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--- |
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license: apache-2.0 |
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datasets: |
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- aisquared/databricks-dolly-15k |
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language: |
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- en |
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library_name: transformers |
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--- |
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# Model Card for `dlite-v2-774m` |
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<!-- Provide a quick summary of what the model is/does. --> |
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AI Squared's `dlite-v2-774m` is a large language |
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model which is derived from OpenAI's large [GPT-2](https://huggingface.co/gpt2-large) model and fine-tuned on a corpus of 15k records |
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([Databricks' "Dolly 15k" Dataset](https://huggingface.co/datasets/aisquared/databricks-dolly-15k)) to help it exhibit chat-based capabilities. |
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Just like [Databricks' Dolly V2 models](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm), |
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`dlite-v2-774m` (and all other members of the `dlite-v2` family) is licensed for both **research and commercial use.** We are extremely grateful |
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for the work that Databricks has done to create the `databricks-dolly-15k` dataset, for without it we would not be able to create and release this |
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model under such an open and permissive license. |
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While `dlite-v2-774m` 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 |
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is important to showcase, as it continues to demonstrate that creating powerful AI capabilities may be much more accessible than previously thought. |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** AI Squared, Inc. |
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- **Shared by:** AI Squared, Inc. |
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- **Model type:** Large Language Model |
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- **Language(s) (NLP):** EN |
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- **License:** Apache v2.0 |
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- **Finetuned from model:** GPT-2 |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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**`dlite-v2-774m` is not a state-of-the-art language model.** `dlite-v2-774m` is an experimental technology, and as with any experimental technology, |
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AI Squared urges potential users of this technology to test its capabilities thoroughly before usage. |
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Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include, |
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but are not limited to: factual inaccuracies, biases, offensive responses, toxicity, and hallucinations. |
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Just as with any other LLM, we advise users of this technology to exercise good judgment when applying this technology. |
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## Usage |
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To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. |
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From your terminal, run: |
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```python |
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pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" |
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``` |
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The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` |
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found in the model repo [here](https://huggingface.co/aisquared/dlite-v2-774m/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. |
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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. |
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It is also fine to remove it if there is sufficient memory. |
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```python |
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from transformers import pipeline |
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import torch |
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generate_text = pipeline(model="aisquared/dlite-v2-774m", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") |
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``` |
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You can then use the pipeline to answer instructions: |
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```python |
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res = generate_text("Who was George Washington?") |
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print(res) |
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``` |
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Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/aisquared/dlite-v2-774m/blob/main/instruct_pipeline.py), |
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store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: |
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```python |
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from instruct_pipeline import InstructionTextGenerationPipeline |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("aisquared/dlite-v2-774m", padding_side="left") |
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model = AutoModelForCausalLM.from_pretrained("aisquared/dlite-v2-774m", device_map="auto", torch_dtype=torch.bfloat16) |
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generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) |
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``` |
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### Model Performance Metrics |
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We present the results from various model benchmarks on the EleutherAI LLM Evaluation Harness for all models in the DLite family. |
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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 |
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state of the art, but rather further show that chat-like behaviors in LLMs can be trained almost independent of model size. |
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| model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | |
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|:--------------|-------------:|-----------:|-------------:|------------:|----------------:|---------:|---------:| |
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| gpt2 | 0.164 | 0.438131 | 0.51618 | 0.289185 | 0.190273 | 0.628945 | 0.487156 | |
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| dlite-v2-124m | 0.174 | 0.44697 | 0.502762 | 0.291974 | 0.192833 | 0.631665 | 0.520183 | |
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| dlite-v1-124m | 0.17 | 0.462542 | 0.494081 | 0.293268 | 0.223549 | 0.622416 | 0.502446 | |
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| gpt2-medium | 0.186 | 0.490741 | 0.531176 | 0.333101 | 0.215017 | 0.676279 | 0.585933 | |
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| dlite-v2-355m | 0.206 | 0.493687 | 0.524073 | 0.334993 | 0.226109 | 0.670838 | 0.582263 | |
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| dlite-v1-355m | 0.216 | 0.507576 | 0.496448 | 0.338478 | 0.234642 | 0.664309 | 0.600306 | |
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| gpt2-large | 0.194 | 0.531566 | 0.553275 | 0.363971 | 0.216724 | 0.703482 | 0.604893 | |
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| dlite-774m-v2 | 0.212 | 0.539562 | 0.5588 | 0.365565 | 0.234642 | 0.700218 | 0.60367 | |
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| dlite-774m-v1 | 0.218 | 0.545875 | 0.562747 | 0.375124 | 0.250853 | 0.698041 | 0.614985 | |
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| gpt2-xl | 0.224 | 0.582912 | 0.583268 | 0.400418 | 0.25 | 0.708379 | 0.617737 | |
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| dlite-v1-1.5b | 0.226 | 0.588384 | 0.584846 | 0.401414 | 0.268771 | 0.708379 | 0.624159 | |
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| dlite-v2-1.5b | 0.226 | 0.59596 | 0.581689 | 0.40719 | 0.273891 | 0.705114 | 0.630887 | |
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