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 1.0 - 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_path="hf:nm-testing/TinyLlama-1.1B-Chat-v1.0-pruned50-quant-ds")
+print(model(formatted_prompt, max_new_tokens=200).generations[0].text)
+
+"""
+1. Preheat the oven to 375°F (178°C).
+2. In a mixing bowl, add 1 cup of all-purpose flour, 1 cup of melted coconut oil, 1/2 cup of sugar, 1/2 cup of banana, 1/2 cup of melted coconut oil, 1/2 cup of salt, 1/2 cup of vanilla extract, and 1/2 cup of baking powder.
+3. Mix the ingredients together until they are well combined.
+4. Add 1/2 cup of melted coconut oil to the mixture.
+5. Add 1/2 cup of melted coconut oil to the mixture.
+6. Mix the ingredients together until they are well combined.
+7. Add 1/2 cup of melted
+
+"""
+```
+## 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
+```
+## Sparse Finetuning
+Continue training the sparse model to improve accuracy: 
+
+```python
+from sparseml.transformers.finetune.text_generation import run_train
+
+
+model = "./obcq_deployment"
+teacher_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+dataset_name = "open_platypus"
+concatenate_data = False
+output_dir = "./output_finetune"
+recipe = "recipe.yaml"
+num_train_epochs=2
+overwrite_output_dir = True
+splits = {
+    "train": "train[:50%]",
+}
+
+run_train(
+    model_name_or_path=model,
+    distill_teacher=teacher_model,
+    dataset_name=dataset_name,
+    output_dir=output_dir,
+    recipe=recipe,
+    num_train_epochs=num_train_epochs,
+    overwrite_output_dir=overwrite_output_dir,
+    concatenate_data = concatenate_data,
+    splits = splits
+)
+```
+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)