Create README.md

README.md

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+---
+base_model: meta-llama/Llama-2-7b-hf
+inference: true
+model_type: llama
+datasets:
+  - cerebras/SlimPajama-627B
+tags:
+- sparse
+---
+
+# Llama-2-7b-pruned50-retrained
+
+This repo contains model files for a [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) model that has had 50% of the parameters pruned in one-shot with [SparseGPT](https://arxiv.org/abs/2301.00774), then retrained by [Cerebras](https://huggingface.co/cerebras) with 45B tokens from SlimPajama while maintaining sparsity.
+
+**Authors**: Neural Magic, Cerebras
+
+## Usage
+
+Below we share some code snippets on how to get quickly started with running the model.
+
+### Fine-tuning examples
+
+Coming soon.
+
+### Running the model
+
+```python
+# pip install transformers accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-pruned50-retrained")
+model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned50-retrained", device_map="auto")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Evaluation Benchmark Results
+
+Model evaluation metrics and results.
+
+| Benchmark                                      | Metric        | Llama-2-7b  | Llama-2-7b-pruned50-retrained |
+|------------------------------------------------|---------------|-------------|-------------------------------|
+| [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                          |
+| ------------------------------                 | ------------- | ----------- | ---------                     |
+| **Average**                                    |               | xxxx        | xxxx                          |
+
+## Model Training Data
+
+Coming soon.
+
+## Sparsification
+
+This model was pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).