metadata
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
inference: false
model_type: llama
prompt_template: |
<|im_start|>user\n
{prompt}<|im_end|>\n
<|im_start|>assistant\n
quantized_by: mwitiderrick
tags:
- deepsparse
TinyLlama 1.1B Chat 1.0 - DeepSparse
This repo contains model files for TinyLlama 1.1B Chat 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"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
model = TextGeneration(model_path="hf:nm-testing/TinyLlama-1.1B-Chat-v1.0-pruned50-quant-ds")
print(model(formatted_prompt, max_new_tokens=200).generations[0].text)
"""
Sure, here's a recipe for making banana bread:
Ingredients:
- 1 cup of all-purpose flour
- 1 cup of instant potatoes
- 1 cup of unsalted instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
- 1/2 cup of instant potatoes
-
"""
Prompt template
<|im_start|>user\n
{prompt}<|im_end|>\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.
git clone https://github.com/neuralmagic/sparseml
pip install -e "sparseml[transformers]"
python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py TinyLlama/TinyLlama-1.1B-Chat-v1.0 open_platypus --precision float16 --recipe recipe.yaml --save True
python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --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