metadata

base_model: byroneverson/glm-4-9b-chat-abliterated
language:
  - zh
  - en
library_name: transformers
license: other
license_name: glm-4
license_link: https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/LICENSE
pipeline_tag: text-generation
tags:
  - glm
  - chatglm
  - thudm
  - llama-cpp
  - gguf-my-repo
  - chat
  - abliterated

byroneverson/glm-4-9b-chat-abliterated-gguf

Version 1.1 (Updated 9/1/2024): Layer 16 is used for abliteration instead of 20. Refusal mitigation tends to work better with this layer. PCA and cosine similarity tests seem to agree.

This model was converted to GGUF format from byroneverson/glm-4-9b-chat-abliterated 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 byroneverson/glm-4-9b-chat-abliterated-Q4_K_M-GGUF --hf-file glm-4-9b-chat-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo byroneverson/glm-4-9b-chat-abliterated-Q4_K_M-GGUF --hf-file glm-4-9b-chat-abliterated-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 byroneverson/glm-4-9b-chat-abliterated-Q4_K_M-GGUF --hf-file glm-4-9b-chat-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo byroneverson/glm-4-9b-chat-abliterated-Q4_K_M-GGUF --hf-file glm-4-9b-chat-abliterated-q4_k_m.gguf -c 2048