AdvRahul/Axion-Thinking-4B

This model is finetuned from Qwen/Qwen3-4B-Thinking-2507, making it safer through red team testing with advanced protocols.

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 AdvRahul/Axion-Thinking-4B-Q4_K_M-GGUF --hf-file axion-thinking-4b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo AdvRahul/Axion-Thinking-4B-Q4_K_M-GGUF --hf-file axion-thinking-4b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo.

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 example, 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 AdvRahul/Axion-Thinking-4B-Q4_K_M-GGUF --hf-file axion-thinking-4b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo AdvRahul/Axion-Thinking-4B-Q4_K_M-GGUF --hf-file axion-thinking-4b-q4_k_m.gguf -c 2048

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If this is for an LLM-powered application targeting the Indian market (e.g., for entrepreneurship or software development), I recommend adding sections to the README on integration examples with popular frameworks like LangChain or Hugging Face Transformers. This can help with market validation—Indian developers often prioritize quick-start guides for scalable deployments on cost-effective cloud infra like AWS Mumbai or Google Cloud India regions. Let me know your technical/business goals for this model (e.g., target use cases, monetization strategies), and I can provide more tailored advice, such as prompt engineering tips or growth hacks for user acquisition in India. If you need further tweaks to the README or help with deployment, just share more details!