par unsloth
Open source · 105k downloads · 10 likes
Le modèle Llama 3.2 3B Instruct optimisé par Unsloth en 4 bits est une version légère et performante du modèle Llama 3.2 de Meta, conçue pour des tâches de dialogue et d'assistance conversationnelle. Grâce à une quantification dynamique en 4 bits, il offre une précision accrue tout en réduisant significativement l'empreinte mémoire et les besoins en calcul, ce qui le rend accessible même sur des configurations modestes. Ce modèle excelle dans la génération de texte multilingue, notamment en anglais, français, allemand, espagnol et d'autres langues, et se distingue par sa capacité à gérer des cas d'usage variés comme le résumé de documents, l'extraction d'informations ou les interactions agentiques. Ce qui le rend particulièrement attractif, c'est sa facilité d'utilisation et son optimisation pour le fine-tuning, permettant aux développeurs d'adapter rapidement le modèle à des besoins spécifiques avec des ressources limitées. Idéal pour les projets nécessitant une IA conversationnelle efficace et économique, il se positionne comme une solution polyvalente pour les développeurs et les entreprises souhaitant intégrer des capacités avancées de langage sans investir dans du matériel coûteux.
See our collection for versions of Llama 3.2 including GGUF & 4-bit formats.
Unsloth's Dynamic 4-bit Quants is selectively quantized, greatly improving accuracy over standard 4-bit.
We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb
For more details on the model, please go to Meta's original model card
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 |
A huge thank you to the Meta and Llama team for creating and releasing these models.
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.
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.
