metadata
base_model: Xenova/llama2.c-stories110M
inference: true
model_type: llama
quantized_by: mgoin
tags:
- nm-vllm
- sparse
llama2.c-stories110M-pruned50
This repo contains model files for llama2.c 110M tinystories optimized for NM-vLLM, a high-throughput serving engine for compressed LLMs.
This model was pruned with SparseGPT, using SparseML.
Inference
Install NM-vLLM for fast inference and low memory-usage:
pip install nm-vllm[sparse]
Run in a Python pipeline for local inference:
from vllm import LLM, SamplingParams
model = LLM("nm-testing/llama2.c-stories110M-pruned50", sparsity="sparse_w16a16")
prompt = "Hello my name is"
sampling_params = SamplingParams(max_tokens=100, temperature=0)
outputs = model.generate(prompt, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
Prompt template
N/A
Sparsification
For details on how this model was sparsified, see the recipe.yaml in this repo and follow the instructions below.
Install SparseML:
git clone https://github.com/neuralmagic/sparseml
pip install -e "sparseml[transformers]"
Replace the recipe as you like and run this one-shot compression script to apply SparseGPT:
import sparseml.transformers
original_model_name = "Xenova/llama2.c-stories110M"
calibration_dataset = "open_platypus"
output_directory = "output/"
recipe = """
test_stage:
obcq_modifiers:
SparseGPTModifier:
sparsity: 0.5
sequential_update: true
targets: ['re:model.layers.\d*$']
"""
# Apply SparseGPT to the model
sparseml.transformers.oneshot(
model=original_model_name,
dataset=calibration_dataset,
recipe=recipe,
output_dir=output_directory,
)
Slack
For further support, and discussions on these models and AI in general, join Neural Magic's Slack Community