Causallm
Models by this creator
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14B
291
The CausalLM 14B model is a large language model developed by the CausalLM team. It is fully compatible with the Meta LLaMA 2 model and can be loaded using the Transformers library without requiring external code. The model can be quantized using GGUF, GPTQ, and AWQ methods for efficient inference on various hardware. The CausalLM 14B-DPO-alpha version has been shown to outperform the Zephyr-7b model on the MT-Bench evaluation, demonstrating strong performance compared to other models of similar size. The CausalLM 7B-DPO-alpha version also performs well on this benchmark. Both the 14B and 7B models have high consistency, so the 7B version can be used as a more efficient alternative if your hardware has insufficient VRAM. Model inputs and outputs Inputs Text prompts in the chatml format Outputs Generated text continuations based on the input prompt Capabilities The CausalLM 14B model has demonstrated strong performance on a variety of benchmarks, including MMLU, CEval, and GSM8K, often outperforming other models of similar size. It has also achieved a high win rate on the AlpacaEval Leaderboard, indicating its effectiveness in open-ended dialogue tasks. What can I use it for? The CausalLM 14B model can be used for a wide range of natural language processing tasks, such as text generation, question answering, and language modeling. Its strong performance on benchmarks suggests it could be useful for applications like conversational AI, content creation, and knowledge-based systems. Things to try One interesting aspect of the CausalLM 14B model is its compatibility with the LLaVA1.5 prompt format, which enables rapid implementation of effective multimodal capabilities by aligning the ViT Projection module with the frozen language model under visual instructions. This could be an exciting area to explore for researchers and developers interested in building multimodal AI systems.
Updated 5/28/2024
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7B
136
The 7B model from CausalLM is a 7 billion parameter causal language model that is fully compatible with the Meta LLaMA 2 model. It outperforms existing models of 33B parameters or less across most quantitative evaluations. The model was trained using synthetic and filtered datasets, with a focus on improving safety and helpfulness. It provides a strong open-source alternative to proprietary large language models. Model inputs and outputs Inputs Text**: The model takes in text as input, which can be used to generate additional text. Outputs Text**: The model outputs generated text, which can be used for a variety of natural language processing tasks. Capabilities The 7B model from CausalLM exhibits strong performance across a range of benchmarks, outperforming existing models of 33B parameters or less. It has been carefully tuned to provide safe and helpful responses, making it well-suited for use in production systems and assistants. The model is also fully compatible with the popular llama.cpp library, allowing for efficient deployment on a variety of hardware. What can I use it for? The CausalLM 7B model can be used for a wide range of natural language processing tasks, such as text generation, language modeling, and conversational AI. Its strong performance and safety-focused training make it a compelling option for building production-ready AI assistants and applications. Developers can leverage the model's capabilities through the Transformers library or integrate it directly with the llama.cpp library for efficient CPU and GPU-accelerated inference. Things to try One interesting aspect of the CausalLM 7B model is its compatibility with the Meta LLaMA 2 model. Developers can leverage this compatibility to seamlessly integrate the model into existing systems and workflows that already support LLaMA 2. Additionally, the model's strong performance on quantitative benchmarks suggests that it could be a powerful tool for a variety of natural language tasks, from text generation to question answering.
Updated 5/27/2024
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14B-DPO-alpha
110
The 14B-DPO-alpha model from CausalLM is a large language model that has undergone Direct Preference Optimization (DPO) training. This model is an optimized version of the CausalLM/14B model, with some detailed parameters changed during the DPO process. The CausalLM/7B-DPO-alpha is a smaller, 7B parameter version that has also undergone DPO training. Model inputs and outputs Inputs The model accepts natural language text as input, similar to other large language models. Outputs The model generates natural language text as output, with the ability to perform a wide variety of tasks such as question answering, summarization, and text generation. Capabilities The 14B-DPO-alpha model has demonstrated strong performance on a range of benchmarks, including outperforming all ~13B chat models on the MT-Bench leaderboard as of December 2023. The DPO-trained versions have achieved higher scores than the original CausalLM/14B and CausalLM/7B models on this benchmark. What can I use it for? The 14B-DPO-alpha model can be used for a variety of natural language processing tasks, such as text generation, question answering, and summarization. The model's strong performance on benchmarks suggests it could be useful for applications that require high-quality language understanding and generation, such as chatbots, content creation tools, and academic or research applications. Things to try One key insight about the 14B-DPO-alpha model is that it has been optimized through Direct Preference Optimization (DPO) training, which aims to align the model's outputs with human preferences. This could make the model useful for applications where safety and alignment with human values are important, such as conversational AI assistants. Experimenting with the model's behavior and outputs, and evaluating its safety and reliability, could provide valuable insights into the potential of DPO-trained models.
Updated 5/28/2024
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72B-preview-llamafied-qwen-llamafy
73
The 72B-preview-llamafied-qwen-llamafy model is a large language model created by CausalLM. It is a 72 billion parameter "chat model" that has been "llamafied" and is described as a preview version with no performance guarantees. This model is compatible with the Meta LLaMA 2 model and can be used with the transformers library to load the model and tokenizer. The model was initialized from the Qwen 72B model and has gone through some training and editing, but details on the exact process are limited. It is available under a GPL3 license for this preview version, with the final version planned to be under a WTFPL license. Model inputs and outputs Inputs Freeform text prompts in the "chatml" format, which is a conversational format with markers for the start and end of the human and system messages. Outputs Freeform text responses generated by the model in continuation of the provided prompt. Capabilities The 72B-preview-llamafied-qwen-llamafy model is a large language model capable of generating human-like text on a wide range of topics. It has been compared to the performance of other large models like GPT-4 and ChatGPT, but with the caveat that it is still a preview version with no guarantees about its performance. What can I use it for? This model could potentially be used for a variety of natural language processing tasks, such as: Chatbots and virtual assistants Content generation (e.g. articles, stories, product descriptions) Question answering Summarization Language translation However, users should be cautious as the model was trained on unfiltered internet data, so the outputs may contain offensive or inappropriate content. It is recommended to implement your own safety and content filtering measures when using this model. Things to try One interesting aspect of this model is its compatibility with the Meta LLaMA 2 model. This means that the model architecture and training process are likely similar, which could allow for further fine-tuning or transfer learning between the two models. Additionally, the use of the "chatml" format for inputs and outputs suggests that the model may be well-suited for conversational AI applications, where maintaining a coherent dialogue is important.
Updated 5/28/2024
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35b-beta-long
60
CausalLM/35b-beta-long is a large language model released by the CausalLM team. It was fine-tuned on a dataset of over 30 million multi-turn dialogue entries synthesized from web crawl data, aiming to improve the model's ability to extract thematic summaries, compare information across sources, and perform other long-context tasks. The model was initialized with the weights of Cohere's 35B parameter multilingual MHA model, which the CausalLM team found to be the most responsive to high-quality training data during the supervised fine-tuning process. The fine-tuning process incorporated both the original training data and the synthesized dialogue data to achieve a more balanced performance profile. Compared to the original base model, this fine-tuned version demonstrates improved long-context capabilities and reduced hallucinations, as well as enhanced general abilities like math, coding, and knowledge recall. It is fully compatible with Meta LLaMA 2 and can be used with the Transformers library. Model inputs and outputs Inputs Free-form text prompts in the chatml format Outputs Generated text continuations of the input prompt The model can be used for a variety of text-to-text tasks, such as question answering, summarization, and general language generation Capabilities The CausalLM/35b-beta-long model demonstrates improved performance on long-context tasks compared to the original base model. It is better able to extract thematic summaries, compare information across sources, and recall abstract concepts from the training data. The model also shows enhanced general abilities like math, coding, and knowledge recall. What can I use it for? The CausalLM/35b-beta-long model can be used for a wide range of text-to-text tasks, such as: Question answering on complex, multi-document topics Long-form summarization of lengthy documents or web pages Generating coherent and informative multi-turn dialogues Providing detailed analysis and comparison of source materials Given its improved long-context capabilities and reduced hallucinations, the model could be particularly useful for applications that require extracting and synthesizing information from multiple sources, such as research, analysis, or customer support. Things to try One interesting aspect of the CausalLM/35b-beta-long model is its use of synthesized dialogue data during the fine-tuning process. This approach appears to have yielded significant improvements in the model's ability to maintain coherence and recall relevant information over long stretches of text. To better understand the model's capabilities, you could try prompting it with multi-part questions or tasks that require drawing insights from multiple sources. For example, you could ask the model to summarize and compare the key points from a set of related articles, or to generate a detailed, fact-based dialogue on a complex topic. Additionally, you may want to explore the model's performance on specialized or technical tasks, such as code generation, mathematical problem-solving, or domain-specific question answering. The CausalLM team notes that the model has shown promising results in these areas as well.
Updated 7/2/2024
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7B-DPO-alpha
59
The CausalLM/7B-DPO-alpha model is a 7B parameter language model developed by CausalLM that has undergone Direct Preference Optimization (DPO) training. It is an optimized version of the CausalLM/7B model, aiming to improve alignment with human preferences. Compared to other 7B models like Zephyr-7b- and GPT-3.5-Turbo, the CausalLM/7B-DPO-alpha model achieves higher performance on the MT-Bench benchmark, scoring 7.038125. This suggests the DPO training has improved the model's overall capabilities. Model inputs and outputs Inputs Raw text prompts that the model can use to generate coherent, contextual responses. Outputs Generated text continuations of the input prompts, with the goal of producing human-like, informative, and aligned responses. Capabilities The CausalLM/7B-DPO-alpha model can be used for a variety of text-to-text tasks, such as: Open-ended conversation and dialogue Question answering Summarization Creative writing Code generation The model's improved alignment through DPO training aims to make it more reliable and safer to use for these applications. What can I use it for? The CausalLM/7B-DPO-alpha model could be useful for companies or individuals looking to build language-based AI assistants, chatbots, or content generation tools. Its enhanced performance and alignment properties make it a potentially valuable model for these types of applications. Some example use cases could include: Building a customer service chatbot to handle inquiries and provide helpful information Automating the generation of blog posts, product descriptions, or other marketing content Developing an AI writing assistant to help users brainstorm ideas or improve their writing Things to try One interesting aspect of the CausalLM/7B-DPO-alpha model is its potential for improved safety and reliability compared to earlier language models. You could try prompting the model with requests that require ethical reasoning or sensitivity, and observe how it responds. Additionally, the model's strong performance on the MT-Bench benchmark suggests it may excel at more technical or analytical tasks, such as code generation or data analysis. You could experiment with using the model for these types of applications and see how it performs. Overall, the CausalLM/7B-DPO-alpha model appears to be a capable and potentially valuable language model, with improvements in both capability and alignment. Exploring its various use cases and strengths could lead to interesting applications and insights.
Updated 5/28/2024
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34b-beta
56
The 34b-beta model is a large language model created by CausalLM. It is a 34 billion parameter model that is designed for text-to-text generation tasks. The model builds on the capabilities of other large language models like the CausalLM 7B and CausalLM 14B versions, which have demonstrated strong performance on a variety of benchmarks. Model inputs and outputs Inputs The model accepts natural language prompts in the chatml format. The model can take prompts of varying lengths, though there are some precision issues with longer sequences that will be addressed in future updates. Outputs The model generates human-like text continuations of the provided prompts. The outputs can be used for a wide range of text-to-text generation tasks, such as content creation, question answering, and dialogue. Capabilities The 34b-beta model has shown strong performance on a variety of benchmarks, including MMLU where it achieved an average accuracy of 63.82%, outperforming many smaller models. It has also performed well on the CEval and GSM8K benchmarks. Additionally, the model has demonstrated a high win rate of 88.26% on the AlpacaEval leaderboard, suggesting it has strong conversational and task-completion abilities. What can I use it for? The 34b-beta model can be used for a wide range of text-to-text generation tasks, such as content creation, question answering, dialogue, and more. Given its strong performance on benchmarks, it could be a valuable tool for companies or individuals working on language-based applications or services. However, it's important to note that the model was trained on unfiltered internet data, so users will need to carefully monitor the outputs for any objectionable content. Things to try One interesting aspect of the 34b-beta model is its potential for multimodal capabilities. The model was fine-tuned on the prompt format introduced in LLaVA1.5, which is unrelated to image attention calculation. This suggests that the model may be able to effectively integrate visual information, opening up possibilities for tasks like image captioning or visual question answering. Users interested in exploring these capabilities should consider aligning the ViT Projection module with the frozen language model. Additionally, the model's strong performance on the MMLU and CEval benchmarks indicates that it could be a useful tool for knowledge-intensive tasks, such as question answering or fact-checking. Users may want to experiment with prompts that leverage the model's broad base of knowledge.
Updated 5/28/2024