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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 | arc_challenge | arc_easy | boolq | hellaswag | openbookqa | piqa | winogrande | |
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|:--------------|----------------:|-----------:|---------:|------------:|-------------:|---------:|-------------:| |
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| dlite-v2-124m | 0.199659 | 0.447811 | 0.494801 | 0.291675 | 0.156 | 0.620239 | 0.487766 | |
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| gpt2 | 0.190273 | 0.438131 | 0.487156 | 0.289185 | 0.164 | 0.628945 | 0.51618 | |
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| dlite-v1-124m | 0.223549 | 0.462542 | 0.502446 | 0.293268 | 0.17 | 0.622416 | 0.494081 | |
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| gpt2-medium | 0.215017 | 0.490741 | 0.585933 | 0.333101 | 0.186 | 0.676279 | 0.531176 | |
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| dlite-v2-355m | 0.251706 | 0.486111 | 0.547401 | 0.344354 | 0.216 | 0.671926 | 0.52723 | |
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| dlite-v1-355m | 0.234642 | 0.507576 | 0.600306 | 0.338478 | 0.216 | 0.664309 | 0.496448 | |
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| gpt2-large | 0.216724 | 0.531566 | 0.604893 | 0.363971 | 0.194 | 0.703482 | 0.553275 | |
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| dlite-v1-774m | 0.250853 | 0.545875 | 0.614985 | 0.375124 | 0.218 | 0.698041 | 0.562747 | |
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| dlite-v2-774m | 0.269625 | 0.52904 | 0.613761 | 0.395937 | 0.256 | 0.691513 | 0.566693 | |
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| gpt2-xl | 0.25 | 0.582912 | 0.617737 | 0.400418 | 0.224 | 0.708379 | 0.583268 | |
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| dlite-v1-1_5b | 0.268771 | 0.588384 | 0.624159 | 0.401414 | 0.226 | 0.708379 | 0.584846 | |
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| dlite-v2-1_5b | 0.289249 | 0.565657 | 0.601223 | 0.434077 | 0.272 | 0.703482 | 0.588003 | |
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### Limitations |
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*DLite is an experimental technology and is not designed for use in any environment without significant testing and safety consideration. |
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Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include, but are not limited to: factual |
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inaccuracies, biases, offensive responses, toxicity, and hallucinations. Just as with any other LLM, we advise users of this technology |
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to exercise good judgment when applying this technology.* |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aisquared__dlite-v2-774m) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 29.01 | |
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| ARC (25-shot) | 30.12 | |
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| HellaSwag (10-shot) | 47.68 | |
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| MMLU (5-shot) | 25.37 | |
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| TruthfulQA (0-shot) | 40.0 | |
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| Winogrande (5-shot) | 53.99 | |
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| GSM8K (5-shot) | 0.0 | |
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| DROP (3-shot) | 5.93 | |
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