Llama-2-7b-instruct

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

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

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