RichardErkhov/proto-llm_-_uniwiz-7B-v0.1-gguf

Quantization made by Richard Erkhov.

uniwiz-7B-v0.1 - GGUF

Model creator: https://huggingface.co/proto-llm/
Original model: https://huggingface.co/proto-llm/uniwiz-7B-v0.1/

Name	Quant method	Size
uniwiz-7B-v0.1.Q2_K.gguf	Q2_K	2.53GB
uniwiz-7B-v0.1.IQ3_XS.gguf	IQ3_XS	2.81GB
uniwiz-7B-v0.1.IQ3_S.gguf	IQ3_S	2.96GB
uniwiz-7B-v0.1.Q3_K_S.gguf	Q3_K_S	2.95GB
uniwiz-7B-v0.1.IQ3_M.gguf	IQ3_M	3.06GB
uniwiz-7B-v0.1.Q3_K.gguf	Q3_K	3.28GB
uniwiz-7B-v0.1.Q3_K_M.gguf	Q3_K_M	3.28GB
uniwiz-7B-v0.1.Q3_K_L.gguf	Q3_K_L	3.56GB
uniwiz-7B-v0.1.IQ4_XS.gguf	IQ4_XS	3.67GB
uniwiz-7B-v0.1.Q4_0.gguf	Q4_0	3.83GB
uniwiz-7B-v0.1.IQ4_NL.gguf	IQ4_NL	3.87GB
uniwiz-7B-v0.1.Q4_K_S.gguf	Q4_K_S	3.86GB
uniwiz-7B-v0.1.Q4_K.gguf	Q4_K	4.07GB
uniwiz-7B-v0.1.Q4_K_M.gguf	Q4_K_M	4.07GB
uniwiz-7B-v0.1.Q4_1.gguf	Q4_1	4.24GB
uniwiz-7B-v0.1.Q5_0.gguf	Q5_0	4.65GB
uniwiz-7B-v0.1.Q5_K_S.gguf	Q5_K_S	4.65GB
uniwiz-7B-v0.1.Q5_K.gguf	Q5_K	4.78GB
uniwiz-7B-v0.1.Q5_K_M.gguf	Q5_K_M	4.78GB
uniwiz-7B-v0.1.Q5_1.gguf	Q5_1	5.07GB
uniwiz-7B-v0.1.Q6_K.gguf	Q6_K	5.53GB
uniwiz-7B-v0.1.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0

Model Overview:

Model Name: UniWiZ-7B-v0.1
Architecture: Mistral-7B
Training Objective: Knowledge and Safety Orchestration
Training Dataset: Curated dataset encompassing diverse knowledge domains and safety-focused content
Training Duration: [Specify training duration]

Intended Use:

UniWiZ-7B-v0.1 is designed for various natural language understanding tasks, including but not limited to text generation, summarization, question-answering, and conversation. Its training data emphasizes a broad spectrum of knowledge domains while incorporating safety considerations to ensure responsible and ethical use.

Scope of Applications:

UniWiZ-7B-v0.1 can be employed across a wide range of applications such as:

Content Generation: Creating human-like text for articles, blogs, creative writing, etc.
Summarization: Condensing lengthy texts into concise summaries while preserving key information.
Question-Answering: Responding to user queries by extracting relevant information from its extensive knowledge base.
Conversational Agents: Engaging in natural and contextually relevant conversations with users.
Educational Assistance: Providing explanations, definitions, and insights on various topics.

Data and Training:

UniWiZ-7B-v0.1 was trained on a diverse dataset encompassing knowledge from different domains. The training process included safety orchestration to mitigate biases and ensure ethical AI behavior. The model's architecture, Mistral-7B, enables it to understand and generate coherent and contextually relevant text.

Performance and Limitations:

While UniWiZ-7B-v0.1 demonstrates strong performance across a variety of tasks, it may exhibit limitations in:

Handling Uncommon or Specialized Topics: The model's knowledge is extensive but may not cover extremely niche or specialized subjects.
Sensitive Content: Despite safety measures, there is a possibility of generating content that may be considered inappropriate or offensive.

Users are encouraged to exercise discretion and provide feedback to improve the model's performance and address any potential biases or shortcomings.

Ethical Considerations:

UniWiZ-7B-v0.1 is developed with ethical AI principles in mind. Proto-AI is committed to addressing concerns related to bias, fairness, and the responsible use of AI technology. Users are encouraged to report unintended behavior or bias for continuous improvement.

Future Updates:

Proto-AI is dedicated to refining and enhancing UniWiZ-7B-v0.1. Regular updates will be released to improve performance, address user feedback, and incorporate the latest advancements in AI research.

This model card is a reference for users to understand UniWiZ-7B-v0.1's capabilities, limitations, and ethical considerations. Proto-AI values transparency and accountability in the deployment and use of AI models. More details about the model and training will be released later.