初始化项目，由ModelHub XC社区提供模型

Model: RedHatAI/Llama-2-7b-pruned70-retrained
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:
+  - cerebras/SlimPajama-627B
+tags:
+- sparse
+---
+
+# Llama-2-7b-pruned70-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 50B tokens from SlimPajama while maintaining sparsity. It was then one-shot pruned to 70% sparsity and trained for another 100B tokens.
+
+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 has not been fine-tuned for instruction-following but 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("neuralmagic/Llama-2-7b-pruned70-retrained")
+model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned70-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. [UPDATE]
+
+| Benchmark                                      | Metric        | Llama-2-7b  | Llama-2-7b-pruned70-retrained |
+|------------------------------------------------|---------------|-------------|-------------------------------|
+| [MMLU](https://arxiv.org/abs/2009.03300)       | 5-shot        | 46.9%       | 36.5%                         |
+| [HellaSwag](https://arxiv.org/abs/1905.07830)  | 0-shot        | 78.6%       | 74.1%                         |
+| [WinoGrande](https://arxiv.org/abs/1907.10641) | 5-shot        | 74.0%       | 69.5%                         |
+| [ARC-c](https://arxiv.org/abs/1911.01547)      | 25-shot       | 53.1%       | 45.4%                         |
+| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 5-shot        | 38.8%       | 36.7%                         |
+| [GSM8K](https://arxiv.org/abs/2110.14168)      | 5-shot        | 14.5%       | 8.0%                          |
+| [HumanEval](https://arxiv.org/abs/2107.03374)  | pass@1        | 13.4%       | 14.4%                         |
+
+## Model Training Details
+
+[UPDATE]
+
+## 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)