Update README.md

README.md

@@ -15,8 +15,12 @@ This repo contains model files for [TinyLlama 1.1B Chat](https://huggingface.co/
 
 This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
 
-## Usage
-
+## 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
 
@@ -97,7 +101,8 @@ There are many factors to consider when choosing a university. Here are some tip
 <|im_start|>assistant\n
 
 ```
-## One-shot and Export
+## 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
@@ -106,10 +111,20 @@ wget https://huggingface.co/nm-testing/TinyLlama-1.1B-Chat-v0.4-pruned50-quant/r
 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
-wget https://huggingface.co/nm-testing/TinyLlama-1.1B-Chat-v0.4-pruned50-quant/raw/main/onnx_kv_inject.py # kv_cache file
-python onnx_kv_inject.py --input-file deployment/model-orig.onnx --output-file deployment/model.onnx
 ```
-
+Run this kv-cache injection afterwards:
+```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] guide for step-by-step instruction on performing one-shot quantization on your own large language models. 
 ## Slack
 
 For further support, and discussions on these models and AI in general, join us at [Neural Magic's Slack server](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)