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---
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](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) optimized for [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models.
This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
## Inference
Install [DeepSparse LLM](https://github.com/neuralmagic/deepsparse) for fast inference on CPUs:
```bash
pip install deepsparse-nightly[llm]
```
Run in a [Python pipeline](https://github.com/neuralmagic/deepsparse/blob/main/docs/llms/text-generation-pipeline.md):
```python
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.
```bash
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:
```python
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
```bash
sparseml.export --task text-generation output_finetune/
```
Follow the instructions on our [One Shot With SparseML](https://github.com/neuralmagic/sparseml/tree/main/src/sparseml/transformers/sparsification/obcq) 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](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ) |