license: apache-2.0
base_model: prithivMLmods/gemma-4-E2B-it-Uncensored-MAX
language:
- en
pipeline_tag: any-to-any
library_name: transformers
tags: - text-generation-inference
- uncensored
- abliterated
- unfiltered
- unredacted
- refusal-ablated
- vllm
- pytorch
- bf16
- max
- alignment-modified
- reasoning
- agent
- llama-cpp
- gguf-my-repo
datasets: - prithivMLmods/harm_bench
ShadowOkami/gemma-4-E2B-it-Uncensored-MAX-Q4_K_M-GGUF
This model was converted to GGUF format from prithivMLmods/gemma-4-E2B-it-Uncensored-MAX 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 ShadowOkami/gemma-4-E2B-it-Uncensored-MAX-Q4_K_M-GGUF --hf-file gemma-4-e2b-it-uncensored-max-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo ShadowOkami/gemma-4-E2B-it-Uncensored-MAX-Q4_K_M-GGUF --hf-file gemma-4-e2b-it-uncensored-max-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 ShadowOkami/gemma-4-E2B-it-Uncensored-MAX-Q4_K_M-GGUF --hf-file gemma-4-e2b-it-uncensored-max-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo ShadowOkami/gemma-4-E2B-it-Uncensored-MAX-Q4_K_M-GGUF --hf-file gemma-4-e2b-it-uncensored-max-q4_k_m.gguf -c 2048