Triangle104/DeepSeek-R1-0528-Qwen3-8B-Q5_K_M-GGUF

This model was converted to GGUF format from deepseek-ai/DeepSeek-R1-0528-Qwen3-8B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro.

Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.

Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/DeepSeek-R1-0528-Qwen3-8B-Q5_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/DeepSeek-R1-0528-Qwen3-8B-Q5_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q5_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/DeepSeek-R1-0528-Qwen3-8B-Q5_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/DeepSeek-R1-0528-Qwen3-8B-Q5_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q5_k_m.gguf -c 2048