Llama-2-7b-pruned50-retrained-instruct

This repo contains a 50% sparse Llama 2 7B finetuned for instruction-following tasks using a blend of the Platypus + Open Orca + Dolphin datasets.

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 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("Llama-2-7b-pruned50-retrained-instruct")
model = AutoModelForCausalLM.from_pretrained("Llama-2-7b-pruned50-retrained-instruct", device_map="auto")

input_text = "Write a recipe for banana bread:\n"
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-instruct	Llama-2-7b-pruned50-retrained-instruct
MMLU	5-shot	48.60%	45.10%
HellaSwag	10-shot	79.45%	78.86%
WinoGrande	5-shot	75.69%	72.61%
ARC-c	25-shot	53.92%	50.77%
TruthfulQA	0-shot	43.63%	44.40%
GSM8K	5-shot	15.92%	16.38%

Model Training Details

This model was obtained by sparse-tranfer of the sparse foundational model Llama-2-7b-pruned50-retrained on a blend of Open Platypus, 10% Open Orca and 10% Dolphin datasets. Training was perfomerd for 2 epochs.

Help

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