This repo contains a 50% sparse Llama 2 7B finetuned for code generation tasks using the Evolved CodeAlpaca dataset. It was then quantized to 8-bit weights + activations and exported to deploy with DeepSparse, a CPU inference runtime for sparse models.

Official model weights from Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment.

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

For accelerated inference with sparsity on CPUs, deploy with deepsparse.

python

# pip install deepsparse[llm]
from deepsparse import TextGeneration

model = TextGeneration(model_path="hf:neuralmagic/Llama-2-7b-pruned50-retrained-evolcodealpaca-quant-ds")

input_text = "def fibonacci(n):\n"
outputs = model(input_text, max_new_tokens=100)
print(outputs.generations[0].text)

Evaluation Benchmark Results

Model evaluation metrics and results.

Benchmark	Metric	Llama-2-7b-evolcodealpaca	Llama-2-7b-pruned50-retrained-evolcodealpaca-quant-ds
HumanEval	pass@1	32.03	36.34

Help

For further support, and discussions on these models and AI in general, join Neural Magic's Slack Community

RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50-quantized-deepsparse

Llama-2-7b-pruned50-retrained-evolcodealpaca-quant-ds

Usage

Sparse Transfer

Running the model

Evaluation Benchmark Results

Help

RedHatAI/
Llama-2-7b-evol-code-alpaca-pruned_50-quantized-deepsparse