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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).
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