base_model: carsenk/llama3.2_1b_2025_uncensored_v2
datasets:
- mlabonne/FineTome-100k
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- PawanKrd/math-gpt-4o-200k
- V3N0M/Jenna-50K-Alpaca-Uncensored
- FreedomIntelligence/medical-o1-reasoning-SFT
language: - en
library_name: transformers
license: llama3.2
pipeline_tag: text-generation
tags: - llama
- unsloth
- uncensored
- llama-3.2
- llama.cpp
- gguf
- inference
- llama-cpp
- gguf-my-repo
bulieme/llama3.2_1b_2025_uncensored_v2-Q3_K_M-GGUF
This model was converted to GGUF format from carsenk/llama3.2_1b_2025_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 bulieme/llama3.2_1b_2025_uncensored_v2-Q3_K_M-GGUF --hf-file llama3.2_1b_2025_uncensored_v2-q3_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo bulieme/llama3.2_1b_2025_uncensored_v2-Q3_K_M-GGUF --hf-file llama3.2_1b_2025_uncensored_v2-q3_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 bulieme/llama3.2_1b_2025_uncensored_v2-Q3_K_M-GGUF --hf-file llama3.2_1b_2025_uncensored_v2-q3_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo bulieme/llama3.2_1b_2025_uncensored_v2-Q3_K_M-GGUF --hf-file llama3.2_1b_2025_uncensored_v2-q3_k_m.gguf -c 2048