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Model: neuralmagic/Llama-2-7b-dolphin-open_platypus-pruned_50
Source: Original Platform

README.md

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+---
+base_model: neuralmagic/Llama-2-7b-pruned50-retrained
+inference: true
+model_type: llama
+pipeline_tag: text-generation
+datasets:
+  - garage-bAInd/Open-Platypus
+  - Open-Orca/OpenOrca
+  - cognitivecomputations/dolphin
+tags:
+- sparse
+- instruct
+---
+
+# Llama-2-7b-pruned50-retrained-instruct
+
+This repo contains a [50% sparse Llama 2 7B](https://huggingface.co/neuralmagic/Llama-2-7b-pruned50-retrained) finetuned for instruction-following tasks using a blend of the Platypus + Open Orca + Dolphin datasets.
+
+Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594).
+
+**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
+
+This model may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).
+
+```python
+# pip install transformers accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Llama-2-7b-pruned50-retrained-instruct")
+model = AutoModelForCausalLM.from_pretrained("Llama-2-7b-pruned50-retrained-instruct", device_map="auto")
+
+input_text = "Write a recipe for banana bread:\n"
+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-instruct  | Llama-2-7b-pruned50-retrained-instruct |
+|------------------------------------------------|---------------|-------------|-------------------------------|
+| [MMLU](https://arxiv.org/abs/2009.03300)       | 5-shot        | 48.60%  | 45.10% |
+| [HellaSwag](https://arxiv.org/abs/1905.07830)  | 10-shot       | 79.45%  | 78.86% |
+| [WinoGrande](https://arxiv.org/abs/1907.10641) | 5-shot        | 75.69%  | 72.61% |
+| [ARC-c](https://arxiv.org/abs/1911.01547)      | 25-shot       | 53.92%  | 50.77% |
+| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 0-shot        | 43.63%  | 44.40% |
+| [GSM8K](https://arxiv.org/abs/2110.14168)      | 5-shot        | 15.92%  | 16.38% |
+
+## Model Training Details
+
+This model was obtained by sparse-tranfer of the sparse foundational model [Llama-2-7b-pruned50-retrained](https://huggingface.co/neuralmagic/Llama-2-7b-pruned70-retrained) on a blend of [Open Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus), 10% [Open Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca) and 10% [Dolphin](https://huggingface.co/datasets/cognitivecomputations/dolphin) datasets.
+Training was perfomerd for 2 epochs.
+
+## 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)