istvanpatai/RoGemma2-9b-Instruct-2024-10-09-Q4_K_M-GGUF
This model was converted to GGUF format from OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09
using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo istvanpatai/RoGemma2-9b-Instruct-2024-10-09-Q4_K_M-GGUF --hf-file rogemma2-9b-instruct-2024-10-09-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo istvanpatai/RoGemma2-9b-Instruct-2024-10-09-Q4_K_M-GGUF --hf-file rogemma2-9b-instruct-2024-10-09-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps 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 istvanpatai/RoGemma2-9b-Instruct-2024-10-09-Q4_K_M-GGUF --hf-file rogemma2-9b-instruct-2024-10-09-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo istvanpatai/RoGemma2-9b-Instruct-2024-10-09-Q4_K_M-GGUF --hf-file rogemma2-9b-instruct-2024-10-09-q4_k_m.gguf -c 2048
- Downloads last month
- 692
Model tree for istvanpatai/RoGemma2-9b-Instruct-2024-10-09-Q4_K_M-GGUF
Base model
google/gemma-2-9b
Finetuned
google/gemma-2-9b-it
Datasets used to train istvanpatai/RoGemma2-9b-Instruct-2024-10-09-Q4_K_M-GGUF
Evaluation results
- Score on RoMT-Benchself-reported6.080
- First turn on RoMT-Benchself-reported6.780
- Second turn on RoMT-Benchself-reported5.390
- Score on RoCulturaBenchself-reported4.200
- Average accuracy on Romanian_Academic_Benchmarksself-reported57.060
- Average accuracy on OpenLLM-Ro/ro_arc_challengeself-reported56.200
- 0-shot on OpenLLM-Ro/ro_arc_challengeself-reported53.300
- 1-shot on OpenLLM-Ro/ro_arc_challengeself-reported54.930
- 3-shot on OpenLLM-Ro/ro_arc_challengeself-reported57.070
- 5-shot on OpenLLM-Ro/ro_arc_challengeself-reported57.330