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 model that has had 50% of the parameters pruned in one-shot with SparseGPT, then retrained by Cerebras with 50B tokens from SlimPajama while maintaining sparsity. It was then one-shot pruned to 70% sparsity and trained for another 100B tokens.
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.
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 or deepsparse.
# 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 | 5-shot | 46.9% | 36.5% |
| HellaSwag | 0-shot | 78.6% | 74.1% |
| WinoGrande | 5-shot | 74.0% | 69.5% |
| ARC-c | 25-shot | 53.1% | 45.4% |
| TruthfulQA | 5-shot | 38.8% | 36.7% |
| GSM8K | 5-shot | 14.5% | 8.0% |
| HumanEval | pass@1 | 13.4% | 14.4% |
Model Training Details
[UPDATE]
Help
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