
# mpt-7b-instruct: sharded


This is a version of the [mpt-7b-instruct](https://huggingface.co/mosaicml/mpt-7b-instruct) model, sharded to 2 GB chunks for low-RAM loading (i.e. Colab).
The weights are stored in `bfloat16` so in theory you can run this on CPU, though it may take forever.
Original code and credits go to [mpt-7b-storywriter-sharded](https://huggingface.co/ethzanalytics/mpt-7b-storywriter-sharded).
See the [community discussion](https://huggingface.co/ethzanalytics/mpt-7b-storywriter-sharded/discussions/2) on how to replicate this.

Please refer to the previously linked repo for details on usage/implementation/etc. This model was downloaded from the original repo under Apache-2.0 and is redistributed under the same license.


## Basic Usage

> Note when using: this is **not** an instruction-tuned model, so you need to give it sufficient input text to continue generating something on-topic with your prompt
> 
Install/upgrade packages:

```bash
pip install -U torch transformers accelerate einops
```

Load the model:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'jprafael/mpt-7b-instruct-sharded'
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    revision='8d8911ad980f48f8a791e5f5876dea891dcbc064', # optional, but a good idea
    device_map='auto',
    load_in_8bit=False, # install bitsandbytes then set to true for 8-bit
)
model = torch.compile(model)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

Then you can use `model.generate()` as you would normally - see the notebook for details.


---
{"language": ["en"], "license": "apache-2.0", "tags": ["mosaicML", "sharded", "instruct"], "datasets": ["the_pile_books3"], "pipeline_tag": "text-generation", "inference": false}
transformers pytorch mpt text-generation mosaicML sharded instruct custom_code en dataset:the_pile_books3 license:apache-2.0 autotrain_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mpt #text-generation #mosaicML #sharded #instruct #custom_code #en #dataset-the_pile_books3 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 


# mpt-7b-instruct: sharded


This is a version of the mpt-7b-instruct model, sharded to 2 GB chunks for low-RAM loading (i.e. Colab).
The weights are stored in 'bfloat16' so in theory you can run this on CPU, though it may take forever.
Original code and credits go to mpt-7b-storywriter-sharded.
See the community discussion on how to replicate this.

Please refer to the previously linked repo for details on usage/implementation/etc. This model was downloaded from the original repo under Apache-2.0 and is redistributed under the same license.


## Basic Usage

> Note when using: this is not an instruction-tuned model, so you need to give it sufficient input text to continue generating something on-topic with your prompt
> 
Install/upgrade packages:



Load the model:



Then you can use 'model.generate()' as you would normally - see the notebook for details.


---
# mpt-7b-instruct: sharded


This is a version of the mpt-7b-instruct model, sharded to 2 GB chunks for low-RAM loading (i.e. Colab).
The weights are stored in 'bfloat16' so in theory you can run this on CPU, though it may take forever.
Original code and credits go to mpt-7b-storywriter-sharded.
See the community discussion on how to replicate this.

Please refer to the previously linked repo for details on usage/implementation/etc. This model was downloaded from the original repo under Apache-2.0 and is redistributed under the same license. ## Basic Usage

> Note when using: this is not an instruction-tuned model, so you need to give it sufficient input text to continue generating something on-topic with your prompt
> 
Install/upgrade packages:



Load the model:



Then you can use 'model.generate()' as you would normally - see the notebook for details.


---
TAGS
#transformers #pytorch #mpt #text-generation #mosaicML #sharded #instruct #custom_code #en #dataset-the_pile_books3 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 
 # mpt-7b-instruct: sharded


This is a version of the mpt-7b-instruct model, sharded to 2 GB chunks for low-RAM loading (i.e. Colab).
The weights are stored in 'bfloat16' so in theory you can run this on CPU, though it may take forever.
Original code and credits go to mpt-7b-storywriter-sharded.
See the community discussion on how to replicate this.

Please refer to the previously linked repo for details on usage/implementation/etc. This model was downloaded from the original repo under Apache-2.0 and is redistributed under the same license. ## Basic Usage

> Note when using: this is not an instruction-tuned model, so you need to give it sufficient input text to continue generating something on-topic with your prompt
> 
Install/upgrade packages:



Load the model:



Then you can use 'model.generate()' as you would normally - see the notebook for details.


---