- Developers & Engineers: Integrate powerful reasoning and coding LLM into production without heavy MoE complexity.
- Researchers & Students: Study high-quality chain-of-thought reasoning in a compact, transparent model.
- Startups & Enterprises: Deploy reasoning-first LLM on consumer-grade hardware with quantization.
- Open-Source Enthusiasts: Modify and tinker freely; MIT license supports innovation and redistribution.
- Benchmarkers: Compare small-but-mighty performance against dense and MoE peers in math and code.
How to Use DeepSeek R1 0528 – Qwen3-8B?
- Load the Model: Access via Hugging Face (`deepseek-ai/DeepSeek-R1-0528-Qwen3-8B`) or inference frameworks like Ollama/vLLM.
- Use Chain-of-Thought Prompts: Provide reasoning prompts at temperature
0.6, top_p0.95 for best clarity. - Support up to 32K Tokens: Handles long math problems, code debugging, logic chains.
- Quantize for Efficiency: Use GGUF / Q4_K formats to run on setups with ~20–40GB RAM or H100-class GPUs.
- Call via API: Also available through DeepSeek API with DeepThink mode enabled.
- 10% Over Qwen3 8B: Surpasses base Qwen3-8B by +10% on AIME 2024 and matches Qwen3-235B for chain-of-thought.
- Efficient Yet Deep: Brings flagship-level reasoning into an 8B model—ideal for limited hardware.
- Easy to Deploy & Customize: MIT license, HuggingFace access, supported by Ollama and LM Studio.
- Exceptional reasoning and coding benchmark scores in a compact model
- Outperforms Qwen3 8B by +10% while rivaling much larger open-source models
- Efficient deployment on modest GPUs with quantization support
- Open-source and permissively licensed—ideal for fine-tuning and extension
- Rich ecosystem support (Hugging Face, Ollama, vLLM, LM Studio)
- No multimodal or MoE capabilities—text-only and limited context window
- May not match full R1‑0528 model on absolute reasoning depth
- Some users report occasional quirks in code generation vs specialized coders
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