Triangle104
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README.md
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This model was converted to GGUF format from [`prithivMLmods/Llama-Sentient-3.2-3B-Instruct`](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`prithivMLmods/Llama-Sentient-3.2-3B-Instruct`](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) for more details on the model.
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---
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Model details:
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The Llama-Sentient-3.2-3B-Instruct model is a fine-tuned version of the Llama-3.2-3B-Instruct model, optimized for text generation tasks, particularly where instruction-following abilities are critical. This model is trained on the mlabonne/lmsys-arena-human-preference-55k-sharegpt
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dataset, which enhances its performance in conversational and advisory
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contexts, making it suitable for a wide range of applications.
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Key Use Cases:
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Conversational AI: Engage in intelligent dialogue,
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offering coherent responses and following instructions, useful for
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customer support and virtual assistants.
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Text Generation: Generate high-quality,
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contextually appropriate content such as articles, summaries,
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explanations, and other forms of written communication based on user
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prompts.
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Instruction Following: Follow specific instructions
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with accuracy, making it ideal for tasks that require structured
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guidance, such as technical troubleshooting or educational assistance.
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The model uses a PyTorch-based architecture and includes a range of necessary files such as configuration files, tokenizer files, and model weight files for deployment.
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Intended Applications:
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Chatbots for virtual assistance, customer support, or as personal digital assistants.
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Content Creation Tools, aiding in the generation of written materials, blog posts, or automated responses based on user inputs.
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Educational and Training Systems, providing explanations and guided learning experiences in various domains.
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Human-AI Interaction platforms, where the model can follow user instructions to provide personalized assistance or perform specific tasks.
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With its strong foundation in instruction-following and conversational contexts, the Llama-Sentient-3.2-3B-Instruct model offers versatile applications for both general and specialized domains.
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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