Update README.md

README.md

@@ -6,47 +6,11 @@ tags:
 - gguf-my-repo
 base_model: t-tech/T-pro-it-1.0
 ---
-
 # al-x/T-pro-it-1.0-Q3_K_M-GGUF
 This model was converted to GGUF format from [`t-tech/T-pro-it-1.0`](https://huggingface.co/t-tech/T-pro-it-1.0) 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/t-tech/T-pro-it-1.0) 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:
+## Ollama
 ```bash
-llama-cli --hf-repo al-x/T-pro-it-1.0-Q3_K_M-GGUF --hf-file t-pro-it-1.0-q3_k_m.gguf -p "The meaning to life and the universe is"
-```
-
-### Server:
-```bash
-llama-server --hf-repo al-x/T-pro-it-1.0-Q3_K_M-GGUF --hf-file t-pro-it-1.0-q3_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 al-x/T-pro-it-1.0-Q3_K_M-GGUF --hf-file t-pro-it-1.0-q3_k_m.gguf -p "The meaning to life and the universe is"
-```
-or 
-```
-./llama-server --hf-repo al-x/T-pro-it-1.0-Q3_K_M-GGUF --hf-file t-pro-it-1.0-q3_k_m.gguf -c 2048
+ollama run hf.co/al-x/T-pro-it-1.0-Q3_K_M-GGUF:Q3_K_M
 ```