---
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)
```