---
license: mit
library_name: transformers
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF
This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-Distill-Llama-8B`](https://huggingface.co./deepseek-ai/DeepSeek-R1-Distill-Llama-8B) 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-Llama-8B) 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-Llama-8B-Q8_0-GGUF --hf-file deepseek-r1-distill-llama-8b-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF --hf-file deepseek-r1-distill-llama-8b-q8_0.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-Llama-8B-Q8_0-GGUF --hf-file deepseek-r1-distill-llama-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF --hf-file deepseek-r1-distill-llama-8b-q8_0.gguf -c 2048
```