From the model card
Fields below are copied from the tags and counters on the HuggingFace repository deepseek-ai/DeepSeek-V3.2 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)
- deepseek-ai
- Pipeline tag
- text-generation
- Library
- Transformers
- Weight formats
- safetensors
- License tag
mit— read the license file in the repo before relying on it- Lineage
-
- base model deepseek-ai/DeepSeek-V3.2-Exp-Base
- fine-tune of deepseek-ai/DeepSeek-V3.2-Exp-Base
- Downloads (HF counter at last fetch)
- 1,485,301
- Likes (HF counter at last fetch)
- 1,473
- Model card
- https://huggingface.co/deepseek-ai/DeepSeek-V3.2
Use cases
- Complex reasoning and coding tasks requiring large model capacity
- Research into MoE architecture behavior at scale
- High-quality text generation where API cost is a concern vs. proprietary models
- Self-hosted deployment for privacy-sensitive applications at large scale
- Multilingual generation for languages well-represented in its training data
Pros
- MIT license allows unrestricted commercial use at MoE scale
- MoE architecture gives high effective capacity with lower per-token FLOPs than dense equivalent
- FP8 quantized weights available for reduced memory requirements
- Strong coding and reasoning benchmarks relative to its active parameter count
Cons
- Total model size requires multi-GPU or multi-node serving infrastructure
- FP8 inference requires hardware supporting float8 operations (NVIDIA Hopper or newer)
- MoE load balancing adds deployment complexity vs. dense models
- Inference at full quality is impractical without significant GPU resources
- Knowledge cutoff and potential training data biases require validation for production tasks
Tags
transformerssafetensorsdeepseek_v32text-generationconversationalbase_model:deepseek-ai/DeepSeek-V3.2-Exp-Basebase_model:finetune:deepseek-ai/DeepSeek-V3.2-Exp-Baselicense:miteval-resultsendpoints_compatiblefp8region:usdeploy:sagemaker