DeepSeek-R1-Distill-Llama-8B-abliterated-Q4_K_M-GGUF

cstr/DeepSeek-R1-Distill-Llama-8B-abliterated-Q4_K_M-GGUF

This model was converted to GGUF format from huihui-ai/DeepSeek-R1-Distill-Llama-8B-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 cstr/DeepSeek-R1-Distill-Llama-8B-abliterated-Q4_K_M-GGUF --hf-file deepseek-r1-distill-llama-8b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo cstr/DeepSeek-R1-Distill-Llama-8B-abliterated-Q4_K_M-GGUF --hf-file deepseek-r1-distill-llama-8b-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 cstr/DeepSeek-R1-Distill-Llama-8B-abliterated-Q4_K_M-GGUF --hf-file deepseek-r1-distill-llama-8b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo cstr/DeepSeek-R1-Distill-Llama-8B-abliterated-Q4_K_M-GGUF --hf-file deepseek-r1-distill-llama-8b-abliterated-q4_k_m.gguf -c 2048

Provenance and EU AI Act Art. 53 note

• Upstream model: huihui-ai/DeepSeek-R1-Distill-Llama-8B-abliterated — published by huihui-ai.
• Upstream licence: not declared in this repository’s metadata; see the upstream model card. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF, INT4 precision). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.