From the model card
Fields below are copied from the tags and counters on the HuggingFace repository Qwen/Qwen3-8B 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)
- Qwen
- Pipeline tag
- text-generation
- Library
- Transformers
- Weight formats
- safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Lineage
-
- base model Qwen/Qwen3-8B-Base
- fine-tune of Qwen/Qwen3-8B-Base
- Papers cited
- arXiv:2309.00071, arXiv:2505.09388
- Downloads (HF counter at last fetch)
- 12,826,795
- Likes (HF counter at last fetch)
- 1,342
- Model card
- https://huggingface.co/Qwen/Qwen3-8B
Use cases
- General-purpose instruction following on single-GPU deployments
- Code generation and explanation across popular programming languages
- Multilingual text generation for Qwen3's supported languages
- RAG pipeline generation where 4B models underperform on complex queries
- Self-hosted LLM replacement for API-cost-sensitive applications
Pros
- Apache 2.0 license for unrestricted commercial deployment
- 8B provides meaningfully better reasoning than 4B models on structured tasks
- Text-generation-inference compatible for production serving
- Actively maintained Qwen3 family with regular model updates
Cons
- Requires 16-24GB GPU VRAM at FP16 — quantization needed for consumer GPUs
- Still outperformed by 14B+ models on hard reasoning and long-context tasks
- Competitive 8B models (Llama 3.1-8B, Gemma 3-8B) should be benchmarked per task
- Knowledge cutoff and potential biases in multilingual domains require validation
- MoE variants in same parameter range can offer better efficiency tradeoffs
Tags
transformerssafetensorsqwen3text-generationconversationalarxiv:2309.00071arxiv:2505.09388base_model:Qwen/Qwen3-8B-Basebase_model:finetune:Qwen/Qwen3-8B-Baselicense:apache-2.0eval-resultstext-generation-inferenceendpoints_compatibleregion:usdeploy:sagemakerdeploy:azure