
### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights


This is a version of [EleutherAI's GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-2.7B) with 2.7 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**. Inspired by [GPT-J 8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit). 

Here's how to run it: [![colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/1lMja-CPc0vm5_-gXNXAWU-9c0nom7vZ9)

## Model Description

GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.


## Links

* [EleutherAI](https://www.eleuther.ai)
* [Hivemind](https://training-transformers-together.github.io/)
* [Gustave Cortal](https://twitter.com/gustavecortal)
{"language": "en", "license": "mit", "tags": ["causal-lm"], "datasets": ["The_Pile"]}
transformers pytorch gpt_neo text-generation causal-lm en dataset:The_Pile license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #dataset-The_Pile #license-mit #autotrain_compatible #endpoints_compatible #region-us 


### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights


This is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. 

Here's how to run it: ![colab](URL

## Model Description

GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.


## Links

* EleutherAI
* Hivemind
* Gustave Cortal
### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights


This is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. 

Here's how to run it: ![colab](URL ## Model Description

GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model. ## Links

* EleutherAI
* Hivemind
* Gustave Cortal
TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #dataset-The_Pile #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights


This is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. 

Here's how to run it: ![colab](URL ## Model Description

GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model. ## Links

* EleutherAI
* Hivemind
* Gustave Cortal
passage: TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #dataset-The_Pile #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights


This is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. 

Here's how to run it: ![colab](URL## Model Description

GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.## Links

* EleutherAI
* Hivemind
* Gustave Cortal