base_model: meta-llama/Llama-2-7b-hf
inference: true
model_type: llama
pipeline_tag: text-generation
datasets:
- cerebras/SlimPajama-627B
tags:
- sparse
Llama-2-7b-pruned50-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 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.
Sparse Transfer
You can adapt pruned large language models (LLMs) to new domains and tasks using sparse transfer learning. 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-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 | 5-shot, top-1 | xxxx | xxxx |
| HellaSwag | 0-shot | xxxx | xxxx |
| WinoGrande | partial score | xxxx | xxxx |
| ARC-c | xxxx | xxxx | |
| TruthfulQA | 5-shot | xxxx | xxxx |
| HumanEval | pass@1 | xxxx | xxxx |
| GSM8K | maj@1 | xxxx | xxxx |
Model Training Details
Coming soon.
Help
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