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:

bash

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:

python

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

RedHatAI/OpenHermes-2.5-Mistral-7B-pruned50

OpenHermes-2.5-Mistral-7B-pruned50

Inference

Prompt template

Sparsification

Slack

RedHatAI/
OpenHermes-2.5-Mistral-7B-pruned50