Update README.md

README.md

@@ -1,52 +1,112 @@
 ---
-library_name: transformers
-license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE
-pipeline_tag: text-generation
+license: other
 base_model: Qwen/Qwen3-4B-Instruct-2507
 tags:
-- llama-cpp
+- qwen
+- qwen3
+- fine-tuned
+- safety
+- instruct
+- axion
 ---
 
 # AdvRahul/Axion-4B
-This model is finetuned from Qwen/Qwen3-4B-Instruct-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)
-
-```bash
-brew install llama.cpp
-
-```
-Invoke the llama.cpp server or the CLI.
-
-### CLI:
-```bash
-llama-cli --hf-repo AdvRahul/Axion-4B-Q4_K_M-GGUF --hf-file Axion-4B-Q4_K_M.gguf -p "The meaning to life and the universe is"
-```
-
-### Server:
-```bash
-llama-server --hf-repo AdvRahul/Axion-4B-Q4_K_M-GGUF --hf-file Axion-4B-Q4_K_M.gguf -c 2048
-```
-
-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 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 AdvRahul/Axion-4B-Q4_K_M-GGUF --hf-file Axion-4B-Q4_K_M.gguf -p "The meaning to life and the universe is"
-```
-or 
-```
-./llama-server --hf-repo AdvRahul/Axion-4B-Q4_K_M-GGUF --hf-file Axion-4B-Q4_K_M.gguf -c 2048
-```
+
+**A safety-enhanced version of Qwen3-4B-Instruct, optimized for reliable and responsible AI applications. 🛡️**
+
+`Axion-4B` is a fine-tuned version of the powerful `Qwen/Qwen3-4B-Instruct-2507` model. The primary enhancement in this version is its **robust safety alignment**, making it a more dependable choice for production environments and user-facing applications.
+
+## 🚀 Model Details
+
+* **Model Creator:** AdvRahul
+* **Base Model:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+* **Fine-tuning Focus:** Enhanced Safety & Harmlessness via Red-Teaming
+* **Architecture:** Qwen3
+* **Context Length:** 262,144 tokens
+* **License:** Based on the Tongyi Qianwen LICENSE of the original model.
+
+---
+
+## 📝 Model Description
+
+### Enhanced for Safety
+
+The core purpose of `Axion-4B` is to provide a safer alternative for developers. The base model underwent extensive **red-team testing using advanced protocols** to significantly minimize the generation of harmful, biased, or inappropriate content.
+
+### Powerful Core Capabilities
+
+While adding a crucial safety layer, `Axion-4B` retains the exceptional capabilities of its base model, including:
+* **Strong Logical Reasoning:** Excels at complex problems in math, science, and logic.
+* **Advanced Instruction Following:** Reliably adheres to user commands and constraints.
+* **Multi-lingual Knowledge:** Covers a wide range of languages and cultural contexts.
+* **Massive 256K Context Window:** Capable of understanding and processing very long documents.
+* **Excellent Coding & Tool Use:** Proficient in code generation and agentic tasks.
+
+---
+
+## 💻 Quickstart
+
+You can use this model directly with the `transformers` library (version 4.51.0 or newer is recommended).
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# IMPORTANT: Use the model name for this repository
+model_name = "AdvRahul/Axion-4B"
+
+# Load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Prepare the model input
+prompt = "Give me a short introduction to large language models and their safety considerations."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# Generate text
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512 # Limiting for a concise example
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+print("Response:", content)
+
+Optimized Deployment
+For high-throughput, production-ready deployment, you can use frameworks like vLLM or SGLang to serve the model via an OpenAI-compatible API.
+
+vLLM:
+
+Bash
+
+vllm serve AdvRahul/Axion-4B --max-model-len 262144
+SGLang:
+
+Bash
+
+python -m sglang.launch_server --model-path AdvRahul/Axion-4B --context-length 262144
+Note: If you encounter out-of-memory (OOM) issues, consider reducing the max context length (e.g., --max-model-len 32768).
+
+⚠️ Ethical Considerations and Limitations
+This model was fine-tuned with the explicit goal of improving safety and reducing harmful outputs. However, no AI model is completely immune to risks.
+
+No Guarantees: While the safety alignment is significantly improved, it does not guarantee perfectly harmless outputs in all scenarios.
+
+Inherited Biases: The model may still reflect biases present in the vast amount of data used to train its base model.
+
+Factual Accuracy: Always fact-check critical information, as the model can generate plausible but incorrect statements.
+
+Best Practice: It is strongly recommended that developers implement their own content moderation filters and safety guardrails as part of a comprehensive, defense-in-depth strategy. Thoroughly evaluate the model's performance and safety for your specific use case before deploying it to a live audience.