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--- |
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license: mit |
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library_name: transformers |
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
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tags: |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF |
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This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) for more details on the model. |
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--- |
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Model details: |
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- |
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We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. |
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NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section. |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF --hf-file deepseek-r1-distill-qwen-7b-q4_k_s.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF --hf-file deepseek-r1-distill-qwen-7b-q4_k_s.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF --hf-file deepseek-r1-distill-qwen-7b-q4_k_s.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF --hf-file deepseek-r1-distill-qwen-7b-q4_k_s.gguf -c 2048 |
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``` |
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