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)
"""
"""
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
Sparse Finetuning
Continue training the sparse model to improve accuracy:
from sparseml.transformers.finetune.text_generation import run_train
model = "./obcq_deployment"
teacher_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
dataset_name = "open_platypus"
concatenate_data = False
output_dir = "./output_finetune"
recipe = "recipe.yaml"
num_train_epochs=2
overwrite_output_dir = True
splits = {
"train": "train[:50%]",
}
run_train(
model_name_or_path=model,
distill_teacher=teacher_model,
dataset_name=dataset_name,
output_dir=output_dir,
recipe=recipe,
num_train_epochs=num_train_epochs,
overwrite_output_dir=overwrite_output_dir,
concatenate_data = concatenate_data,
splits = splits
)
Export Model
Export the model while injecting the KV Cache
sparseml.export --task text-generation output_finetune/
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