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---
base_model: teknium/OpenHermes-2.5-Mistral-7B
inference: false
model_type: mistral
prompt_template: |
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
sparsified_by: mgoin
tags:
- deepsparse
---
# OpenHermes 2.5 Mistral 7B - DeepSparse
This repo contains [DeepSparse](https://github.com/neuralmagic/deepsparse) model files for [Teknium's OpenHermes 2.5 Mistral 7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B).
This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
## Inference
Install DeepSparse: `pip install deepsparse-nightly[llm]`
```python
from deepsparse import TextGeneration
system_message = ""
prompt = "Write a quick sort algorithm in Python"
formatted_prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
model = TextGeneration(model="hf:mgoin/Nous-Hermes-llama-2-7b-pruned50-quant-ds")
print(model(formatted_prompt, max_new_tokens=500).generations[0].text)
```
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Sparsification
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 teknium/OpenHermes-2.5-Mistral-7B 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
```
```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}")
```
## Slack
For further support, and discussions on these models and AI in general, join us at:
[Neural Magic's Slack server](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ) |