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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- vihangd/DopeyTinyLlama-1.1B-v1
- gardner/TinyLlama-1.1B-Instruct-3T
- llama-cpp
- gguf-my-repo
base_model: aipib/Tinyllama-moe4
---
# israellaguan/Tinyllama-moe4-Q8_0-GGUF
This model was converted to GGUF format from [`aipib/Tinyllama-moe4`](https://huggingface.co./aipib/Tinyllama-moe4) 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./aipib/Tinyllama-moe4) for more details on the model.
## 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 --hf-repo israellaguan/Tinyllama-moe4-Q8_0-GGUF --hf-file tinyllama-moe4-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo israellaguan/Tinyllama-moe4-Q8_0-GGUF --hf-file tinyllama-moe4-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.
```
./main --hf-repo israellaguan/Tinyllama-moe4-Q8_0-GGUF --hf-file tinyllama-moe4-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./server --hf-repo israellaguan/Tinyllama-moe4-Q8_0-GGUF --hf-file tinyllama-moe4-q8_0.gguf -c 2048
```
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