Text Classification
Transformers
Safetensors
PyTorch
English
deberta-v2
facebook
meta
llama
llama-3
text-embeddings-inference
Instructions to use meta-llama/Prompt-Guard-86M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meta-llama/Prompt-Guard-86M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="meta-llama/Prompt-Guard-86M")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("meta-llama/Prompt-Guard-86M") model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Prompt-Guard-86M", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Llama 4.5 When?
#30
by RexTRO111 - opened
Llama 4.5 when?
Ideas:
Knowledge cutoff: December 2025 or January 2026
The most logical lineup for Llama 4.5 follows Meta's shift toward hybrid Mixture-of-Experts (MoE) architectures and fine-grained dense edge models 🔥✍️:
| Tier | Model Variant | Parameter Size | Architecture / Active Params | Primary Target |
|---|---|---|---|---|
| Edge / Mobile | Llama 4.5 Tiny | 3B & 8B | Dense Transformer | On-device, local laptops, phone inference 📱 |
| Efficient MoE | Llama 4.5 Scout Plus | 109B total | MoE (~17B active) | Long-context RAG, single-node high-throughput ⚡ |
| Workhorse / Flagship | Llama 4.5 Maverick 2 | 400B total | MoE (~17B–34B active) | Frontier coding, math, multimodal reasoning 🧠 |
| Frontier Beast | Llama 4.5 Behemoth | ~2T total | MoE (~288B active) | Codistillation teacher, enterprise cluster scale 🗿 |
The Definitive Sweet Spot Pick
If choosing a single flagship size to spotlight for the release, pick 400B total (~17B active) MoE as the main, the olders also release — it delivers true frontier-tier reasoning while remaining deployable on standardized multi-GPU hardware setups 🚀✨.