TheDrunkenSnail/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF
This model was converted to GGUF format from HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407
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 TheDrunkenSnail/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo TheDrunkenSnail/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF --hf-file human-like-mistral-nemo-instruct-2407-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 TheDrunkenSnail/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo TheDrunkenSnail/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q4_k_m.gguf -c 2048
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Model tree for TheDrunkenSnail/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF
Base model
mistralai/Mistral-Nemo-Base-2407
Finetuned
mistralai/Mistral-Nemo-Instruct-2407
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard54.510
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard32.710
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard7.630
- acc_norm on GPQA (0-shot)Open LLM Leaderboard5.030
- acc_norm on MuSR (0-shot)Open LLM Leaderboard9.400
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard28.010