README.md

metadata

license: creativeml-openrail-m
datasets:
  - mlabonne/lmsys-arena-human-preference-55k-sharegpt
language:
  - en
base_model:
  - meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - Llama
  - Llama-Cpp
  - Llama3.2
  - Instruct
  - 3B
  - bin
  - Sentient

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

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

Original Model Card

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.