Create README.md

README.md

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+---
+base_model: neuralmagic/Llama-2-7b-pruned70-retrained-ultrachat
+inference: false
+model_type: llama
+pipeline_tag: text-generation
+datasets:
+  - cerebras/SlimPajama-627B
+  - HuggingFaceH4/ultrachat_200k
+tags:
+- sparse
+- chat
+- deepsparse
+---
+
+# Llama-2-7b-pruned70-retrained-ultrachat-quant-ds
+
+This repo contains a [70% sparse Llama 2 7B](https://huggingface.co/neuralmagic/Llama-2-7b-pruned70-retrained) finetuned for chat tasks using the [UltraChat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset. 
+It was then quantized to 8-bit weights + activations and exported to deploy with [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models.
+
+**Authors**: Neural Magic, Cerebras
+
+## Usage
+
+Below we share some code snippets on how to get quickly started with running the model.
+
+### Sparse Transfer
+
+By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process [here](https://neuralmagic.github.io/docs-v2/get-started/transfer).
+
+### Running the model
+
+For accelerated inference with sparsity on CPUs, deploy with [deepsparse](https://github.com/neuralmagic/deepsparse).
+
+```python
+# pip install deepsparse[llm]
+from deepsparse import TextGeneration
+
+model = TextGeneration(model_path="hf:neuralmagic/Llama-2-7b-pruned70-retrained-ultrachat-quant-ds")
+
+input_text = "Write me a poem about Machine Learning."
+outputs = model(formatted_prompt, max_new_tokens=100)
+print(outputs.generations[0].text)
+```
+
+## Evaluation Benchmark Results
+
+Model evaluation metrics and results.
+
+| Benchmark                                      | Metric        | Llama-2-7b-ultrachat  | Llama-2-7b-pruned70-retrained-ultrachat-quant-ds |
+|------------------------------------------------|---------------|-------------|-------------------------------|
+| [MMLU](https://arxiv.org/abs/2009.03300)       | 5-shot, top-1 | xxxx        | xxxx                          |
+| [HellaSwag](https://arxiv.org/abs/1905.07830)  | 0-shot        | xxxx        | xxxx                          |
+| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | xxxx        | xxxx                          |
+| [ARC-c](https://arxiv.org/abs/1911.01547)      |               | xxxx        | xxxx                          |
+| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 5-shot        | xxxx        | xxxx                          |
+| [HumanEval](https://arxiv.org/abs/2107.03374)  | pass@1        | xxxx        | xxxx                          |
+| [GSM8K](https://arxiv.org/abs/2110.14168)      | maj@1         | xxxx        | xxxx                          |
+
+## Model Training Details
+
+Coming soon.
+
+## Help
+
+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)