metadata
base_model: NousResearch/Nous-Hermes-2-Yi-34B
inference: true
model_type: llama
quantized_by: mgoin
tags:
- nm-vllm
- sparse
Nous-Hermes-2-Yi-34B-pruned2.4
This repo contains model files for Nous Hermes 2 - Yi-34B 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/Nous-Hermes-2-Yi-34B-pruned2.4", sparsity="semi_structured_sparse_w16a16")
prompt = "How to make banana bread?"
formatted_prompt = f"<|im_start|>User:{prompt}\n<|im_start|>assistant:\n"
sampling_params = SamplingParams(max_tokens=100, temperature=0)
outputs = model.generate(formatted_prompt, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
"""
To make banana bread, follow these steps:
1. Gather the ingredients:
- 2 ripe bananas
- 2 cups of flour
- 1 teaspoon of baking powder
- 1 teaspoon of salt
- 1 teaspoon of sugar
- 1 teaspoon of vanilla extract
2. Preheat the oven to 350°F.
3. In a mixing bowl, combine the flour, baking powder, salt, sugar, and vanilla extract.
4.
"""
Prompt template
<|im_start|>User:{prompt}\n<|im_start|>assistant:\n
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 = "NousResearch/Nous-Hermes-2-Yi-34B"
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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