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---
base_model: glaiveai/glaive-coder-7b
inference: false
model_type: llama
prompt_template: |
<s>[INST]
{prompt}
[/INST]
quantized_by: mwitiderrick
tags:
- deepsparse
---
# Glaive-coder-7b - DeepSparse
This repo contains model files for [Glaive-coder-7b](https://huggingface.co/glaiveai/glaive-coder-7b) 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
template = "<s>[INST] {prompt} [/INST]"
prompt = "Write a quick sort algorithm in Python"
input_str = template.format(prompt=prompt)
model = TextGeneration(model_path="hf:nm-testing/glaive-coder-7b-pruned50-quant-ds")
print(model(input_str, max_new_tokens=200).generations[0].text)
"""
"""
```
## Prompt template
```
<s>[INST]
{prompt}
[/INST]
```
## 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 glaiveai/glaive-coder-7b open_platypus --recipe recipe.yaml --save True
python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --sequence_length 4096 --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) |