From the model card
Fields below are copied from the tags and counters on the HuggingFace repository meta-llama/Llama-3.2-1B-Instruct at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- meta-llama
- Pipeline tag
- text-generation
- Library
- Transformers
- Framework tags
- PyTorch
- Weight formats
- safetensors
- License tag
llama3.2— read the license file in the repo before relying on it- Language tags
- English (en), German (de), French (fr), Italian (it), Portuguese (pt), Hindi (hi), Spanish (es), Thai (th)
- Papers cited
- arXiv:2204.05149, arXiv:2405.16406
- Downloads (HF counter at last fetch)
- 6,072,423
- Likes (HF counter at last fetch)
- 1,604
- Model card
- https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct
Use cases
- On-device inference on mobile hardware or microcontrollers
- Ultra-low-latency text generation in embedded applications
- Lightweight intent detection or text reformatting on CPU-only servers
- Minimum viable LLM integration for latency-critical pipelines
- Testing and debugging LLM integration code with minimal resource usage
Pros
- 1B scale enables deployment on very constrained hardware
- English instruction following at minimal compute cost
- Part of Meta's maintained Llama 3.2 family
Cons
- Llama 3.2 license restricts use by platforms with 700M+ monthly users
- 1B reasoning depth is severely limited — unreliable on multi-step tasks
- Outperformed by Qwen3-0.6B and similar compact instruction models on most benchmarks
- English-only; no multilingual support at this scale in this model
- Not suitable for tasks requiring factual accuracy or complex reasoning
Tags
transformerssafetensorsllamatext-generationfacebookmetapytorchllama-3conversationalendefritpthiestharxiv:2204.05149arxiv:2405.16406license:llama3.2