Thebloke
Models by this creator
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Llama-2-7B-Chat-GGML
811
The Llama-2-7B-Chat-GGML is a version of Meta's Llama 2 model that has been converted to the GGML format for efficient CPU and GPU inference. It is a 7 billion parameter large language model optimized for dialogue and chat use cases. The model was created by TheBloke, who has generously provided multiple quantized versions of the model to enable fast inference on a variety of hardware. This model outperforms many open-source chat models on industry benchmarks and provides a helpful and safe assistant-like conversational experience. Similar models include the Llama-2-13B-GGML with 13 billion parameters, and the Llama-2-70B-Chat-GGUF with 70 billion parameters. These models follow a similar architecture and optimization process as the 7B version. Model inputs and outputs Inputs Text**: The model takes text prompts as input, which can include instructions, context, and conversation history. Outputs Text**: The model generates coherent and contextual text responses to continue the conversation or complete the given task. Capabilities The Llama-2-7B-Chat-GGML model is capable of engaging in open-ended dialogue, answering questions, and assisting with a variety of tasks such as research, analysis, and creative writing. It has been optimized for safety and helpfulness, making it suitable for use as a conversational assistant. What can I use it for? This model could be used to power conversational AI applications, virtual assistants, or chatbots. It could also be fine-tuned for specific domains or use cases, such as customer service, education, or creative writing. The quantized GGML version enables efficient deployment on a wide range of hardware, making it accessible to developers and researchers. Things to try You can try using the Llama-2-7B-Chat-GGML model to engage in open-ended conversations, ask it questions on a variety of topics, or provide it with prompts to generate creative text. The model's capabilities can be explored through frameworks like text-generation-webui or llama.cpp, which support the GGML format.
Updated 5/28/2024
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Llama-2-13B-chat-GGML
680
The Llama-2-13B-chat-GGML model is a 13-billion parameter large language model created by Meta and optimized for dialogue use cases. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters and are designed for a variety of natural language generation tasks. This specific model has been converted to the GGML format, which is designed for CPU and GPU inference using tools like llama.cpp and associated libraries and UIs. The GGML format has since been superseded by GGUF, so users are encouraged to use the GGUF versions of these models going forward. Similar models include the Llama-2-7B-Chat-GGML and the Llama-2-13B-GGML, which offer smaller and larger versions of the Llama 2 architecture in the GGML format. Model Inputs and Outputs Inputs Raw text Outputs Generated text continuations Capabilities The Llama-2-13B-chat-GGML model is capable of engaging in open-ended dialogue, answering questions, and generating coherent and context-appropriate text continuations. It has been fine-tuned to perform well on benchmarks for helpfulness and safety, making it suitable for use in assistant-like applications. What Can I Use It For? The Llama-2-13B-chat-GGML model could be used to power conversational AI assistants, chatbots, or other applications that require natural language generation and understanding. Given its strong performance on safety metrics, it may be particularly well-suited for use cases where providing helpful and trustworthy responses is important. Things to Try One interesting aspect of the Llama-2-13B-chat-GGML model is its ability to handle context and engage in multi-turn conversations. Users could try prompting the model with a series of related questions or instructions to see how it maintains coherence and builds upon previous responses. Additionally, the model's quantization options allow for tuning the balance between performance and accuracy, so users could experiment with different quantization levels to find the optimal tradeoff for their specific use case.
Updated 5/27/2024
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Mixtral-8x7B-Instruct-v0.1-GGUF
560
The Mixtral-8x7B-Instruct-v0.1-GGUF is a large language model created by Mistral AI. It is a fine-tuned version of the Mixtral 8X7B Instruct v0.1 model, which has been optimized for instruction-following tasks. This model outperforms the popular Llama 2 70B model on many benchmarks, according to the maintainer. Model inputs and outputs The Mixtral-8x7B-Instruct-v0.1-GGUF model is a text-to-text model, meaning it takes text as input and generates text as output. Inputs Text prompts**: The model accepts text prompts as input, which can include instructions, questions, or other types of text. Outputs Generated text**: The model outputs generated text, which can include answers, stories, or other types of content. Capabilities The Mixtral-8x7B-Instruct-v0.1-GGUF model has been fine-tuned on a variety of publicly available conversation datasets, making it well-suited for instruction-following tasks. According to the maintainer, the model outperforms the Llama 2 70B model on many benchmarks, demonstrating its strong capabilities in natural language processing and generation. What can I use it for? The Mixtral-8x7B-Instruct-v0.1-GGUF model can be used for a variety of natural language processing tasks, such as: Chatbots and virtual assistants**: The model's ability to understand and follow instructions can make it a useful component in building conversational AI systems. Content generation**: The model can be used to generate text, such as stories, articles, or product descriptions, based on prompts. Question answering**: The model can be used to answer questions on a wide range of topics. Things to try One interesting aspect of the Mixtral-8x7B-Instruct-v0.1-GGUF model is its use of the GGUF format, which is a new file format introduced by the llama.cpp team. This format is designed to replace the older GGML format, which is no longer supported by llama.cpp. You can try using the model with various GGUF-compatible tools and libraries, such as llama.cpp, KoboldCpp, LM Studio, and others, to see how it performs in different environments.
Updated 5/28/2024
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Wizard-Vicuna-30B-Uncensored-GPTQ
547
The Wizard-Vicuna-30B-Uncensored-GPTQ model is a large language model created by Eric Hartford and quantized to GPTQ format by TheBloke. This model is a version of the Wizard Vicuna 30B Uncensored model that has been optimized for efficient GPU inference. TheBloke has also provided multiple GPTQ parameter permutations to allow users to choose the best one for their hardware and requirements. Some similar models from TheBloke include the WizardLM-7B-uncensored-GPTQ, a 7B version of the Wizard LM model, and the Nous-Hermes-13B-GPTQ, a GPTQ version of the Nous-Hermes-13B model. Model inputs and outputs Inputs Text**: The model takes in text prompts as input. Outputs Text**: The model generates text outputs in response to the input prompt. Capabilities The Wizard-Vicuna-30B-Uncensored-GPTQ model can be used for a variety of natural language processing tasks, such as text generation, question answering, and language translation. As an uncensored model, it has fewer built-in guardrails than some other language models, so users should be cautious about the content they generate. What can I use it for? This model could be used for tasks like creative writing, chatbots, language learning, and research. However, given its uncensored nature, users should be thoughtful about how they apply the model and take responsibility for the content it generates. Things to try One interesting thing to try with this model is to prompt it with open-ended questions or creative writing prompts and see the types of responses it generates. The high parameter count and lack of censorship may result in some unexpected or novel outputs. Just be mindful of the potential risks and use the model responsibly.
Updated 5/28/2024
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Mistral-7B-Instruct-v0.1-GGUF
490
The Mistral-7B-Instruct-v0.1-GGUF is an AI model created by Mistral AI and generously supported by a grant from andreessen horowitz (a16z). It is a 7 billion parameter large language model that has been fine-tuned for instruction following capabilities. This model outperforms the base Mistral 7B v0.1 on a variety of benchmarks, including a 105% improvement on the HuggingFace leaderboard. The model is available in a range of quantized versions to optimize for different hardware and performance needs. Model Inputs and Outputs The Mistral-7B-Instruct-v0.1-GGUF model takes natural language prompts as input and generates relevant and coherent text outputs. The prompts can be free-form text or structured using the provided ChatML prompt template. Inputs Natural language prompts**: Free-form text prompts for the model to continue or expand upon. ChatML-formatted prompts**: Prompts structured using the ChatML format with ` and ` tokens. Outputs Generated text**: The model's continuation or expansion of the input prompt, generating relevant and coherent text. Capabilities The Mistral-7B-Instruct-v0.1-GGUF model excels at a variety of text-to-text tasks, including open-ended generation, question answering, and task completion. It demonstrates strong performance on benchmarks like the HuggingFace leaderboard, AGIEval, and BigBench-Hard, outperforming the base Mistral 7B model. The model's instruction-following capabilities allow it to understand and execute a wide range of prompts and tasks. What can I use it for? The Mistral-7B-Instruct-v0.1-GGUF model can be used for a variety of applications that require natural language processing and generation, such as: Content generation**: Writing articles, stories, scripts, or other creative content based on prompts. Dialogue systems**: Building chatbots and virtual assistants that can engage in natural conversations. Task completion**: Helping users accomplish various tasks by understanding instructions and generating relevant outputs. Question answering**: Providing informative and coherent answers to questions on a wide range of topics. By leveraging the model's impressive performance and instruction-following capabilities, developers and researchers can build powerful applications that harness the model's strengths. Things to try One interesting aspect of the Mistral-7B-Instruct-v0.1-GGUF model is its ability to follow complex instructions and complete multi-step tasks. Try providing the model with a series of instructions or a step-by-step process, and observe how it responds and executes the requested actions. This can be a revealing way to explore the model's reasoning and problem-solving capabilities. Another interesting experiment is to provide the model with open-ended prompts that require critical thinking or creativity, such as "Explain the impact of artificial intelligence on society" or "Write a short story about a future where robots coexist with humans." Observe how the model approaches these types of prompts and the quality and coherence of its responses. By exploring the model's strengths and limitations through a variety of input prompts and tasks, you can gain a deeper understanding of its capabilities and potential applications.
Updated 5/28/2024
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Mixtral-8x7B-v0.1-GGUF
414
Mixtral-8x7B-v0.1 is a large language model (LLM) created by Mistral AI_. It is a pretrained generative Sparse Mixture of Experts model that outperforms the Llama 2 70B model on most benchmarks according to the maintainer. The model is provided in a variety of quantized formats by TheBloke to enable efficient inference on CPU and GPU. Model inputs and outputs Mixtral-8x7B-v0.1 is an autoregressive language model that takes text as input and generates new text as output. The model can be used for a variety of natural language generation tasks. Inputs Text prompts for the model to continue or elaborate on Outputs Newly generated text continuation of the input prompt Responses to open-ended questions or instructions Capabilities Mixtral-8x7B-v0.1 is a highly capable language model that can be used for tasks such as text generation, question answering, and code generation. The model demonstrates strong performance on a variety of benchmarks and is able to produce coherent and relevant text. What can I use it for? Mixtral-8x7B-v0.1 could be used for a wide range of natural language processing applications, such as: Chatbots and virtual assistants Content generation for marketing, journalism, or creative writing Code generation and programming assistance Question answering and knowledge retrieval Things to try Some interesting things to try with Mixtral-8x7B-v0.1 include: Exploring the model's capabilities for creative writing by providing it with open-ended prompts Assessing the model's ability to follow complex instructions or multi-turn conversations Experimenting with the quantized model variants provided by TheBloke to find the best balance of performance and efficiency Overall, Mixtral-8x7B-v0.1 is a powerful language model that can be utilized in a variety of applications. Its strong performance and the availability of quantized versions make it an attractive option for developers and researchers.
Updated 5/28/2024
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Llama-2-7B-Chat-GGUF
377
The Llama-2-7B-Chat-GGUF model is a 7 billion parameter large language model created by Meta. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters. The Llama 2 models are designed for dialogue use cases and have been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align them to human preferences for helpfulness and safety. Compared to open-source chat models, the Llama-2-Chat models outperform on many benchmarks and are on par with some popular closed-source models like ChatGPT and PaLM in human evaluations. The model is maintained by TheBloke, who has generously provided GGUF format versions of the model with various quantization levels to enable efficient CPU and GPU inference. Similar GGUF models are also available for the larger 13B and 70B versions of the Llama 2 model. Model inputs and outputs Inputs Text**: The model takes text prompts as input, which can be anything from a single question to multi-turn conversational exchanges. Outputs Text**: The model generates text continuations in response to the input prompt. This can range from short, concise responses to more verbose, multi-sentence outputs. Capabilities The Llama-2-7B-Chat-GGUF model is capable of engaging in open-ended dialogue, answering questions, and generating text on a wide variety of topics. It demonstrates strong performance on tasks like commonsense reasoning, world knowledge, reading comprehension, and mathematical problem solving. Compared to earlier versions of the Llama model, the Llama 2 chat models also show improved safety and alignment with human preferences. What can I use it for? The Llama-2-7B-Chat-GGUF model can be used for a variety of natural language processing tasks, such as building chatbots, question-answering systems, text summarization tools, and creative writing assistants. Given its strong performance on benchmarks, it could be a good starting point for building more capable AI assistants. The quantized GGUF versions provided by TheBloke also make the model accessible for deployment on a wide range of hardware, from CPUs to GPUs. Things to try One interesting thing to try with the Llama-2-7B-Chat-GGUF model is to engage it in multi-turn dialogues and observe how it maintains context and coherence over the course of a conversation. You could also experiment with providing the model with prompts that require reasoning about hypotheticals or abstract concepts, and see how it responds. Additionally, you could try fine-tuning or further training the model on domain-specific data to see if you can enhance its capabilities for particular applications.
Updated 5/28/2024
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Llama-2-13B-chat-GPTQ
357
The Llama-2-13B-chat-GPTQ model is a version of Meta's Llama 2 13B language model that has been quantized using GPTQ, a technique for reducing the model's memory footprint without significant loss in quality. This model was created by TheBloke, a prominent AI researcher and developer. TheBloke has also made available GPTQ versions of the Llama 2 7B and 70B models, as well as other quantized variants using different techniques. The Llama-2-13B-chat-GPTQ model is designed for chatbot and conversational AI applications, having been fine-tuned by Meta on dialogue data. It outperforms many open-source chat models on standard benchmarks and is on par with closed-source models like ChatGPT and PaLM in terms of helpfulness and safety. Model inputs and outputs Inputs The model accepts text input, which can be prompts, questions, or conversational messages. Outputs The model generates text output, which can be responses, answers, or continuations of the input. Capabilities The Llama-2-13B-chat-GPTQ model demonstrates strong natural language understanding and generation capabilities. It can engage in open-ended dialogue, answer questions, and assist with a variety of natural language tasks. The model has been imbued with an understanding of common sense and world knowledge, allowing it to provide informative and contextually relevant responses. What can I use it for? The Llama-2-13B-chat-GPTQ model is well-suited for building chatbots, virtual assistants, and other conversational AI applications. It can be used to power customer service bots, AI tutors, creative writing assistants, and more. The model's capabilities also make it useful for general-purpose language generation tasks, such as content creation, summarization, and language translation. Things to try One interesting aspect of the Llama-2-13B-chat-GPTQ model is its ability to maintain a consistent personality and tone across conversations. You can experiment with different prompts and see how the model adapts its responses to the context and your instructions. Additionally, you can try providing the model with specific constraints or guidelines to observe how it navigates ethical and safety considerations when generating text.
Updated 5/28/2024
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Mistral-7B-Instruct-v0.2-GGUF
345
The Mistral-7B-Instruct-v0.2-GGUF is a text generation model created by Mistral AI_. It is a fine-tuned version of the original Mistral 7B Instruct v0.2 model, using the GGUF file format. GGUF is a new format introduced by the llama.cpp team that replaces the older GGML format. This model provides quantized variants optimized for different hardware and performance requirements. Model inputs and outputs The Mistral-7B-Instruct-v0.2-GGUF model takes text prompts as input and generates coherent and informative text responses. The model has been fine-tuned on a variety of conversational datasets to enable it to engage in helpful and contextual dialogue. Inputs Text prompts**: The model accepts free-form text prompts that can cover a wide range of topics. The prompts should be wrapped in [INST] and [/INST] tags to indicate that they are instructions for the model. Outputs Text responses**: The model will generate relevant and coherent text responses to the provided prompts. The responses can be of varying length depending on the complexity of the prompt. Capabilities The Mistral-7B-Instruct-v0.2-GGUF model is capable of engaging in open-ended dialogue, answering questions, and providing informative responses on a wide variety of topics. It demonstrates strong language understanding and generation abilities, and can adapt its tone and personality to the context of the conversation. What can I use it for? This model could be useful for building conversational AI assistants, chatbots, or other applications that require natural language understanding and generation. The fine-tuning on instructional datasets also makes it well-suited for tasks like content generation, question answering, and task completion. Potential use cases include customer service, education, research assistance, and creative writing. Things to try One interesting aspect of this model is its ability to follow multi-turn conversations and maintain context. You can try providing a series of related prompts and see how the model's responses build upon the previous context. Additionally, you can experiment with adjusting the temperature and other generation parameters to see how they affect the creativity and coherence of the model's outputs.
Updated 5/27/2024
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Wizard-Vicuna-13B-Uncensored-GPTQ
302
The Wizard-Vicuna-13B-Uncensored-GPTQ is a large language model developed by Eric Hartford and maintained by TheBloke. It is a quantized version of the Wizard Vicuna 13B Uncensored model, using the GPTQ compression technique to reduce the model size while maintaining performance. This model is part of a suite of quantized models provided by TheBloke, including Wizard-Vicuna-30B-Uncensored-GPTQ and WizardLM-7B-uncensored-GPTQ. Model inputs and outputs The Wizard-Vicuna-13B-Uncensored-GPTQ model is a text-to-text model, capable of generating natural language responses given text prompts. The model follows the standard Vicuna prompt format, where the user's input is prefixed with "USER:" and the model's response is prefixed with "ASSISTANT:". Inputs Text prompts provided by the user, which the model uses to generate a response. Outputs Natural language text generated by the model in response to the user's input. Capabilities The Wizard-Vicuna-13B-Uncensored-GPTQ model is capable of engaging in open-ended dialogue, answering questions, and generating creative text. It has been fine-tuned to provide helpful, detailed, and polite responses, while avoiding harmful, unethical, or biased content. What can I use it for? The Wizard-Vicuna-13B-Uncensored-GPTQ model can be used for a variety of natural language processing tasks, such as building chatbots, virtual assistants, and text generation applications. Its large size and strong performance make it well-suited for tasks that require in-depth language understanding and generation. Developers can use this model as a starting point for further fine-tuning or deployment in their own applications. Things to try One interesting aspect of the Wizard-Vicuna-13B-Uncensored-GPTQ model is its ability to generate long, coherent responses. You can try providing the model with open-ended prompts and see how it develops a detailed, multi-paragraph answer. Additionally, you can experiment with different temperature and sampling settings to adjust the creativity and diversity of the model's outputs.
Updated 5/28/2024