma-2B-10M

Gemma-2B-10M Visit Over Time

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