Cerebras
Models by this creator
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Cerebras-GPT-13B
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The Cerebras-GPT-1.3B is a 1.3 billion parameter transformer-based language model developed by Cerebras Systems. It is part of the Cerebras-GPT family, which includes models ranging from 111M to 13B parameters, all trained on the The Pile dataset. These models demonstrate the scalability and simplicity of training large language models (LLMs) on the Cerebras software and hardware stack. The Cerebras-GPT models have been trained using the Chinchilla scaling laws, which is a compute-optimal approach. The Cerebras-GPT-1.3B model uses a GPT-3 style architecture with full attention, as opposed to the sparse banded attention used in the original GPT-3 models. It has 24 layers, a d_model of 2048, 16 attention heads, and a d_ffn of 8192. The model was trained on The Pile dataset, which was preprocessed and tokenized using byte-pair encoding. Model inputs and outputs Inputs Text prompts of up to 2048 tokens in length Outputs Continuation of the input text, generating new tokens autoregressively Capabilities The Cerebras-GPT-1.3B model is a powerful general-purpose language model capable of a variety of text generation tasks, such as creative writing, summarization, question answering, and more. It has been evaluated on a suite of standardized benchmarks, where it exhibits strong performance compared to other publicly available LLMs of similar size. What can I use it for? The primary intended use of the Cerebras-GPT-1.3B model is to further research into large language models. Researchers working on NLP, AI applications, ethics, and alignment can use this model as a foundation. The model is released under an Apache 2.0 license, allowing for free commercial use. You can fine-tune and adapt the Cerebras-GPT-1.3B model for deployment via the Cerebras Model Studio or third-party libraries. However, additional safety-related testing and mitigations should be applied before using the model in production downstream applications. Things to try The Cerebras-GPT-1.3B model supports a maximum sequence length of 2048 tokens during training and inference. This allows it to generate coherent and context-rich text, going beyond the capabilities of shorter-context models. Exploring use cases that benefit from this increased sequence length, such as long-form writing or multi-turn dialogues, could yield interesting results.
Updated 8/7/2024
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btlm-3b-8k-base
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The btlm-3b-8k-base is a 3 billion parameter language model with an 8k context length trained on 627B tokens of the SlimPajama dataset by Cerebras. It sets a new standard for 3B parameter models, outperforming models trained on hundreds of billions more tokens and achieving comparable performance to open 7B parameter models. The model can also be quantized to 4-bit to fit in devices with as little as 3GB of memory. Model inputs and outputs This model is a text-to-text transformer that takes in a text prompt and generates relevant text output. It has a high context length of 8k tokens, enabling long-form applications. Inputs Text prompts**: The model accepts text prompts as input, which can be of varying lengths. Outputs Generated text**: The model outputs relevant generated text based on the input prompt. Capabilities The btlm-3b-8k-base model demonstrates state-of-the-art performance for a 3B parameter model, surpassing models with hundreds of billions more training tokens. It also supports 8k sequence lengths and can be efficiently quantized to 4-bit, making it usable on devices with limited memory. What can I use it for? The btlm-3b-8k-base model can be used for a variety of natural language processing tasks, such as text generation, summarization, and question answering. Its high context length makes it well-suited for long-form applications like story writing, dialogue, and document generation. Additionally, the model's small size and efficient quantization allow it to be deployed on resource-constrained devices. Things to try One key feature of the btlm-3b-8k-base model is its ability to handle long input sequences of up to 8k tokens. This enables applications that require reasoning over long contexts, like multi-document summarization or long-form story generation. Researchers and developers can experiment with using the model's high context capacity to tackle these types of tasks.
Updated 5/28/2024
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Cerebras-GPT-111M
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Cerebras-GPT-111M is a 111 million parameter transformer-based language model developed by Cerebras Systems. It is part of the Cerebras-GPT family, which ranges from 111M to 13B parameters. All Cerebras-GPT models follow the Chinchilla scaling laws and were trained on the Pile dataset using Cerebras' weight streaming technology on their Andromeda AI supercomputer. These models demonstrate the scalability and simplicity of training large language models on Cerebras' hardware and software stack. The Cerebras-GPT-111M model specifically is the smallest in the Cerebras-GPT family, with 111 million parameters. It uses a GPT-3 style architecture, with a sequence length of 2048, 12 attention heads, and a feed-forward network dimension of 3072. The model was trained for 9,037 steps with a batch size of 120 and a learning rate of 6e-4. Compared to the larger Cerebras-GPT models, the 111M version trades off some performance for a smaller model size and faster inference. As shown in the evaluations, it achieves strong results on language modeling and few-shot downstream tasks, though the larger models outperform it. Model inputs and outputs Inputs Text prompt**: The model takes a text prompt as input and generates a continuation of the text. Outputs Generated text**: The model outputs a continuation of the input text, generating new tokens autoregressively. Capabilities The Cerebras-GPT-111M model demonstrates strong few-shot learning capabilities, achieving competitive results on benchmark tasks like Wino-Grande, PIQA, and OpenBookQA compared to much larger models. This shows the efficiency of the Cerebras training approach, which allows them to achieve high performance with a relatively small model size. While the 111M model does not match the absolute performance of the largest 13B version, it provides a good balance of capability and efficiency. The model can be useful for applications that do not require the full capabilities of the largest Cerebras-GPT models, but still want to leverage strong few-shot learning. What can I use it for? The primary intended use of the Cerebras-GPT models is to further research into large language models. Researchers can use these models as foundation models for NLP, AI ethics, and alignment work. Practitioners may also find these models useful as reference implementations, leveraging the pre-trained checkpoints and training setups documented in the Cerebras-GPT paper. You can fine-tune and adapt the Cerebras-GPT-111M model for your own applications using either the Cerebras Model Studio or third-party fine-tuning libraries. However, you should apply additional safety-related testing and mitigations before deploying the model in production environments. Things to try An interesting aspect of the Cerebras-GPT models is their use of Chinchilla scaling laws, which optimize the model size and training compute for the best performance. This allows the smaller 111M model to punch above its weight in few-shot learning. You could experiment with prompts that leverage this few-shot capability, and compare the performance to larger language models. Additionally, the Cerebras weight streaming technology allows for efficient scaling of training across multiple nodes. You could explore how this impacts the training time and efficiency compared to more traditional training approaches for large language models.
Updated 5/28/2024
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Cerebras-GPT-6.7B
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Cerebras-GPT-6.7B is part of the Cerebras-GPT family of language models developed by Cerebras Systems. The Cerebras-GPT models are released to facilitate research into scaling laws for large language models (LLMs) using open architectures and datasets. The models demonstrate the simplicity and scalability of training LLMs on Cerebras' software and hardware stack. The Cerebras-GPT family includes models ranging from 111M to 13B parameters, all trained following the Chinchilla scaling laws which is compute-optimal. The models were trained on the Andromeda AI supercomputer using Cerebras' weight streaming technology to efficiently scale training across multiple nodes. Similar models in the Cerebras-GPT family include the Cerebras-GPT-13B with 13B parameters, as well as smaller 111M, 256M, 590M, 1.3B, and 2.7B parameter versions. Model inputs and outputs Inputs Text prompt**: The model takes a text prompt as input and generates additional text in response. Outputs Generated text**: The model outputs a sequence of generated text, continuing from the provided prompt. Capabilities The Cerebras-GPT-6.7B model is capable of generating human-like text on a wide variety of topics. It can be used for tasks like text summarization, open-ended question answering, creative writing, and more. The model's large size and training on a diverse dataset enable it to draw insights and generate coherent text on complex subjects. What can I use it for? Cerebras-GPT-6.7B can be a valuable tool for researchers and practitioners working on natural language processing and large language model development. The model can be fine-tuned on specific tasks or datasets to adapt its capabilities for various applications. For example, you could fine-tune the model on a domain-specific corpus to create a content generation tool for your industry. Or you could use the model as a starting point for research into few-shot learning, prompt engineering, or multi-modal AI systems. Cerebras also offers cloud-based systems for pre-training and fine-tuning through the Cerebras Model Studio, making it easier to leverage the power of this model for your projects. Things to try One interesting aspect of the Cerebras-GPT-6.7B model is its support for long sequence lengths, enabled by the use of Learned Positional Encoding. This allows the model to generate coherent text over extended passages, which could be useful for tasks like story generation or long-form content creation. Another intriguing possibility is to explore the model's few-shot learning capabilities. Since the Cerebras-GPT models were trained following the Chinchilla scaling laws, they may exhibit strong performance on downstream tasks with limited fine-tuning data. Experimenting with different prompting techniques and few-shot learning setups could uncover novel applications for this model.
Updated 5/28/2024
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Llama3-DocChat-1.0-8B
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The Llama3-DocChat-1.0-8B model, developed by Cerebras, is an 8 billion parameter large language model built on top of the Llama 3 base. It is designed for document-based conversational question answering, building on insights from NVIDIA's ChatQA model series. Cerebras leveraged their expertise in LLM training and dataset curation to improve upon the limitations of the ChatQA datasets and training recipes. Additionally, they employed synthetic data generation to address gaps that could not be fully resolved with available real data. Model inputs and outputs Inputs Text**: The model takes natural language text as input, which can include questions, instructions, or dialogue. Outputs Text**: The model generates relevant and coherent natural language responses to the input text. Capabilities The Llama3-DocChat-1.0-8B model excels at conversational question answering tasks, particularly when the context is provided in the form of documents. It can understand and respond to queries that require reasoning over the provided information, and it outperforms several popular models on relevant benchmarks. What can I use it for? The Llama3-DocChat-1.0-8B model can be used to build applications that involve document-based question answering, such as: Customer support**: Enabling users to ask questions and get answers based on product manuals, FAQs, or other relevant documentation. Research assistance**: Helping researchers find relevant information and answer questions based on a corpus of academic papers or reports. Intelligent search**: Enhancing search experiences by providing direct answers to queries, rather than just a list of relevant documents. Things to try One interesting aspect of the Llama3-DocChat-1.0-8B model is its ability to handle multi-turn conversations. By leveraging the provided context, the model can engage in a back-and-forth dialogue, building upon previous exchanges to provide more comprehensive and relevant responses. Developers can explore ways to incorporate this capability into their applications to create more natural and helpful conversational experiences.
Updated 9/19/2024
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Cerebras-GPT-1.3B
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The Cerebras-GPT-1.3B is a 1.3 billion parameter transformer-based language model developed by Cerebras Systems. It is part of the Cerebras-GPT family, which includes models ranging from 111M to 13B parameters, all trained on the The Pile dataset. These models demonstrate the scalability and simplicity of training large language models (LLMs) on the Cerebras software and hardware stack. The Cerebras-GPT models have been trained using the Chinchilla scaling laws, which is a compute-optimal approach. The Cerebras-GPT-1.3B model uses a GPT-3 style architecture with full attention, as opposed to the sparse banded attention used in the original GPT-3 models. It has 24 layers, a d_model of 2048, 16 attention heads, and a d_ffn of 8192. The model was trained on The Pile dataset, which was preprocessed and tokenized using byte-pair encoding. Model inputs and outputs Inputs Text prompts of up to 2048 tokens in length Outputs Continuation of the input text, generating new tokens autoregressively Capabilities The Cerebras-GPT-1.3B model is a powerful general-purpose language model capable of a variety of text generation tasks, such as creative writing, summarization, question answering, and more. It has been evaluated on a suite of standardized benchmarks, where it exhibits strong performance compared to other publicly available LLMs of similar size. What can I use it for? The primary intended use of the Cerebras-GPT-1.3B model is to further research into large language models. Researchers working on NLP, AI applications, ethics, and alignment can use this model as a foundation. The model is released under an Apache 2.0 license, allowing for free commercial use. You can fine-tune and adapt the Cerebras-GPT-1.3B model for deployment via the Cerebras Model Studio or third-party libraries. However, additional safety-related testing and mitigations should be applied before using the model in production downstream applications. Things to try The Cerebras-GPT-1.3B model supports a maximum sequence length of 2048 tokens during training and inference. This allows it to generate coherent and context-rich text, going beyond the capabilities of shorter-context models. Exploring use cases that benefit from this increased sequence length, such as long-form writing or multi-turn dialogues, could yield interesting results.
Updated 9/6/2024
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Cerebras-GPT-2.7B
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The Cerebras-GPT-2.7B is a transformer-based language model developed by Cerebras Systems. It is part of the Cerebras-GPT family, which includes models ranging from 111M to 13B parameters. All Cerebras-GPT models have been trained using the Chinchilla scaling laws, which is a compute-optimal approach. These models were trained on the Andromeda AI supercomputer using Cerebras' weight streaming technology to enable efficient scaling across nodes. The Cerebras-GPT models are available on Hugging Face, and the checkpoints can be accessed in the Cerebras Model Zoo. The Cerebras-GPT-2.7B model has 2.7 billion parameters and follows a GPT-3 style architecture. Model inputs and outputs Inputs Text prompts that the model can use to generate continuation or completion of the input. Outputs Continued or completed text based on the input prompt. The model can generate coherent and contextually relevant text, making it suitable for a variety of natural language processing tasks. Capabilities The Cerebras-GPT-2.7B model can be used for a range of language generation tasks, such as text completion, summarization, and open-ended dialogue. Its capabilities have been evaluated on various benchmarks, including linguistic reasoning, physical and scientific reasoning, and downstream applications. The model has shown strong performance, outperforming GPT-2 and GPT-3 models of similar sizes on these tasks. What can I use it for? The primary intended use of the Cerebras-GPT models is to further research into large language models. These models can serve as foundation models for various NLP applications, ethics, and alignment research. Researchers and practitioners working to improve LLMs can use the Cerebras-GPT models as reference implementations, training setups, and pre-trained checkpoints. You can fine-tune and adapt the Cerebras-GPT-2.7B model for deployment using either the Cerebras Model Studio or third-party libraries. However, further safety-related testing and mitigations should be applied before using the model in production downstream applications. Things to try One interesting aspect of the Cerebras-GPT models is their support for long sequence lengths. The models were trained with a maximum sequence length of 2,048 tokens, and the larger models, such as the 6.7B and 13B versions, can extrapolate to even longer sequences of up to 10,000 tokens with good performance. This makes the Cerebras-GPT models suitable for tasks that require processing of long-form text, such as document summarization or long-form content generation.
Updated 9/6/2024