neuralmagic/OpenHermes-2.5-Mistral-7B-pruned2.4

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-pruned2.4", sparsity="semi_structured_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)
"""
In order to make banana bread, you will need to follow these steps:

1. Prepare the ingredients: You will need flour, sugar, eggs, and bananas.
2. Prepare your ingredients: Prepare your bananas, flour, sugar, and eggs by preparing them in their respective bowls, ready to prepare the banana bread.
3. Make the batter: You will prepare batter by combining the flour, sugar, eggs and bananas. This
"""

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: '2:4'
      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

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