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Add use this model

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  1. README.md +19 -3
README.md CHANGED
@@ -16,20 +16,36 @@ library_name: transformers
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  This model is a clone of [**ibm-granite/granite-7b-instruct**](https://huggingface.co/ibm-granite/granite-7b-instruct) compressed using ZipNN. Compressed losslessly to 67% its original size, ZipNN saved ~5GB in storage and potentially ~30TB in data transfer **monthly**.
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- ## Requirement
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  In order to use the model, ZipNN is necessary:
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  ```bash
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  pip install zipnn
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  ```
 
 
 
 
 
 
 
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- Then simply add at the beginning of the file
 
 
 
 
 
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  ```python
 
 
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  from zipnn import zipnn_hf
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  zipnn_hf()
 
 
 
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  ```
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- And continue as usual. The patch will take care of decompressing the model correctly and safely.
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  # Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081)
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  This model is a clone of [**ibm-granite/granite-7b-instruct**](https://huggingface.co/ibm-granite/granite-7b-instruct) compressed using ZipNN. Compressed losslessly to 67% its original size, ZipNN saved ~5GB in storage and potentially ~30TB in data transfer **monthly**.
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+ ### Requirement
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  In order to use the model, ZipNN is necessary:
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  ```bash
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  pip install zipnn
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  ```
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+ ### Use This Model
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+ ```python
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+ # Use a pipeline as a high-level helper
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+ from transformers import pipeline
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+ from zipnn import zipnn_hf
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+
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+ zipnn_hf()
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+ messages = [
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+ {"role": "user", "content": "Who are you?"},
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+ ]
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+ pipe = pipeline("text-generation", model="royleibov/granite-7b-instruct-ZipNN-Compressed")
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+ pipe(messages)
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+ ```
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  ```python
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+ # Load model directly
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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  from zipnn import zipnn_hf
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  zipnn_hf()
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+
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+ tokenizer = AutoTokenizer.from_pretrained("royleibov/granite-7b-instruct-ZipNN-Compressed")
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+ model = AutoModelForCausalLM.from_pretrained("royleibov/granite-7b-instruct-ZipNN-Compressed")
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  ```
 
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  # Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081)
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