Update README.md

README.md

@@ -1,11 +1,25 @@
 ---
+base_model: HuggingFaceH4/zephyr-7b-beta
+inference: false
+model_type: mistral
+prompt_template: |
+  ### Instruction:\n
+  {prompt}
+  ### Response:\n
+quantized_by: mwitiderrick
 tags:
 - deepsparse
 ---
-## DeepSparse One-Shot and Export of https://huggingface.co/HuggingFaceH4/zephyr-7b-beta
-
-## Usage
+##  Zephyr 7B β - DeepSparse 
+This repo contains model files for [Zephyr 7B β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) 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='### Instruction:\nWrite a Perl script that processes a log file and counts the occurrences of different HTTP status codes. The script should accept the log file path as a command-line argument and print the results to the console in descending order of frequency.\n\n### Response:\n'
@@ -55,4 +69,38 @@ foreach my ($code, $freq) (@sorted) {
 }
 ```
 """
-```
+```
+
+## Prompt template
+```
+
+  ### Instruction:\n
+  {prompt}
+  ### Response:\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 HuggingFaceH4/zephyr-7b-beta 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)