How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED:# Run inference directly in the terminal:
llama cli -hf ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED:# Run inference directly in the terminal:
./llama-cli -hf ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED:# Run inference directly in the terminal:
./build/bin/llama-cli -hf ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED:Use Docker
docker model run hf.co/ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED:Quick Links
ArRENCE AI
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Available GGUF Quantizations
These are ready-to-use quantized versions for llama.cpp, Ollama, LM Studio, etc.
| Quant | File | Size | Notes |
|---|---|---|---|
| Q4_K_M | DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED-Q4_K_M.gguf | ~1.1 GB | Recommended balance |
| Q5_K_M | DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED-Q5_K_M.gguf | ~1.29 GB | Higher quality |
| Q6_K | DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED -Q6_K.gguf | ~1.5 GB | Near-original quality |
DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED
This model was abliterated using the aggressive method via
OBLITERATUS.
| Detail | Value |
|---|---|
| Base model | deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
| Method | aggressive |
| Source | obliterate |
How to Use
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED")
tokenizer = AutoTokenizer.from_pretrained("DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED")
prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
About OBLITERATUS
OBLITERATUS is an open-source tool for removing refusal behavior from language models via activation engineering (abliteration). Learn more at github.com/elder-plinius/OBLITERATUS.
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Base model
deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED:# Run inference directly in the terminal: llama cli -hf ArRENCEAI/DeepSeek-R1-Distill-Qwen-1.5B-OBLITERATED: