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---
license: mit
datasets:
- databricks/databricks-dolly-15k
language:
- en
pipeline_tag: text-generation
tags:
- dolly
- dolly-v2
- instruct
- sharded
- quantized
inference: False
---
# dolly-v2-7b: **8-bit** sharded checkpoint
<a href="https://colab.research.google.com/gist/pszemraj/8100e98caab538be32832d1208e93f65/dolly-v2-7b-8bit-inference.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This is a sharded checkpoint (with ~2GB shards) of the `databricks/dolly-v2-7b` model **in 8-bit precision using `bitsandbytes`**.
Refer to the [original model](https://huggingface.co/databricks/dolly-v2-7b) for all details. For more info on loading 8bit models, refer to the [example repo](https://huggingface.co/ybelkada/bloom-1b7-8bit) and/or the `4.28.0` [release info](https://github.com/huggingface/transformers/releases/tag/v4.28.0).
- total model size is only ~7.5 GB!
- this enables low-RAM loading, i.e. Colab :)
## Basic Usage
install/upgrade `transformers`, `accelerate`, and `bitsandbytes`. For this to work **you must have** `transformers>=4.28.0` and `bitsandbytes>0.37.2`.
```bash
pip install -U -q transformers bitsandbytes accelerate
```
Load the model. As it is serialized in 8bit you don't need to do anything special:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "ethzanalytics/dolly-v2-7b-sharded-8bit"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
``` |