Trl-internal-testing
Rank:Average Model Cost: $0.0000
Number of Runs: 286,394
Models by this creator
tiny-random-GPTNeoXForCausalLM
tiny-random-GPTNeoXForCausalLM
The tiny-random-GPTNeoXForCausalLM model is a text generation model that is part of the GPT-NeoX family of models. It is a smaller version of the GPT-NeoX model and is trained to generate text based on the given inputs. The model uses a causal language modeling approach, where it predicts the next word in a sequence based on the previous words. It is designed to be lightweight and efficient while still providing reasonable text generation capabilities.
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58.7K
Huggingface
dummy-GPT2-correct-vocab
dummy-GPT2-correct-vocab
The dummy-GPT2-correct-vocab model is a text generation model that uses the GPT-2 architecture. It is trained to generate coherent and contextually relevant text based on the given input. This model uses a modified vocabulary to handle potential errors in the text generation process. It can be used for a variety of natural language processing tasks, including language translation, text summarization, and chatbot development.
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34.4K
Huggingface
tiny-random-GPTNeoForCausalLM
tiny-random-GPTNeoForCausalLM
tiny-random-GPTNeoForCausalLM is a language model based on GPT-Neo that generates text in a causal manner. The model has been trained on diverse text sources and can be used for various natural language processing tasks such as text completion, summarization, and dialogue generation.
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29.5K
Huggingface
tiny-random-SwitchTransformersForConditionalGeneration
tiny-random-SwitchTransformersForConditionalGeneration
No description available.
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27.2K
Huggingface
tiny-random-GPT2LMHeadModel
tiny-random-GPT2LMHeadModel
The tiny-random-GPT2LMHeadModel is a language model that is based on the GPT-2 architecture. It is a smaller version of the original GPT-2 model, and it has been trained to generate coherent and contextually relevant text. The model takes a sequence of text as input and predicts the next word or words in the sequence. It can be fine-tuned for specific tasks such as text completion, question answering, or language generation. The model's small size makes it well-suited for applications with limited computational resources.
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24.7K
Huggingface
tiny-random-CodeGenForCausalLM
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22.4K
Huggingface
tiny-random-GPTJForCausalLM
tiny-random-GPTJForCausalLM
The tiny-random-GPTJForCausalLM model is a language model that can generate text based on a given prompt in a causal manner, meaning it predicts the next word in a sequence based on the previous words. The model is designed to be small and efficient while still being able to generate coherent and meaningful text. It can be used for various natural language processing tasks such as text generation, completion, and summarization.
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22.4K
Huggingface
tiny-random-BloomForCausalLM
tiny-random-BloomForCausalLM
The tiny-random-BloomForCausalLM model is a text generation model trained on a large corpus of text data. It is designed to generate coherent and contextually relevant text based on a given prompt or input. The model utilizes pre-trained language models and applies techniques from causal language modeling to generate text predictions. It is particularly useful for tasks such as text generation, language translation, and language understanding.
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22.3K
Huggingface
tiny-random-OPTForCausalLM
tiny-random-OPTForCausalLM
The tiny-random-OPTForCausalLM model is a text generation model that is based on the OpenAI Optimized Transformer (OPT) architecture. It is designed for causal language modeling tasks and is capable of generating coherent and contextually appropriate text. This model is trained using random techniques to introduce variation in the generated text, which can lead to more diverse and creative output.
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22.3K
Huggingface
tiny-random-CodeGenForCausalLM-sharded
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22.3K
Huggingface