Create README.md

README.md

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+---
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.4
+inference: false
+model_type: llama
+prompt_template: |
+  <|im_start|>user\n
+  {prompt}<|im_end|>\n
+  <|im_start|>assistant\n
+quantized_by: mwitiderrick
+tags:
+- deepsparse
+---
+## TinyLlama 1.1B Chat 0.4 - DeepSparse
+This repo contains model files for [TinyLlama 1.1B Chat](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4) optimized for [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models.
+
+This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
+
+## Inference
+Install [DeepSparse LLM](https://github.com/neuralmagic/deepsparse) for fast inference on CPUs: 
+```bash
+pip install deepsparse-nightly[llm]
+```
+Run in a [Python pipeline](https://github.com/neuralmagic/deepsparse/blob/main/docs/llms/text-generation-pipeline.md):
+```python
+from deepsparse import TextGeneration
+
+prompt = "How to make banana bread?"
+formatted_prompt =  f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+
+model = TextGeneration(model="hf:neuralmagic/TinyLlama-1.1B-Chat-v0.4-pruned50-quant")
+print(model(formatted_prompt, max_new_tokens=500).generations[0].text)
+
+"""
+Banana bread is a delicious and easy-to-make recipe that is sure to please. Here is a recipe for making banana bread:
+
+Ingredients:
+
+    For the Banana Bread:
+
+    - 1 cup of sugar
+    - 1 cup of flour
+    - 1/2 cup of mashed bananas
+    - 1/4 cup of milk
+    - 1/2 cup of melted butter
+    - 1/4 cup of baking powder
+    - 1/4 cup of baking soda
+    - 1/4 cup of eggs
+    - 1/4 cup of milk
+    - 1/4 cup of sugar
+
+
+Instructions:
+
+    1. Preheat the oven to 325°F (160°C).
+    2. In a large bowl, combine the sugar and flour.
+    3. In a separate bow, combine the mashed bananas, milk, butter, baking powder, baking soda, milk, sugar.
+    4. Add the bananas and milk into the flour-sugar mixture.
+    5. Pour the milk into the bowl of the flour-sugar mixture.
+    6. Pour the baking powder into the bowl of the flour-sugar mixture.
+    7. Pour the mashed bananas into the bowl of the flour-sugar mixture.
+    8. Add the eggs into the bowl of the flour-sugar mixture.
+    9. Stir the mixture until it becomes a dough.
+    10. Grease a 9-inch (23 cm) square pan.
+    11. Pour the mixture into the pan.
+    12. Bake the banana bread in the oven for 40 minutes.
+    13. Remove the banana bread from the oven and cool it.
+    14. Cut the bread into 16 pieces.
+    15. Make the glaze:
+    16. Sprinkle the sugar over the bread.
+    17. Bake the bread in the oven for 30 minutes.
+"""
+```
+## Prompt template
+
+```
+<|im_start|>user\n
+{prompt}<|im_end|>\n
+<|im_start|>assistant\n
+
+```
+## Sparsification
+For details on how this model was sparsified, see the `recipe.yaml` in this repo and follow the instructions below.
+
+```bash
+git clone https://github.com/neuralmagic/sparseml
+pip install -e "sparseml[transformers]"
+wget https://huggingface.co/neuralmagic/TinyLlama-1.1B-Chat-v0.4-pruned50-quant/raw/main/recipe.yaml # download recipe
+python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py TinyLlama/TinyLlama-1.1B-Chat-v0.4 open_platypus --recipe recipe.yaml --save True
+python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --model_path obcq_deployment 
+cp deployment/model.onnx deployment/model-orig.onnx
+```
+Run this kv-cache injection to speed up the model at inference by caching the Key and Value states:
+```python
+import os
+import onnx
+from sparseml.exporters.kv_cache_injector import KeyValueCacheInjector
+input_file = "deployment/model-orig.onnx"
+output_file = "deployment/model.onnx"
+model = onnx.load(input_file, load_external_data=False)
+model = KeyValueCacheInjector(model_path=os.path.dirname(input_file)).apply(model)
+onnx.save(model, output_file)
+print(f"Modified model saved to: {output_file}")
+```
+Follow the instructions on our [One Shot With SparseML](https://github.com/neuralmagic/sparseml/tree/main/src/sparseml/transformers/sparsification/obcq) page for a step-by-step guide for performing one-shot quantization of large language models. 
+## Slack
+
+For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)