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README.md
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---
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base_model:
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inference: false
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model_type: llama
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prompt_template: |
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{prompt}
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quantized_by: mwitiderrick
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tags:
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- deepsparse
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---
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##
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This repo contains model files for [
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This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
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@@ -24,50 +24,14 @@ Run in a [Python pipeline](https://github.com/neuralmagic/deepsparse/blob/main/d
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```python
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from deepsparse import TextGeneration
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prompt =
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formatted_prompt = f"
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model = TextGeneration(model="
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print(model(formatted_prompt, max_new_tokens=
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"""
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Ingredients:
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For the Banana Bread:
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- 1 cup of sugar
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- 1 cup of flour
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- 1/2 cup of mashed bananas
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- 1/4 cup of milk
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- 1/2 cup of melted butter
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- 1/4 cup of baking powder
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- 1/4 cup of baking soda
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- 1/4 cup of eggs
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- 1/4 cup of milk
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- 1/4 cup of sugar
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Instructions:
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1. Preheat the oven to 325°F (160°C).
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2. In a large bowl, combine the sugar and flour.
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3. In a separate bow, combine the mashed bananas, milk, butter, baking powder, baking soda, milk, sugar.
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4. Add the bananas and milk into the flour-sugar mixture.
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5. Pour the milk into the bowl of the flour-sugar mixture.
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6. Pour the baking powder into the bowl of the flour-sugar mixture.
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7. Pour the mashed bananas into the bowl of the flour-sugar mixture.
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8. Add the eggs into the bowl of the flour-sugar mixture.
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9. Stir the mixture until it becomes a dough.
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10. Grease a 9-inch (23 cm) square pan.
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11. Pour the mixture into the pan.
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12. Bake the banana bread in the oven for 40 minutes.
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13. Remove the banana bread from the oven and cool it.
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14. Cut the bread into 16 pieces.
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15. Make the glaze:
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16. Sprinkle the sugar over the bread.
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17. Bake the bread in the oven for 30 minutes.
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"""
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```
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## Prompt template
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---
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base_model: openlm-research/open_llama_3b
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inference: false
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model_type: llama
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prompt_template: |
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Q:
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{prompt}
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\nA
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quantized_by: mwitiderrick
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tags:
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- deepsparse
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---
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## OpenLLaMA 3B - DeepSparse
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This repo contains model files for [OpenLLaMA](https://huggingface.co/openlm-research/open_llama_3b) optimized for [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models.
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This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
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```python
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from deepsparse import TextGeneration
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prompt = 'Q: What is the largest animal?\nA:'
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formatted_prompt = f"Q: {prompt}\nA:"
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model = TextGeneration(model="deployment")
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print(model(formatted_prompt, max_new_tokens=200).generations[0].text)
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"""
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the in the in in the in in in in in the in the the in in the in in the the the the the in the the in the the in the the in the in the the in the the in the the in in the the the the in in in the the the the in the in in the the the the in the the in the the in the the the the the the the in the the the in the the the the in the the the the in in the the the the the the the in the the the the in the the the in the the in the the the in the the the the the in the the the the the the the the the in the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the
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"""
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```
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## Prompt template
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