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Running Llama 2 Chat Model on Google Colab
Now, let me explain how it works in simpler terms: imagine you’re having a conversation with someone and they ask you a question. The…
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LlamaModel Configuration for Tensor Parallelism
Here’s an example: say you have a huge dataset with millions of rows, but your computer only has one CPU core to handle it…
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Transformers for Text Generation
So, how does this work exactly? First off, transformers are like magic wands for text generation they take a bunch of words and turn…
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Fine-Tuning Models for Better Performance
For example, let’s say you have a bunch of pictures of cats and dogs, but your model only knows how to identify cats or…
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Using Key-Value Cache in Transformers for Efficient Decoding
This can be time-consuming if you have long sequences or are running on slower hardware. But what if we could save some of these…
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Optimizing Flash Attention for Inference in LLMs
These techniques allow for more efficient computation of attention scores, which can significantly improve performance on long text inputs. In traditional self-attention mechanisms, each…
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Python Quantization for Memory Efficiency: Achieving High Accuracy with Low Resource Consumption
First off, let me explain what quantization is in simpler terms. It’s like taking a picture and compressing it to make it smaller without…
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How to Download and Use Pretrained Models for Natural Language Processing in Python
It is often used as a weighting factor in text searches and classification learning algorithms, and can be applied at either the document level…
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Transformers Offline Mode
Here’s how it works: first, you gather up a bunch of text data that your transformer will learn from. This could be anything from…
