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QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF

This is quantized version of prithivMLmods/Llama-Sentient-3.2-3B-Instruct created using llama.cpp

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Llama-Sentient-3.2-3B-Instruct Modelfile

File Name Size Description Upload Status
.gitattributes 1.57 kB Git attributes configuration file Uploaded
README.md 42 Bytes Initial commit README Uploaded
config.json 1.04 kB Configuration file Uploaded
generation_config.json 248 Bytes Generation configuration file Uploaded
pytorch_model-00001-of-00002.bin 4.97 GB PyTorch model file (part 1) Uploaded (LFS)
pytorch_model-00002-of-00002.bin 1.46 GB PyTorch model file (part 2) Uploaded (LFS)
pytorch_model.bin.index.json 21.2 kB Model index file Uploaded
special_tokens_map.json 477 Bytes Special tokens mapping Uploaded
tokenizer.json 17.2 MB Tokenizer JSON file Uploaded (LFS)
tokenizer_config.json 57.4 kB Tokenizer configuration file Uploaded
Model Type Size Context Length Link
GGUF 3B - 🤗 Llama-Sentient-3.2-3B-Instruct-GGUF

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 dataset, which enhances its performance in conversational and advisory contexts, making it suitable for a wide range of applications.

Key Use Cases:

  1. Conversational AI: Engage in intelligent dialogue, offering coherent responses and following instructions, useful for customer support and virtual assistants.
  2. Text Generation: Generate high-quality, contextually appropriate content such as articles, summaries, explanations, and other forms of written communication based on user prompts.
  3. Instruction Following: Follow specific instructions with accuracy, making it ideal for tasks that require structured guidance, such as technical troubleshooting or educational assistance.

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.

Intended Applications:

  • Chatbots for virtual assistance, customer support, or as personal digital assistants.
  • Content Creation Tools, aiding in the generation of written materials, blog posts, or automated responses based on user inputs.
  • Educational and Training Systems, providing explanations and guided learning experiences in various domains.
  • Human-AI Interaction platforms, where the model can follow user instructions to provide personalized assistance or perform specific tasks.

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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