Create README.md

README.md

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+---
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+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-v1.0) 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-ds")
+print(model(formatted_prompt, max_new_tokens=500).generations[0].text)
+
+"""
+
+
+"""
+```
+## 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]"
+python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py TinyLlama/TinyLlama-1.1B-Chat-v1.0 open_platypus --precision float16  --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)