AI Squared's chopt-research-1_3b is a large language model which is derived from Meta AI's Open Pre-trained Transformer language modelsand fine-tuned on a single GPU on a corpus of 50k records (Stanford Alpaca) to help it exhibit chat-based capabilities.

The ChOPT family of models from AI Squared are licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.

While chopt-research-1_3b is not a state-of-the-art model, we believe that the level of interactivity that can be achieved on such a small model that is trained so cheaply is important to showcase, as it continues to demonstrate that creating powerful AI capabilities may be much more accessible than previously thought.

chopt-research-1_3b is not a state-of-the-art language model. chopt-research-1_3b is an experimental technology and is not designed for use in any environment other than for research purposes. Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include, but are not limited to: factual inaccuracies, biases, offensive responses, toxicity, and hallucinations. Just as with any other LLM, we advise users of this technology to exercise good judgment when applying this technology.

To use the model with the transformers library on a machine with GPUs, first make sure you have the transformers and accelerate libraries installed. From your terminal, run:

The instruction following pipeline can be loaded using the pipeline function as shown below. This loads a custom InstructionTextGenerationPipeline found in the model repo here, which is why trust_remote_code=True is required. Including torch_dtype=torch.bfloat16 is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory.

You can then use the pipeline to answer instructions:

Alternatively, if you prefer to not use trust_remote_code=True you can download instruct_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:

We present the results from various model benchmarks on the EleutherAI LLM Evaluation Harness for all models in the DLite family. Model results are sorted by mean score, ascending, to provide an ordering. These metrics serve to further show that none of the DLite models are state of the art, but rather further show that chat-like behaviors in LLMs can be trained almost independent of model size.