Llama 3.2 1B
par unsloth
Open source · 97k downloads · 64 likes
2.3
(64 avis)ChatAPI & LocalÀ propos
Le modèle Llama 3.2 1B est une version légère et optimisée de la famille Llama 3.2 développée par Meta, conçue pour des tâches de dialogue multilingue et des applications nécessitant une compréhension contextuelle avancée. Grâce à des techniques de fine-tuning et d'alignement par apprentissage par renforcement avec feedback humain, il excelle dans des cas d'usage comme la génération de texte conversationnel, le résumé de documents ou l'assistance agentique, tout en supportant huit langues majeures. Sa taille réduite le rend particulièrement adapté aux environnements où les ressources sont limitées, tout en offrant des performances comparables à des modèles plus lourds sur des benchmarks standards. Ce modèle se distingue par sa flexibilité, permettant une adaptation facile à des besoins spécifiques via des outils comme Unsloth, qui accélèrent son entraînement et son déploiement. Idéal pour les développeurs cherchant un équilibre entre efficacité et puissance, il combine accessibilité et robustesse pour des applications variées.
Documentation
See our collection for all versions of Llama 3.2 including GGUF, 4-bit and original 16-bit formats.
Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.2 (1B) here: https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing
Llama-3.2-1B
For more details on the model, please go to Meta's original model card
✨ 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-3.1 (11B vision) | ▶️ Start on Colab | 2.4x faster | 58% 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 |
| DPO - Zephyr | ▶️ Start on Colab | 1.9x faster | 19% less |
- This 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.
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.
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.
Liens & Ressources