Llama-3.2-3B-Instruct-unsloth-bnb-4bit Open-Source Large Language Model - An Option for Efficient Conversation and Information Processing

Llama 3.2 3B Instruct Unsloth Bnb 4bit

Developed by unsloth

An efficient large language model optimized with Unsloth's dynamic 4-bit quantization technology, based on Meta Llama 3.2-3B-Instruct

Large Language Model

Transformers

English#Dynamic 4-bit Quantization #Multilingual Dialogue Optimization #Efficient Fine-tuning Acceleration

Downloads 240.35k

Release Time : 1/23/2025

Model Overview

The Llama 3.2 series is a generative model optimized for multilingual dialogue use cases, supporting languages such as English, German, French, and more, suitable for agent retrieval and summarization tasks

Model Features

Dynamic 4-bit Quantization

Utilizes Unsloth's selective quantization technology, significantly improving accuracy compared to standard 4-bit quantization

Efficient Fine-tuning

Achieves 2-5x faster fine-tuning speeds with Unsloth technology, reducing memory usage by 70%

Multilingual Support

Optimized for 8 major languages, though training data covers a broader range of languages

Dialogue Optimization

Specially tuned for multilingual dialogue use cases, including agent retrieval and summarization tasks

Model Capabilities

Multilingual text generation

Dialogue system construction

Text summarization

Information retrieval

Use Cases

Dialogue Systems

Multilingual Customer Support Bot

Build intelligent customer support systems supporting multiple languages

Outperforms many open-source and closed-source chat models in supported languages

Content Generation

Multilingual Content Creation

Generate marketing copy, articles, and other content in multiple languages

🚀 unsloth/Llama-3.2-3B-unsloth-bnb-4bit

Fine-tune LLMs 2 - 5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: Google Colab Notebook

⚠️ Important Note

See our collection for versions of Llama 3.2 including GGUF & 4-bit formats.

💡 Usage Tip

Unsloth's Dynamic 4-bit Quants is selectively quantized, greatly improving accuracy over standard 4-bit.

🚀 Quick Start

For more details on the model, please go to Meta's original model card

✨ Features

Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports	Free Notebooks	Performance	Memory use
Llama-3.2 (3B)	▶️ Start on Colab	2.4x faster	58% less
Llama-3.2 (11B vision)	▶️ Start on Colab	2x faster	60% less
Qwen2 VL (7B)	▶️ Start on Colab	1.8x faster	60% less
Qwen2.5 (7B)	▶️ Start on Colab	2x faster	60% less
Llama-3.1 (8B)	▶️ Start on Colab	2.4x faster	58% less
Phi-3.5 (mini)	▶️ Start on Colab	2x faster	50% less
Gemma 2 (9B)	▶️ Start on Colab	2.4x faster	58% less
Mistral (7B)	▶️ Start on Colab	2.2x faster	62% less

This Llama 3.2 conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

📚 Documentation

Special Thanks

A huge thank you to the Meta and Llama team for creating and releasing these models.

Model Information

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction - tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction - tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.

Property	Details
Model developer	Meta
Model Architecture	Llama 3.2 is an auto - regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine - tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Supported languages	English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine - tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
Llama 3.2 family of models	Token counts refer to pretraining data only. All model versions use Grouped - Query Attention (GQA) for improved inference scalability.
Model Release Date	Sept 25, 2024
Status	This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
License	Use of Llama 3.2 is governed by the Llama 3.2 Community License (a custom, commercial license agreement).

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go here.

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