Triangle104/Qwen2.5-7B-Instruct-Uncensored-Q8_0-GGUF
This model was converted to GGUF format from Orion-zhen/Qwen2.5-7B-Instruct-Uncensored
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 Triangle104/Qwen2.5-7B-Instruct-Uncensored-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-uncensored-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Qwen2.5-7B-Instruct-Uncensored-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-uncensored-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.
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 Triangle104/Qwen2.5-7B-Instruct-Uncensored-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-uncensored-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Qwen2.5-7B-Instruct-Uncensored-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-uncensored-q8_0.gguf -c 2048
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Model tree for Triangle104/Qwen2.5-7B-Instruct-Uncensored-Q8_0-GGUF
Base model
Qwen/Qwen2.5-7B
Finetuned
Qwen/Qwen2.5-7B-Instruct
Datasets used to train Triangle104/Qwen2.5-7B-Instruct-Uncensored-Q8_0-GGUF
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard72.040
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard35.830
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard1.360
- acc_norm on GPQA (0-shot)Open LLM Leaderboard7.050
- acc_norm on MuSR (0-shot)Open LLM Leaderboard13.580
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard38.070