GPT-2-dolly

GPT-2-dolly is an instruction fine-tuned model based on the GPT-2 transformer architecture.

Benchmark Metrics

Metric	GPT-2-dolly	GPT-2 (base)
Avg.	30.91	29.99
ARC (25-shot)	22.70	21.84
HellaSwag (10-shot)	30.15	31.6
MMLU (5-shot)	25.81	25.86
TruthfulQA (0-shot)	44.97	40.67

We use state-of-the-art Language Model Evaluation Harness to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.

Model Details

Trained by: Luiz G A Alves
Model type: GPT-2-dolly is an auto-regressive language model based on the GPT-2 transformer architecture.
Language(s): English

How to use:

# Use a pipeline as a high-level helper
>>> from transformers import pipeline
>>> pipe = pipeline("text-generation", model="lgaalves/gpt2-dolly")
>>> question = "What is a large language model?"
>>> answer = pipe(question)
>>> print(answer[0]['generated_text'])

or, you can load the model direclty using:

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2-dolly")
model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2-dolly")

Training Dataset

lgaalves/gpt2-dolly trained using the Databricks Dolly dataset databricks/databricks-dolly-15k.

Training Procedure

lgaalves/gpt2-dolly was instruction fine-tuned using LoRA on 1 T4 GPU on Google Colab. It took about 1.5 hours to train it.

Intended uses, limitations & biases

You can use the raw model for text generation or fine-tune it to a downstream task. The model was not extensively tested and may produce false information. It contains a lot of unfiltered content from the internet, which is far from neutral.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	25.53
ARC (25-shot)	22.7
HellaSwag (10-shot)	30.15
MMLU (5-shot)	25.81
TruthfulQA (0-shot)	44.97
Winogrande (5-shot)	51.46
GSM8K (5-shot)	0.15
DROP (3-shot)	3.45

lgaalves
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gpt2-dolly