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erwanf/
gpt2-mini

Name: gpt2-mini
Availability: InStock

Skylion007/openwebtext

mit

autotrain_compatible

text-generation-inference

endpoints_compatible

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GPT-2 Mini

A smaller GPT-2 model with (only) 39M parameters. It was pretrained on a subset of OpenWebText, the open-source version of the pretraining dataset used by OpenAI for the original GPT-2 models.

Uses

The purpose of this model is mainly for research and education. Its small size allows for fast experiments in resource-limited settings, while still being able of generating complex and coherent text.

Getting Started

Use the code below to get started with the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model = AutoModelForCausalLM.from_pretrained("erwanf/gpt2-mini")
model.eval()

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("erwanf/gpt2-mini")

# Generate text
prompt = "Hello, I'm a language model,"
input_ids = tokenizer.encode(prompt, return_tensors="pt")

output = model.generate(input_ids, do_sample=True, max_length=50, num_return_sequences=5)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
print(output_text)

Output:

["Hello, I'm a language model, I can't be more efficient in words.\n\nYou can use this as a point to find out the next bit in your system, and learn more about me.\n\nI think a lot of the",
 "Hello, I'm a language model, my teacher is a good teacher - a good school teacher – and one thing you have to remember:\n\nIt's not perfect. A school is not perfect; it isn't perfect at all!\n\n",
 'Hello, I\'m a language model, but if I can do something for you then go for it (for a word). Here is my blog, the language:\n\nI\'ve not used "normal" in English words, but I\'ve always',
 'Hello, I\'m a language model, I\'m talking to you the very first time I used a dictionary and it can be much better than one word in my dictionary. What would an "abnormal" English dictionary have to do with a dictionary and',
 'Hello, I\'m a language model, the most powerful representation of words and phrases in the language I\'m using."\n\nThe new rules change that makes it much harder for people to understand a language that does not have a native grammar (even with']

Training Details

The architecture relies on the GPT-2 model, with smaller dimensions and less layers. It uses the same tokenizer as GPT-2. We used the first 2M rows from the OpenWebText dataset, out of which we use 1k for test and validation sets.

Hyperparameters

Hyperparameter	Value
Model Parameters
Vocabulary Size	50,257
Context Length	512
Number of Layers	4
Hidden Size	512
Number of Attention Heads	8
Intermediate Size	2048
Activation Function	GELU
Dropout	No
Training Parameters
Learning Rate	5e-4
Batch Size	256
Optimizer	AdamW
beta1	0.9
beta2	0.98
Weight Decay	0.1
Training Steps	100,000
Warmup Steps	4,000
Learning Rate Scheduler	Cosine
Training Dataset Size	1M samples
Validation Dataset Size	1k samples
Float Type	bf16