Aashraf995/SauerkrautLM-v2-14b-DPO-Q4_K_M-GGUF
This model was converted to GGUF format from VAGOsolutions/SauerkrautLM-v2-14b-DPO
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 Aashraf995/SauerkrautLM-v2-14b-DPO-Q4_K_M-GGUF --hf-file sauerkrautlm-v2-14b-dpo-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Aashraf995/SauerkrautLM-v2-14b-DPO-Q4_K_M-GGUF --hf-file sauerkrautlm-v2-14b-dpo-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 Aashraf995/SauerkrautLM-v2-14b-DPO-Q4_K_M-GGUF --hf-file sauerkrautlm-v2-14b-dpo-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Aashraf995/SauerkrautLM-v2-14b-DPO-Q4_K_M-GGUF --hf-file sauerkrautlm-v2-14b-dpo-q4_k_m.gguf -c 2048
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Model tree for Aashraf995/SauerkrautLM-v2-14b-DPO-Q4_K_M-GGUF
Base model
Qwen/Qwen2.5-14B
Finetuned
VAGOsolutions/SauerkrautLM-v2-14b-SFT
Finetuned
VAGOsolutions/SauerkrautLM-v2-14b-DPO
Datasets used to train Aashraf995/SauerkrautLM-v2-14b-DPO-Q4_K_M-GGUF
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard74.120
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard50.930
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard27.340
- acc_norm on GPQA (0-shot)Open LLM Leaderboard9.280
- acc_norm on MuSR (0-shot)Open LLM Leaderboard13.780
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard45.750