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This repo contains a Llama 2 7B finetuned for code generation tasks using the Evolved CodeAlpaca dataset.

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

Authors: Neural Magic, Cerebras


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("neuralmagic/Llama-2-7b-evolcodealpaca")
model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-evolcodealpaca", device_map="auto")

input_text = "def fibonacci(n):\n"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)

Evaluation Benchmark Results

Model evaluation metrics and results.

Benchmark Metric Llama-2-7b-evolcodealpaca
HumanEval pass@1 32.03

Model Training Details

Coming soon.


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Model size
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Finetuned from

Dataset used to train neuralmagic/Llama-2-7b-evolcodealpaca

Collection including neuralmagic/Llama-2-7b-evolcodealpaca