DeepSeek has published the V3.1-Base model weights to Hugging Face, which can be downloaded and used directly. Many media outlets called this "open source" release; However, the current model card is not available, the license is not marked, and the strict boundaries of use still need to be subject to the official follow-up instructions. The online service has been upgraded to V3.1, claiming that the context length has been expanded to 128K, and the Web/App/Mini Program remains compatible with the API.
1. Key information
- Release form: Hugging Face provides V3.1-Base weights (safetensors), and the page shows that it supports BF16 / F8_E4M3 / F32, providing a variety of quantitative versions.
- Scale parameters: The page displays "Model size:685B params".
- Context length: Multiple reports and official announcements show that the online model has been upgraded to 128K context, and the API call method remains unchanged.
- Architectural background: The V3 series is a MoE (Mixture-of-Experts) route, combined with DeepSeek's self-developed MLA and other technologies; V3.1 On this basis, engineering and context enhancement are done (according to public materials and media summaries).
- Notes: There are no detailed and permission fields for the current Hugging Face model card; You need to check the license and terms before downloading and commercializing.
2. Open source address (official and authoritative entrance)
- Hugging Face · DeepSeek-V3.1-Base:
https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base
2, DeepSeek official website (products and APIs):
https://www.deepseek.com/
3, DeepSeek-V3 GitHub (Architecture and Thesis Background Reference):
https://github.com/deepseek-ai/DeepSeek-V3
3. Availability and deployment suggestions
- Download and format: SafeTensors is preferred; Pick the BF16 or FP8(F8_E4M3) quantified variants by hardware.
- Inference resources: 685B (MoE total parameters) level model requires high video memory/distributed inference; If resources are limited, choose quantization or cloud inference first.
- Context strategy: 128K context is suitable for long documents/long codebases, and the prompt project should be combined with retrieval enhancement (RAG) to reduce invalid context injection.
- Evaluation and grayscale: first use small sample benchmarks (code, search, long article summary) to do A/B, set thresholds for speed and cost, and then expand to production.
4. Typical application scenarios
- Long document understanding and compliance summary: one-time context loading and segmented reasoning of contracts/annual reports/technical white papers.
- Code agent: read, write and reconstruct large code bases, combined with tool calls and test frameworks.
- Enterprise search and knowledge assistant: Combined with vector retrieval/RAG, it uses longer context to do cross-database summary and evidence chain answers.
5. Risks and boundaries
- Unclear license: Currently, there is no clear license field, and commercial use is strictly prohibited by default. Wait for official model card and license updates.
- Computing power and cost: MoE-level models still have significant memory/bandwidth requirements; Evaluate TCO and throughput before deciding on the scale of the landing.
- Data compliance: Sensitive data is easy to carry in long contexts, requiring desensitization, grading, and access control, and configuring log and expiration erasure policies.
6. FAQ
- Is V3.1 really "open source"?
Currently, the weights can be downloaded on Hugging Face, which is published in "open weights"; However, the model card is not yet available, the license is not marked, and the strict open source/commercial boundary must be subject to the official license.
- Where can I experience the online version and call the API?
DeepSeek's official website provides Web/App/Mini Programs and APIs, and the official announcement says that it has been upgraded to V3.1, and the API remains compatible.
- What are the main differences from V3?
public information focuses on "longer context (to 128K)" and "engineering optimization and speed experience improvement"; The underlying layer still continues the V3 system, and detailed training and evaluation data need to be supplemented by the official model card.
- How to try it if there are not enough resources?
Priority is given to quantitative weights and cloud inference; Offline deployment allows for a small sample evaluation before deciding whether to invest in distributed inference and high-end GPUs.
