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---
base_model: mediocredev/open-llama-3b-v2-instruct
inference: false
model_type: llama
prompt_template: |
### User:\n
{prompt}
### Assistant:\n
quantized_by: mwitiderrick
tags:
- deepsparse
---
## Open-llama-3b-v2-instruct - DeepSparse
This repo contains model files for [Open-llama-3b-v2-instruct](https://huggingface.co/mediocredev/open-llama-3b-v2-instruct) 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
system_message = 'You are a helpful assistant, who always provide explanation.'
user_message = 'How many days are there in a leap year?'
formatted_prompt = f'### System:\n{system_message}<|endoftext|>\n### User:\n{user_message}<|endoftext|>\n### Assistant:\n'
model = TextGeneration(model_path="hf:nm-testing/open-llama-3b-v2-instruct-pruned50-quant-ds")
print(model(formatted_prompt, max_new_tokens=200,repetition_penalty=1.05).generations[0].text)
"""
- 2-1-1-1-1-re-1-1-2-1.
#!-1.
- 2. 1.
- 2. 12. 1! - 1. #. 12 're-2. 2. 1-2-12-2-1-1-1-2-0. #1. #. 1. #1-1-0. 're-1-1-2-2-1-0. 1-1-0-1-1-2. 1. #1-1-1-1-0. #1-2-1-2-1-1-1-1-1-2-2-1-1-1-1-1-1-1-2-1-1-1-1-1-1-1-1-1-1-
"""
```
## Prompt template
```
### User:\n
{prompt}
### 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 mediocredev/open-llama-3b-v2-instruct open_platypus --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:
```python
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](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) |