Nulia/Llama-3.1-8B-Lexi-Uncensored-V2-IQ4_XS-GGUF
This model was converted to GGUF format from Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
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 Nulia/Llama-3.1-8B-Lexi-Uncensored-V2-IQ4_XS-GGUF --hf-file llama-3.1-8b-lexi-uncensored-v2-iq4_xs-imat.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Nulia/Llama-3.1-8B-Lexi-Uncensored-V2-IQ4_XS-GGUF --hf-file llama-3.1-8b-lexi-uncensored-v2-iq4_xs-imat.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 Nulia/Llama-3.1-8B-Lexi-Uncensored-V2-IQ4_XS-GGUF --hf-file llama-3.1-8b-lexi-uncensored-v2-iq4_xs-imat.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Nulia/Llama-3.1-8B-Lexi-Uncensored-V2-IQ4_XS-GGUF --hf-file llama-3.1-8b-lexi-uncensored-v2-iq4_xs-imat.gguf -c 2048
- Downloads last month
- 10
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for Nulia/Llama-3.1-8B-Lexi-Uncensored-V2-IQ4_XS-GGUF
Base model
Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard77.920
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard29.690
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard16.920
- acc_norm on GPQA (0-shot)Open LLM Leaderboard4.360
- acc_norm on MuSR (0-shot)Open LLM Leaderboard7.770
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard30.900