license: apache-2.0
datasets:
- tatsu-lab/alpaca
language:
- en
library_name: transformers
Model Card for dlite-v1-124m
AI Squared's dlite-v1-124m (blog post) is a large language
model which is derived from OpenAI's smallest GPT-2 model and fine-tuned on a single T4 GPU on a corpus of 50k records
(Stanford Alpaca) to help it exhibit chat-based capabilities.
While dlite-v1-124m 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.
Model Description
- Developed by: AI Squared, Inc.
- Shared by: AI Squared, Inc.
- Model type: Large Language Model
- Language(s) (NLP): EN
- License: Apache v2.0
- Finetuned from model: GPT-2
Bias, Risks, and Limitations
dlite-v1-124m is not a state-of-the-art language model. dlite-v1-124m 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.
Usage
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:
pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"
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.
from transformers import pipeline
import torch
generate_text = pipeline(model="aisquared/dlite-v1-124m", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
You can then use the pipeline to answer instructions:
res = generate_text("Who was George Washington?")
print(res)
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:
from instruct_pipeline import InstructionTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("aisquared/dlite-v1-124m", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("aisquared/dlite-v1-124m", device_map="auto", torch_dtype=torch.bfloat16)
generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
Model Performance Metrics
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.
| Model | arc_challenge | arc_easy | boolq | hellaswag | openbookqa | piqa | winogrande |
|---|---|---|---|---|---|---|---|
| dlite-v2-124m | 0.199659 | 0.447811 | 0.494801 | 0.291675 | 0.156 | 0.620239 | 0.487766 |
| gpt2 | 0.190273 | 0.438131 | 0.487156 | 0.289185 | 0.164 | 0.628945 | 0.51618 |
| dlite-v1-124m | 0.223549 | 0.462542 | 0.502446 | 0.293268 | 0.17 | 0.622416 | 0.494081 |
| gpt2-medium | 0.215017 | 0.490741 | 0.585933 | 0.333101 | 0.186 | 0.676279 | 0.531176 |
| dlite-v2-355m | 0.251706 | 0.486111 | 0.547401 | 0.344354 | 0.216 | 0.671926 | 0.52723 |
| dlite-v1-355m | 0.234642 | 0.507576 | 0.600306 | 0.338478 | 0.216 | 0.664309 | 0.496448 |
| gpt2-large | 0.216724 | 0.531566 | 0.604893 | 0.363971 | 0.194 | 0.703482 | 0.553275 |
| dlite-v1-774m | 0.250853 | 0.545875 | 0.614985 | 0.375124 | 0.218 | 0.698041 | 0.562747 |
| dlite-v2-774m | 0.269625 | 0.52904 | 0.613761 | 0.395937 | 0.256 | 0.691513 | 0.566693 |
| gpt2-xl | 0.25 | 0.582912 | 0.617737 | 0.400418 | 0.224 | 0.708379 | 0.583268 |
| dlite-v1-1_5b | 0.268771 | 0.588384 | 0.624159 | 0.401414 | 0.226 | 0.708379 | 0.584846 |
| dlite-v2-1_5b | 0.289249 | 0.565657 | 0.601223 | 0.434077 | 0.272 | 0.703482 | 0.588003 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 24.62 |
| ARC (25-shot) | 24.32 |
| HellaSwag (10-shot) | 31.16 |
| MMLU (5-shot) | 25.08 |
| TruthfulQA (0-shot) | 36.38 |
| Winogrande (5-shot) | 50.2 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 5.2 |