Update README.md

README.md

@@ -1,44 +1,61 @@
+---
 library_name: transformers
 license: apache-2.0
 license_link: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507/blob/main/LICENSE
 pipeline_tag: text-generation
 base_model: Qwen/Qwen3-4B-Thinking-2507
 tags:
+- llama-cpp
+- gguf-my-repo
+---
 
-llama-cpp
+# AdvRahul/Axion-Thinking-4B
 
-gguf-my-repo
+This model is finetuned from [`Qwen/Qwen3-4B-Thinking-2507`](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507), making it safer through red team testing with advanced protocols.
 
-AdvRahul/Axion-Thinking-4B
-This model is finetuned from [Qwen/Qwen3-4B-Thinking-2507] making it safer by red team testing with advanced protocols.
+## Use with llama.cpp
+
+Install llama.cpp through brew (works on Mac and Linux):
 
-Use with llama.cpp
-Install llama.cpp through brew (works on Mac and Linux)
 
-bash
 brew install llama.cpp
+
+
 Invoke the llama.cpp server or the CLI.
 
-CLI:
-bash
+### 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:
-bash
+
+
+### 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 as well.
+
+
+Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo.
+
 Step 1: Clone llama.cpp from GitHub.
 
-text
-git clone [https://github.com/ggerganov/llama.cpp](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).
+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).
 
-text
 cd llama.cpp && LLAMA_CURL=1 make
+
+
+
 Step 3: Run inference through the main binary.
 
-text
 ./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
 
-text
-./llama-server --hf-repo AdvRahul/Axion-Thinking-4B-Q4_K_M-GGUF --hf-file axion-thinking-4b-q4_k_m.gguf -c 2048
+./llama-server --hf-repo AdvRahul/Axion-Thinking-4B-Q4_K_M-GGUF --hf-file axion-thinking-4b-q4_k_m.gguf -c 2048
+
+---
+
+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!