Roneneldan

Rank:

Average Model Cost: $0.0000

Number of Runs: 15,467

Models by this creator

TinyStories-1M

TinyStories-1M

roneneldan

Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759 ------ EXAMPLE USAGE --- from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-1M') tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M") prompt = "Once upon a time there was" input_ids = tokenizer.encode(prompt, return_tensors="pt") Generate completion output = model.generate(input_ids, max_length = 1000, num_beams=1) Decode the completion output_text = tokenizer.decode(output[0], skip_special_tokens=True) Print the generated text print(output_text)

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$-/run

5.7K

Huggingface

TinyStories-33M

TinyStories-33M

Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759 Based on GPT-Neo architecture. License: mit ------ EXAMPLE USAGE --- from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-33M') tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M") prompt = "Once upon a time there was" input_ids = tokenizer.encode(prompt, return_tensors="pt") Generate completion output = model.generate(input_ids, max_length = 1000, num_beams=1) Decode the completion output_text = tokenizer.decode(output[0], skip_special_tokens=True) Print the generated text print(output_text)

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$-/run

4.1K

Huggingface

TinyStories-3M

TinyStories-3M

Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759 ------ EXAMPLE USAGE --- from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-3M') tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M") prompt = "Once upon a time there was" input_ids = tokenizer.encode(prompt, return_tensors="pt") Generate completion output = model.generate(input_ids, max_length = 1000, num_beams=1) Decode the completion output_text = tokenizer.decode(output[0], skip_special_tokens=True) Print the generated text print(output_text)

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$-/run

614

Huggingface

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