RedHatAI/Llama-2-7b-pruned70-retrained
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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)

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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