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:

Conversational AI: Engage in intelligent dialogue, offering coherent responses and following instructions, useful for customer support and virtual assistants.
Text Generation: Generate high-quality, contextually appropriate content such as articles, summaries, explanations, and other forms of written communication based on user prompts.
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.