OpenHermes-2.5-Mistral-7B-pruned50
This repo contains model files for OpenHermes-2.5-Mistral-7B 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/OpenHermes-2.5-Mistral-7B-pruned50", sparsity="sparse_w16a16")
prompt = "How to make banana bread?"
formatted_prompt = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
sampling_params = SamplingParams(max_tokens=100)
outputs = model.generate(formatted_prompt, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
"""
Here is a simple recipe for making banana bread:
Ingredients:
- 3 ripe bananas
- 2 eggs
- 1/2 cup of sugar
- 1/2 cup of butter
- 2 cups of flour
- 1 teaspoon baking powder
- 2 teaspoons of baking soda
Instructions:
1. Preheat your oven at 350 degree Fahrenant.
"""
Prompt template
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
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 = "teknium/OpenHermes-2.5-Mistral-7B"
calibration_dataset = "open_platypus"
output_directory = "output/"
recipe = """
test_stage:
obcq_modifiers:
SparseGPTModifier:
sparsity: 0.5
sequential_update: true
mask_structure: 0:0
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
- Downloads last month
- 358
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.