Bartowski
Models by this creator
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gemma-2-9b-it-GGUF
138
The gemma-2-9b-it-GGUF model is a quantized version of the Google/gemma-2-9b-it model, created by the maintainer bartowski. Similar models include the Codestral-22B-v0.1-GGUF, Meta-Llama-3-8B-Instruct-GGUF, LLaMA3-iterative-DPO-final-GGUF, and Llama-3-Lumimaid-8B-v0.1-OAS-GGUF-IQ-Imatrix. These models use the llama.cpp library for quantization, with various dataset and hyperparameter choices. Model inputs and outputs The gemma-2-9b-it-GGUF model is a text-to-text AI model, taking a user prompt as input and generating a corresponding text response. Inputs User prompt**: The text prompt provided by the user to the model. Outputs Generated text**: The text response generated by the model based on the user prompt. Capabilities The gemma-2-9b-it-GGUF model has been quantized to various file sizes, allowing users to choose a version that fits their hardware and performance requirements. The model is capable of generating high-quality, coherent text responses on a wide range of topics. It can be used for tasks such as language generation, text summarization, and question answering. What can I use it for? The gemma-2-9b-it-GGUF model can be used in a variety of applications, such as chatbots, content generation, and language-based assistants. For example, you could use the model to build a virtual assistant that can engage in natural conversations, or to generate summaries of long-form text. The maintainer has also provided quantized versions of other large language models, such as the Codestral-22B-v0.1-GGUF and Meta-Llama-3-8B-Instruct-GGUF, which may be suitable for different use cases or hardware constraints. Things to try One interesting thing to try with the gemma-2-9b-it-GGUF model is to experiment with the different quantization levels and their impact on performance and quality. The maintainer has provided a range of options, from the high-quality Q8_0 version to the more compact Q2_K and IQ2 variants. By testing these different versions, you can find the best balance between model size, inference speed, and output quality for your specific use case and hardware.
Updated 7/31/2024
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Codestral-22B-v0.1-GGUF
137
The Codestral-22B-v0.1-GGUF is a language model developed by bartowski and quantized using the llama.cpp framework. This 22B parameter model is an extension of the original Codestral-22B-v0.1 model, offering various quantized versions to suit different performance and storage requirements. Model inputs and outputs The Codestral-22B-v0.1-GGUF model is a text-to-text AI model, designed to take in textual prompts and generate relevant responses. Inputs Textual prompts in a specific format: [INST] > {system_prompt} {prompt} [/INST] Outputs Generated text responses based on the provided prompts Capabilities The Codestral-22B-v0.1-GGUF model is capable of performing a wide range of text generation tasks, such as natural language generation, question answering, and language translation. The model's performance can be fine-tuned by adjusting the quantization level, allowing users to balance quality, file size, and memory requirements. What can I use it for? The Codestral-22B-v0.1-GGUF model can be utilized in various applications that require advanced language understanding and generation, such as: Chatbots and virtual assistants Content creation and summarization Dialogue systems Language translation Personalized recommendation systems Things to try Experiment with different prompts and system prompts to explore the model's capabilities in tasks like creative writing, analytical reasoning, and task-oriented dialogue. Additionally, you can try different quantization levels to find the optimal balance between model performance and resource requirements for your specific use case.
Updated 6/29/2024
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Codestral-22B-v0.1-GGUF
137
The Codestral-22B-v0.1-GGUF is a language model developed by bartowski and quantized using the llama.cpp framework. This 22B parameter model is an extension of the original Codestral-22B-v0.1 model, offering various quantized versions to suit different performance and storage requirements. Model inputs and outputs The Codestral-22B-v0.1-GGUF model is a text-to-text AI model, designed to take in textual prompts and generate relevant responses. Inputs Textual prompts in a specific format: [INST] > {system_prompt} {prompt} [/INST] Outputs Generated text responses based on the provided prompts Capabilities The Codestral-22B-v0.1-GGUF model is capable of performing a wide range of text generation tasks, such as natural language generation, question answering, and language translation. The model's performance can be fine-tuned by adjusting the quantization level, allowing users to balance quality, file size, and memory requirements. What can I use it for? The Codestral-22B-v0.1-GGUF model can be utilized in various applications that require advanced language understanding and generation, such as: Chatbots and virtual assistants Content creation and summarization Dialogue systems Language translation Personalized recommendation systems Things to try Experiment with different prompts and system prompts to explore the model's capabilities in tasks like creative writing, analytical reasoning, and task-oriented dialogue. Additionally, you can try different quantization levels to find the optimal balance between model performance and resource requirements for your specific use case.
Updated 6/29/2024
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gemma-2-27b-it-GGUF
102
The gemma-2-27b-it-GGUF model is a quantized version of the original gemma-2-27b-it model, created by maintainer bartowski. Similar quantized models like gemma-2-9b-it-GGUF, LLaMA3-iterative-DPO-final-GGUF, Codestral-22B-v0.1-GGUF, and Meta-Llama-3-8B-Instruct-GGUF are also available from the same maintainer. Model inputs and outputs The gemma-2-27b-it-GGUF model is a text-to-text model, taking in a prompt as input and generating a text response as output. The model does not support a system prompt. Inputs Prompt**: The input text that the model will use to generate a response. Outputs Text response**: The model's generated output text, based on the input prompt. Capabilities The gemma-2-27b-it-GGUF model can be used for a variety of text generation tasks, such as language modeling, summarization, translation, and more. It has been quantized using llama.cpp to provide a range of options for file size and performance tradeoffs, allowing users to select the version that best fits their hardware and use case. What can I use it for? With its broad capabilities, the gemma-2-27b-it-GGUF model can be used for a wide range of applications, such as: Content Generation**: The model can be used to generate articles, stories, product descriptions, and other types of text content. Chatbots and Conversational Agents**: The model can be used to power the language understanding and response generation components of chatbots and virtual assistants. Summarization**: The model can be used to summarize long-form text, such as news articles or research papers. Translation**: The model can be used to translate text between different languages. Things to try One interesting aspect of the gemma-2-27b-it-GGUF model is the range of quantized versions available, allowing users to find the right balance between file size and performance for their specific needs. Users can experiment with the different quantization levels to see how they impact the model's output quality and speed, and choose the version that works best for their use case. Another interesting thing to try is using the model for tasks beyond just text generation, such as text classification or text-based reasoning. The model's broad language understanding capabilities may make it useful for a variety of NLP applications.
Updated 8/7/2024
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LLaMA3-iterative-DPO-final-GGUF
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The LLaMA3-iterative-DPO-final-GGUF model is a series of quantized versions of the LLaMA3-iterative-DPO-final model, created by maintainer bartowski. The model was quantized using llama.cpp to provide various file sizes and tradeoffs between quality and memory usage. This allows users to choose the version that best fits their hardware and performance requirements. Similar models include the Meta-Llama-3-8B-Instruct-GGUF, which is a series of quantized versions of Meta's Llama-3-8B Instruct model, also created by bartowski. Model inputs and outputs Inputs System prompt**: Provides the context and instructions for the assistant User prompt**: The text input from the user Outputs Assistant response**: The generated text response from the model Capabilities The LLaMA3-iterative-DPO-final-GGUF model is capable of generating human-like text responses based on the provided prompts. It can be used for a variety of text-to-text tasks, such as open-ended conversation, question answering, and creative writing. What can I use it for? The LLaMA3-iterative-DPO-final-GGUF model can be used for projects that require natural language generation, such as chatbots, virtual assistants, and content creation tools. The different quantized versions allow users to balance performance and memory usage based on their specific hardware and requirements. Things to try One interesting aspect of the LLaMA3-iterative-DPO-final-GGUF model is the range of quantized versions available. Users can experiment with the different file sizes and bit depths to find the optimal balance of quality and memory usage for their use case. For example, the Q6_K version provides very high quality with a file size of 6.59GB, while the Q4_K_S version has a smaller file size of 4.69GB with slightly lower quality, but still good performance.
Updated 6/26/2024
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Meta-Llama-3.1-8B-Instruct-GGUF
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The Meta-Llama-3.1-8B-Instruct-GGUF model is a set of quantized versions of the Meta-Llama-3.1-8B-Instruct model, created by bartowski using the llama.cpp framework. These quantized models offer a range of file sizes and quality trade-offs, allowing users to choose the best fit for their hardware and performance requirements. The model is similar to other quantized LLaMA-based models and Phi-3 models created by the same maintainer. Model inputs and outputs The Meta-Llama-3.1-8B-Instruct-GGUF model is a text-to-text model, accepting natural language prompts as input and generating human-like responses as output. Inputs Natural language prompts in English Outputs Human-like responses in English Capabilities The Meta-Llama-3.1-8B-Instruct-GGUF model is capable of engaging in a wide variety of natural language tasks, such as question answering, text summarization, and open-ended conversation. The model has been trained on a large corpus of text data and can draw upon a broad knowledge base to provide informative and coherent outputs. What can I use it for? The Meta-Llama-3.1-8B-Instruct-GGUF model could be useful for building chatbots, virtual assistants, or other applications that require natural language processing and generation. The model's flexibility and broad knowledge base make it suitable for use in a variety of domains, from customer service to education to creative writing. Additionally, the range of quantized versions available allows users to choose the model that best fits their hardware and performance requirements. Things to try One interesting aspect of the Meta-Llama-3.1-8B-Instruct-GGUF model is its ability to adapt to different prompt formats and styles. Users could experiment with providing the model with prompts in various formats, such as the provided prompt format, to see how it responds and how the output changes. Additionally, users could try providing the model with prompts that require reasoning, analysis, or creativity to see how it handles more complex tasks.
Updated 9/6/2024
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Meta-Llama-3-8B-Instruct-GGUF
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The Meta-Llama-3-8B-Instruct-GGUF is a quantized version of the Meta-Llama-3-8B-Instruct model, created by bartowski using the llama.cpp library. This 8-billion parameter model is part of the larger Llama 3 family of language models developed by Meta, which includes both pre-trained and instruction-tuned variants in 8 and 70 billion parameter sizes. The Llama 3 instruction-tuned models are optimized for dialog use cases and outperform many open-source chat models on common benchmarks. Model inputs and outputs Inputs Text input only Outputs Generated text and code Capabilities The Meta-Llama-3-8B-Instruct-GGUF model is capable of a wide range of natural language processing tasks, from open-ended conversations to code generation. It has been shown to excel at multi-turn dialogues, general world knowledge, and coding prompts. The 8-billion parameter size makes it a fast and efficient model, yet it still outperforms larger models like Llama 2 on many benchmarks. What can I use it for? This model would be well-suited for building conversational AI assistants, automating routine tasks through natural language interfaces, or enhancing existing applications with language understanding and generation capabilities. The instruction-tuned nature of the model makes it particularly adept at following user requests and guidelines, making it a good fit for customer service, content creation, and other interactive use cases. Things to try One interesting aspect of this model is its ability to adapt its personality and tone to the given system prompt. For example, by instructing the model to respond as a "pirate chatbot who always responds in pirate speak", you can generate creative, engaging conversations with a unique character. This flexibility allows the model to be tailored to diverse scenarios and user preferences.
Updated 6/17/2024
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DeepSeek-Coder-V2-Lite-Instruct-GGUF
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The DeepSeek-Coder-V2-Lite-Instruct-GGUF model is a quantized version of the original DeepSeek-Coder-V2-Lite-Instruct model, created using llama.cpp by the maintainer bartowski. This model is designed for text-to-text tasks and offers a range of quantized versions to suit different performance and storage requirements. Model inputs and outputs The DeepSeek-Coder-V2-Lite-Instruct-GGUF model takes in a user prompt and generates a response from the assistant. The model does not have a separate system prompt input. Inputs Prompt**: The user's input text that the model will generate a response to. Outputs Assistant response**: The text generated by the model in response to the user's prompt. Capabilities The DeepSeek-Coder-V2-Lite-Instruct-GGUF model is capable of a wide range of text-to-text tasks, including language generation, question answering, and code generation. It can be used for tasks such as chatbots, creative writing, and programming assistance. What can I use it for? The DeepSeek-Coder-V2-Lite-Instruct-GGUF model can be used for a variety of applications, such as building conversational AI assistants, generating creative content, and assisting with programming tasks. For example, you could use it to create a chatbot that can engage in natural conversations, generate stories or poems, or help with coding challenges. Things to try One interesting thing to try with the DeepSeek-Coder-V2-Lite-Instruct-GGUF model is to experiment with the different quantized versions available, as they offer a range of performance and storage trade-offs. You could test out the various quantization levels and see how they impact the model's capabilities and efficiency on your specific use case.
Updated 8/7/2024
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Phi-3-medium-128k-instruct-GGUF
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The Phi-3-medium-128k-instruct model is an AI language model created by Microsoft and optimized for text generation and natural language understanding tasks. It is a medium-sized version of the Phi-3 series of models, which are based on the Transformer architecture and trained on a large corpus of text data. The model has been further fine-tuned on an instruction dataset, giving it the ability to understand and generate responses to a wide range of prompts and tasks. The maintainer, bartowski, has provided several quantized versions of the model using the llama.cpp library, which allow the model to be used on a variety of hardware configurations with different performance and storage requirements. Model inputs and outputs Inputs Prompt**: The text to be used as input for the model, which can be a question, statement, or any other type of natural language text. Outputs Generated text**: The model's response to the input prompt, which can be a continuation of the text, a relevant answer, or a new piece of text generated based on the input. Capabilities The Phi-3-medium-128k-instruct model is capable of generating coherent and contextually appropriate text across a wide range of domains, including creative writing, analytical tasks, and open-ended conversations. It has been trained to understand and follow instructions, allowing it to assist with tasks such as research, summarization, and problem-solving. What can I use it for? The Phi-3-medium-128k-instruct model can be used for a variety of natural language processing tasks, such as: Content generation**: The model can be used to generate articles, stories, or other forms of written content based on a given prompt or topic. Question answering**: The model can be used to answer questions or provide information on a wide range of topics. Task completion**: The model can be used to assist with tasks that require natural language understanding and generation, such as data analysis, report writing, or code generation. Things to try One interesting aspect of the Phi-3-medium-128k-instruct model is its ability to adapt to different prompting styles and formats. For example, you could experiment with providing the model with structured prompts or templates, such as those used in the Meta-Llama-3-8B-Instruct-GGUF model, to see how it responds and how the output might differ from more open-ended prompts. Another area to explore is the model's performance on specific types of tasks or domains, such as creative writing, technical documentation, or scientific analysis. By testing the model on a variety of tasks, you can gain a better understanding of its strengths and limitations, and potentially identify ways to further fine-tune or optimize it for your particular use case.
Updated 9/6/2024
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Reflection-Llama-3.1-70B-GGUF
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The Reflection-Llama-3.1-70B-GGUF is a large language model developed by the researcher bartowski. It is based on the Llama architecture, a widely-used family of models known for their strong performance on a variety of natural language tasks. This particular model has been trained on a large corpus of text data, allowing it to generate human-like responses on a wide range of subjects. Model inputs and outputs The Reflection-Llama-3.1-70B-GGUF model takes in natural language text as input and generates human-like responses as output. The input can be in the form of a question, statement, or any other type of prompt, and the model will attempt to provide a relevant and coherent response. Inputs Natural language text prompts Outputs Human-like text responses Capabilities The Reflection-Llama-3.1-70B-GGUF model is capable of engaging in complex reasoning and reflection, as indicated by the developer's instruction to use a specific prompt format for improved reasoning. This suggests the model can go beyond simple language generation and perform more advanced cognitive tasks. What can I use it for? The Reflection-Llama-3.1-70B-GGUF model could be useful for a variety of applications, such as conversational AI assistants, text generation for creative writing or content creation, and even tasks that require complex reasoning and analysis. The developer has provided instructions for using the model with the llama.cpp library and LM Studio, which could be a good starting point for experimentation and development. Things to try One interesting aspect of the Reflection-Llama-3.1-70B-GGUF model is the use of "thought" and "output" tokens, which the developer suggests can be enabled for improved visibility of the model's reasoning process. This could be a valuable feature for understanding how the model arrives at its responses, and could be an area worth exploring further.
Updated 9/19/2024