nm-testing/openchat-3.5-0106-pruned50-quant-ds

Openchat-3.5-0106 - DeepSparse

This repo contains model files for Openchat-3.5-0106 optimized for DeepSparse, a CPU inference runtime for sparse models.

This model was quantized and pruned with SparseGPT, using SparseML.

Inference

Install DeepSparse LLM for fast inference on CPUs:

pip install deepsparse-nightly[llm]

Run in a Python pipeline:

from deepsparse import TextGeneration

prompt = "How to make banana bread?"
formatted_prompt =  f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:"

model = TextGeneration(model_path="hf:nm-testing/openchat-3.5-0106-pruned50-quant-ds")

print(model(formatted_prompt, max_new_tokens=200).generations[0].text)
"""
To make banana bread, follow these steps:

1. Preheat your oven to 350°F (175°C).

2. In a large bowl, mix together 3 ripe bananas, 1 cup of sugar, 2 beaten eggs, 1/2 cup of melted butter, and 1/2 teaspoon of salt.

3. Add 2 cups of flour, 3/4 teaspoon of baking soda, and 1 teaspoon of baking powder.

4. Mix gently until the dough is smooth and free of lumps.

5. Pour the mixture into a loaf pan that has been greased and floured.

6. Bake in a moderate oven for 30 minutes, or until a toothpick inserted in the center comes out clean.

7. When done, turn out of the pan and cool on a rack.
"""

Prompt template

  GPT4 Correct User:
  {prompt}<|end_of_turn|>
  GPT4 Correct Assistant:

Sparsification

For details on how this model was sparsified, see the recipe.yaml in this repo and follow the instructions below.

git clone https://github.com/neuralmagic/sparseml
pip install -e "sparseml[transformers]"
python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py openchat/openchat-3.5-0106 open_platypus --precision float16 --recipe recipe.yaml --save True
python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --sequence_length 4096 --model_path obcq_deployment 
cp deployment/model.onnx deployment/model-orig.onnx

Run this kv-cache injection to speed up the model at inference by caching the Key and Value states:

import os
import onnx
from sparseml.exporters.kv_cache_injector import KeyValueCacheInjector
input_file = "deployment/model-orig.onnx"
output_file = "deployment/model.onnx"
model = onnx.load(input_file, load_external_data=False)
model = KeyValueCacheInjector(model_path=os.path.dirname(input_file)).apply(model)
onnx.save(model, output_file)
print(f"Modified model saved to: {output_file}")

Follow the instructions on our One Shot With SparseML page for a step-by-step guide for performing one-shot quantization of large language models.

Slack

For further support, and discussions on these models and AI in general, join Neural Magic's Slack Community