Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct-GGUF
This model was converted to GGUF format from Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct
using croco.cpp's fork Croco.cpp via the ggml.ai's GGUF-my-repo space.
This model was converted to GGUF format from Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct
using llama.cpp's fork Croco.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 Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct-GGUF --hf-file meditsolutions_llama-3.2-sun-1b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct-GGUF --hf-file meditsolutions_llama-3.2-sun-1b-instruct-q8_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. -> necessary to use Croco.
Step 1: Clone llama.cpp from GitHub. -> necessary to use Croco.
git clone https://github.com/Nexesenex/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 Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct-GGUF --hf-file meditsolutions_llama-3.2-sun-1b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct-GGUF --hf-file meditsolutions_llama-3.2-sun-1b-instruct-q8_0.gguf -c 2048
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Base model
meta-llama/Llama-3.2-1B-InstructDatasets used to train Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct-GGUF
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard64.130
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard9.180
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard4.610
- acc_norm on GPQA (0-shot)Open LLM Leaderboard0.000
- acc_norm on MuSR (0-shot)Open LLM Leaderboard4.050
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard8.680