readme: ---
  tags:
  - text-generation
  - 8bit
  - 8-bit
  - quantization
  - compression
  inference: False
  license: apache-2.0
  ---
  
  # ethzanalytics/gpt-j-6B-8bit-sharded
  
  This is a version of `hivemind/gpt-j-6B-8bit` for low-RAM loading, i.e., free Colab runtimes :)
  
  - shards are <= 1000MB each
  - a demo notebook of how to use it [is here](https://colab.research.google.com/gist/pszemraj/1c0b32173df5b1efbdb7a2358ed4195b/generate-text-with-an-llm-sharded-on-huggingface.ipynb)
  
  
   [![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/pszemraj/1c0b32173df5b1efbdb7a2358ed4195b/generate-text-with-an-llm-sharded-on-huggingface.ipynb)
   
   
  **Please refer to the [original model card for hivemind/gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit) for all details.**
  
  ## Usage
  
  
  > **NOTE:** PRIOR to loading the model, you need to "patch" it to be compatible with loading 8bit weights etc. See the original model card above for details on how to do this.
  
  install `transformers`, `accelerate`, and `bitsandbytes` if needed:
  ```sh
  pip install transformers accelerate bitsandbytes
  ```
  Patch the model, load using `device_map="auto"`:
  
  ```python
  import transformers 
  from transformers import AutoTokenizer
  
  """
  CODE TO PATCH GPTJForCausalLM GOES HERE
  """
  
  tokenizer = AutoTokenizer.from_pretrained("ethzanalytics/gpt-j-6B-8bit-sharded")
  
  model = GPTJForCausalLM.from_pretrained(
      "ethzanalytics/gpt-j-6B-8bit-sharded",
      device_map="auto",
  )
  ```
  
  Take a look at the notebook for details.
  