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---
license: mit
library_name: transformers
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/DeepSeek-R1-Distill-Qwen-14B-Q5_K_M-GGUF
This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for more details on the model.
---
Model details:
-
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.
NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/DeepSeek-R1-Distill-Qwen-14B-Q5_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/DeepSeek-R1-Distill-Qwen-14B-Q5_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-q5_k_m.gguf -c 2048
```
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.
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-Distill-Qwen-14B-Q5_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/DeepSeek-R1-Distill-Qwen-14B-Q5_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-q5_k_m.gguf -c 2048
```
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