Eleutherai
Models by this creator
🖼️
gpt-j-6b
1.4K
The gpt-j-6b is a large language model trained by EleutherAI, a research group dedicated to developing open-source AI systems. The model has 6 billion trainable parameters and uses the same tokenizer as GPT-2 and GPT-3, with a vocabulary size of 50,257. It utilizes Rotary Position Embedding (RoPE) for positional encoding. Similar models include GPT-2B-001 and ChatGLM2-6B, which are also large transformer models trained for language generation tasks. However, the gpt-j-6b model differs in its specific architecture, training data, and intended use cases. Model inputs and outputs Inputs The model takes in text prompts as input, which can be of varying length up to the model's context window of 2048 tokens. Outputs The model generates human-like text continuation based on the provided prompt. The output can be of arbitrary length, though it is typically used to generate short- to medium-length responses. Capabilities The gpt-j-6b model is adept at generating coherent and contextually relevant text continuations. It can be used for a variety of language generation tasks, such as creative writing, dialogue generation, and content summarization. However, the model has not been fine-tuned for specific downstream applications like chatbots or commercial use cases. What can I use it for? The gpt-j-6b model is well-suited for research and experimentation purposes, as it provides a powerful language generation capability that can be further fine-tuned or incorporated into larger AI systems. Potential use cases include: Prototyping conversational AI agents Generating creative writing prompts and story continuations Summarizing long-form text Augmenting existing language models with additional capabilities However, the model should not be deployed for human-facing applications without appropriate supervision, as it may generate harmful or offensive content. Things to try One interesting aspect of the gpt-j-6b model is its ability to generate long-form text continuations. Researchers could experiment with prompting the model to write multi-paragraph essays or short stories, and analyze the coherence and creativity of the generated output. Additionally, the model could be fine-tuned on specific datasets or tasks to explore its potential for specialized language generation applications.
Updated 5/10/2024
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gpt-neox-20b
498
gpt-neox-20b is a 20 billion parameter autoregressive language model developed by EleutherAI. Its architecture is similar to that of GPT-J-6B, with the key difference being a larger model size. Like GPT-J-6B, gpt-neox-20b was trained on a diverse corpus of English-language text using the GPT-NeoX library. Model inputs and outputs gpt-neox-20b is a general-purpose language model that can be used for a variety of text-to-text tasks. The model takes in a sequence of text as input and generates a continuation of that text as output. Inputs Text prompt**: A sequence of text that the model will use to generate additional text. Outputs Generated text**: The model's attempt at continuing or completing the input text prompt. Capabilities gpt-neox-20b is capable of generating coherent and contextually relevant text across a wide range of domains, from creative writing to question answering. The model's large size and broad training data allow it to capture complex linguistic patterns and generate fluent, human-like text. What can I use it for? The gpt-neox-20b model can be used as a foundation for a variety of natural language processing tasks and applications. Researchers may find it useful for probing the capabilities and limitations of large language models, while practitioners may choose to fine-tune the model for specific use cases such as chatbots, content generation, or knowledge extraction. Things to try One interesting aspect of gpt-neox-20b is its ability to handle long-range dependencies and generate coherent text over extended sequences. Experimenting with prompts that require the model to maintain context and logical consistency over many tokens can be a good way to explore the model's strengths and weaknesses.
Updated 5/10/2024
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gpt-neo-2.7B
389
gpt-neo-2.7B is a transformer language model developed by EleutherAI. It is a replication of the GPT-3 architecture with 2.7 billion parameters. The model was trained on the Pile, a large-scale curated dataset created by EleutherAI, using a masked autoregressive language modeling approach. Similar models include the GPT-NeoX-20B and GPT-J-6B models, also developed by EleutherAI. These models use the same underlying architecture but have different parameter counts and training datasets. Model Inputs and Outputs gpt-neo-2.7B is a language model that can be used for text generation. The model takes a string of text as input and generates the next token in the sequence. This allows the model to continue a given prompt and generate coherent text. Inputs A string of text to be used as a prompt for the model. Outputs A continuation of the input text, generated by the model. Capabilities gpt-neo-2.7B excels at generating human-like text from a given prompt. It can be used to continue stories, write articles, and generate other forms of natural language. The model has also shown strong performance on downstream tasks like question answering and text summarization. What Can I Use It For? gpt-neo-2.7B can be a useful tool for a variety of natural language processing tasks, such as: Content generation**: The model can be used to generate text for blog posts, stories, scripts, and other creative writing projects. Chatbots and virtual assistants**: The model can be fine-tuned to engage in more natural, human-like conversations. Question answering**: The model can be used to answer questions based on provided context. Text summarization**: The model can be used to generate concise summaries of longer passages of text. Things to Try One interesting aspect of gpt-neo-2.7B is its flexibility in handling different prompts. Try providing the model with a wide range of inputs, from creative writing prompts to more analytical tasks, and observe how it responds. This can help you understand the model's strengths and limitations, and identify potential use cases that fit your needs.
Updated 5/10/2024
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gpt-neo-1.3B
234
GPT-Neo 1.3B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. The model was trained on the Pile, a large-scale curated dataset created by EleutherAI. GPT-Neo refers to the class of models, while 1.3B represents the number of parameters of this particular pre-trained model. Compared to similar models like GPT-Neo 2.7B and GPT-J 6B, GPT-Neo 1.3B has a smaller parameter size but still demonstrates strong performance on a variety of language tasks. The model was trained using a similar approach to GPT-3, learning an inner representation of the English language that can then be used to extract features useful for downstream applications. Model inputs and outputs GPT-Neo 1.3B is a language model that takes in a string of text as input and generates the next token in the sequence. The model can be used for a variety of text-to-text tasks, such as text generation, summarization, and question answering. Inputs A string of text, which the model will use to predict the next token Outputs A predicted token that continues the input text sequence The model can be used to generate full text passages by repeatedly applying the model to generate the next token Capabilities GPT-Neo 1.3B demonstrates strong performance on a variety of language understanding and generation tasks. On the LAMBADA task, which measures language modeling ability, the model achieves a perplexity of 7.498. It also performs well on other benchmarks like Winogrande (55.01% accuracy) and Hellaswag (38.66% accuracy). While the model was not specifically fine-tuned for downstream tasks, its general language understanding capabilities make it useful for applications like text summarization, question answering, and creative writing assistance. The model can generate fluent and contextually relevant text, though users should be mindful of potential biases or inaccuracies in the generated output. What can I use it for? GPT-Neo 1.3B can be a valuable tool for a variety of natural language processing applications. Researchers and developers may find it useful for pre-training on language tasks or as a starting point for fine-tuning on specific domains or applications. For example, the model could be fine-tuned for summarization tasks, where it generates concise summaries of longer text passages. It could also be used for question answering, where the model is prompted with a question and generates a relevant answer. In the creative writing domain, the model can assist with ideation and text generation to help writers overcome writer's block. However, as with all language models, users should be cautious about deploying GPT-Neo 1.3B in high-stakes applications without thorough testing and curation of the model outputs. The model was trained on a dataset that may contain biases or inaccuracies, so it's important to carefully evaluate the model's behavior and outputs before relying on them for critical tasks. Things to try One interesting aspect of GPT-Neo 1.3B is its strong performance on the Winogrande benchmark, which tests the model's ability to reason about complex linguistic phenomena. Developers could explore using the model for tasks that require deeper language understanding, such as commonsense reasoning or natural language inference. Another area to explore is the model's potential for open-ended text generation. By providing the model with creative prompts, users can see what kinds of imaginative and engaging text it can produce. This could be useful for applications like story writing assistance or chatbots that engage in open-ended dialogue. Ultimately, the versatility of GPT-Neo 1.3B means that there are many possibilities for experimentation and exploration. By understanding the model's strengths and limitations, developers can find innovative ways to apply it to a wide range of natural language processing tasks.
Updated 5/10/2024
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gpt-neo-125m
161
The gpt-neo-125m is a 125 million parameter transformer model developed by EleutherAI, a collective of AI researchers and engineers. It is a replication of the GPT-3 architecture, with the "GPT-Neo" referring to the class of models. This particular model was trained on the Pile, a large-scale curated dataset created by EleutherAI, for 300 billion tokens over 572,300 steps. Compared to similar models, the gpt-neo-125m is a smaller and more lightweight version of GPT-Neo 2.7B and GPT-NeoX-20B, which have 2.7 billion and 20 billion parameters respectively. These larger models demonstrate improved performance on various benchmarks compared to the 125M version. Model inputs and outputs Inputs Text prompt**: The model takes in a text prompt as input, which it uses to generate the next token in a sequence. Outputs Generated text**: The model outputs a sequence of generated text, continuing from the provided prompt. The generated text is produced in an autoregressive manner, with the model predicting the next token based on the previous tokens in the sequence. Capabilities The gpt-neo-125m model is a capable language generation model that can be used to produce human-like text from a given prompt. It has learned an internal representation of the English language that allows it to generate coherent and contextually relevant text. However, as an autoregressive model, it is best suited for tasks like text generation and may not perform as well on other NLP tasks that require more sophisticated reasoning. What can I use it for? The gpt-neo-125m model can be used for a variety of text generation tasks, such as creative writing, content generation, and chatbots. For example, you could use the model to generate product descriptions, short stories, or engaging dialog. The model's relatively small size also makes it suitable for deployment on resource-constrained devices or platforms. However, it's important to note that the model was trained on a dataset that contains potentially offensive content, so the generated text may include biases, profanity, or other undesirable content. It's recommended to carefully curate and filter the model's outputs before using them in production or releasing them to end-users. Things to try One interesting aspect of the gpt-neo-125m model is its ability to capture and generate long-range dependencies in text. Try providing the model with a long, multi-sentence prompt and see how it continues the narrative, maintaining coherence and consistency over several paragraphs. This can showcase the model's understanding of contextual information and its capacity for generating coherent, extended passages of text. Additionally, you can experiment with providing the model with prompts that require some level of reasoning or world knowledge, such as answering questions or completing tasks. While the model may not excel at these types of tasks out-of-the-box, observing its strengths and limitations can provide valuable insights into its capabilities and potential areas for improvement.
Updated 5/10/2024
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pythia-12b
127
The Pythia Scaling Suite is a collection of models developed by EleutherAI to facilitate interpretability research. It contains two sets of eight models with varying sizes, from 70M to 12B parameters, all trained on the same Pile dataset. The Pythia models were deliberately designed to promote scientific research on large language models, with a focus on interpretability rather than downstream performance. Despite this, the models have been found to match or exceed the performance of similar models of the same size, such as those in the OPT and GPT-Neo suites. Model inputs and outputs The Pythia-12B model is a transformer-based language model that takes in text as input and generates text as output. It was trained on the Pile, a large-scale curated dataset created by EleutherAI for the purpose of training language models. Inputs Text prompts of varying length Outputs Continued text sequences generated based on the input prompt Capabilities The Pythia-12B model is capable of generating human-like text in English, with the ability to perform a variety of language-related tasks such as question answering, classification, and summarization. However, the model was not designed with a focus on downstream performance, and may not excel at these tasks compared to models that were specifically fine-tuned for them. What can I use it for? The Pythia-12B model is primarily intended for research purposes, particularly in the area of interpretability. Researchers can use this model to study the inner workings of large language models and understand their limitations and biases. The model may also be useful for applications in fields such as education, creative writing, and artistic generation, though care should be taken to ensure appropriate use and mitigate potential harms. Things to try One interesting aspect of the Pythia model suite is the inclusion of both standard and deduplicated versions of the models. Researchers can explore the impact of dataset deduplication on model performance and interpretability by comparing the two versions. Additionally, the availability of 154 intermediate checkpoints per model provides opportunities to study the evolution of the models during training.
Updated 5/10/2024
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llemma_7b
84
The llemma_7b is a language model for mathematics developed by EleutherAI. It was initialized with Code Llama 7B weights and trained on the Proof-Pile-2 dataset for 200 billion tokens. This model also comes in a 34 billion parameter version called Llemma 34B. Model inputs and outputs Inputs The llemma_7b model takes in text as input. Outputs The model generates text as output, focused on mathematical reasoning and using computational tools for mathematics. Capabilities The llemma_7b model is particularly strong at chain-of-thought mathematical reasoning and using computational tools like Python and formal theorem provers. On benchmarks evaluating these capabilities, it outperforms models like Llama-2, Code Llama, and Minerva when controlling for model size. What can I use it for? The llemma_7b model could be useful for a variety of mathematics-focused applications, such as: Generating step-by-step solutions to mathematical problems Assisting with symbolic mathematics and theorem proving Providing explanations and examples for mathematical concepts Generating code to solve mathematical problems in languages like Python Things to try One interesting aspect of the llemma_7b model is its ability to leverage computational tools for mathematics. You could experiment with prompting the model to generate Python code to solve math problems or interact with formal theorem provers. Additionally, the model's strong performance on chain-of-thought reasoning makes it well-suited for open-ended mathematical problem-solving tasks.
Updated 5/10/2024
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llemma_34b
82
llemma_34b is a large language model for mathematics developed by EleutherAI. It was initialized with the Code Llama 34B weights and further trained on the Proof-Pile-2 dataset for 50B tokens. This model also comes in a 7B parameter version called Llemma 7B. Model inputs and outputs Inputs Text input for mathematical reasoning and problem-solving Outputs Textual responses containing step-by-step computational reasoning and solutions to mathematical problems Capabilities llemma_34b excels at chain-of-thought mathematical reasoning and using computational tools like Python and formal theorem provers. On a range of mathematics tasks, it outperforms models like Llama-2, Code Llama, and even the larger Minerva model when controlling for model size. What can I use it for? llemma_34b can be used for a variety of mathematical applications, such as: Solving complex math word problems Generating step-by-step solutions to mathematical proofs Assisting with the use of computational tools like Python for numerical and symbolic mathematics Enhancing math education and tutoring by providing explanations and guidance Things to try Try prompting llemma_34b with open-ended math questions or problems that require a chain of reasoning. Observe how it breaks down the problem, uses appropriate mathematical concepts and tools, and provides a detailed, step-by-step solution. Its strong performance on these types of tasks makes it a valuable tool for advanced mathematics and research.
Updated 5/10/2024
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polyglot-ko-12.8b
80
The polyglot-ko-12.8b is a large-scale Korean autoregressive language model developed by the EleutherAI polyglot team. It is part of the Polyglot-Ko series of Korean language models. The model consists of 40 transformer layers with a model dimension of 5120 and a feedforward dimension of 20480. It uses Rotary Position Embedding (RoPE) for positional encoding and has a vocabulary size of 30,003. Model inputs and outputs Inputs The model takes in raw text as input, which is then tokenized using the provided tokenizer. Outputs The model generates text auto-regressively, predicting the next token in the sequence based on the previous input. Capabilities The polyglot-ko-12.8b model is capable of generating high-quality Korean text. It can be used for a variety of natural language processing tasks such as language modeling, text generation, and potentially fine-tuned for downstream applications like question-answering or summarization. What can I use it for? The polyglot-ko-12.8b model can be used as a foundation for building various Korean language applications. For example, you could fine-tune the model on a specific domain or task to create a specialized language model for that application. The model could also be used to generate synthetic Korean text for data augmentation or to create chatbots and virtual assistants. Things to try One interesting thing to try with the polyglot-ko-12.8b model is to explore its ability to generate coherent and contextually-appropriate Korean text. You could provide the model with different prompts and observe how it continues the text, paying attention to factors like grammar, semantics, and overall fluency. Additionally, you could experiment with techniques like temperature and top-k sampling to generate more diverse and creative outputs.
Updated 5/10/2024
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polyglot-ko-1.3b
70
polyglot-ko-1.3b is a series of large-scale Korean autoregressive language models made by the EleutherAI polyglot team. The 1.3B parameter model consists of 24 transformer layers with a model dimension of 2048, and a feedforward dimension of 8192. It uses Rotary Position Embedding (RoPE) for positional encoding and is trained on 863GB of diverse Korean language data. Model Inputs and Outputs Inputs Text in Korean Outputs Predicted next token in Korean Capabilities polyglot-ko-1.3b is capable of generating coherent and fluent Korean text given a prompt. It demonstrates strong performance on several Korean language understanding benchmarks, outperforming other comparable models like skt/ko-gpt-trinity-1.2B-v0.5 and kakaobrain/kogpt. What Can I Use It For? polyglot-ko-1.3b can be used for a variety of Korean language tasks, such as text generation, summarization, translation, and question answering. It could be fine-tuned for specific domains or applications, like generating product descriptions, writing stories, or creating chatbots. However, as with any large language model, the outputs should be carefully curated and filtered before deployment, as the model may generate biased or inappropriate content. Things to Try One interesting aspect of polyglot-ko-1.3b is its ability to leverage positional information through the use of Rotary Position Embedding (RoPE). Experimenting with different RoPE configurations or prompts that require strong understanding of context and structure could yield interesting results. Additionally, comparing the performance of polyglot-ko-1.3b to the larger polyglot-ko-12.8b model could provide insights into the benefits of scaling up the model size.
Updated 5/10/2024