Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3-0.6B
+library_name: transformers
+tags:
+- unsloth
+- reasoning
+- code
+- chain-of-thought
+- text-generation
+- shadow
+- conversational
+datasets:
+- unsloth/gsm8k
+- deepseek-ai/DeepSeek-R1
+pipeline_tag: text-generation
+---
+
+# 🌑 Shadow 0.7B (Reasoning Edition)
+
+**Shadow 0.7B** is a specialized Small Language Model (SLM) optimized for **logical reasoning, competitive coding, and chain-of-thought processing**. 
+
+Built on the Qwen architecture and fine-tuned using **Unsloth**, Shadow punches far above its weight class, delivering "thinking" capabilities usually found in much larger models.
+
+## 🚀 Key Features
+* **🧠 Native Reasoning:** Trained to use `<think>` tags to plan and verify logic before answering.
+* **💻 Code Expert:** Optimized for Python and C++ algorithmic solutions (Chain of Draft).
+* **⚡ Lightweight:** Runs comfortably on free T4 GPUs, CPUs, and mobile devices (via Ollama).
+* **🆔 Custom Persona:** Maintains the identity of "Shadow", created by **Aman Kumar Pandey**.
+
+## 💻 Quick Start (Python)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Redhanuman/Shadow-0.7B-Qwen3-Reasoning" # Replace with your actual username/repo
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Shadow works best when you ask it to think
+prompt = "Write a Python script to check for palindromes. Explain your logic."
+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)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024
+)
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+🦙 Run Locally (Ollama)
+If you have converted this model to GGUF, you can run it locally:
+
+Bash
+
+ollama run shadow
+🛠️ Training Details
+Creator: Aman Kumar Pandey (LPU)
+
+Framework: Unsloth (2x Faster Training)
+
+Base Model: Qwen 2.5 0.5B Instruct
+
+Method: QLoRA Fine-tuning with Chain of Draft (CoD) data.
+
+Created with ❤️ by Aman Kumar Pandey.
+
+
+### 📝 Instructions:
+1.  Go to your Model Page on Hugging Face.
+2.  Click **"Update model card"** (or create `README.md`).
+3.  **Delete everything** currently there.
+4.  **Paste** the code above.
+5.  **Important:** In the Python code section, make sure `Redhanuman/Shadow-0.7B-Qwen3-Reasoning` matches your *exact* repo name.
+6.  Click **Commit changes**.
+
+
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+library_name: transformers
+tags:
+  - unsloth
+  - reasoning
+  - code
+  - chain-of-thought
+  - text-generation
+  - shadow
+  - conversational
+datasets:
+  - unsloth/gsm8k
+  - deepseek-ai/DeepSeek-R1
+pipeline_tag: text-generation
+---
+
+# 🌑 Shadow 0.7B (Reasoning Edition)
+
+**Shadow 0.7B** is a specialized Small Language Model (SLM) optimized for **logical reasoning, competitive coding, and chain-of-thought processing**. 
+
+Built on the Qwen architecture and fine-tuned using **Unsloth**, Shadow punches far above its weight class, delivering "thinking" capabilities usually found in much larger models.
+
+## 🚀 Key Features
+* **🧠 Native Reasoning:** Trained to use `<think>` tags to plan and verify logic before answering.
+* **💻 Code Expert:** Optimized for Python and C++ algorithmic solutions (Chain of Draft).
+* **⚡ Lightweight:** Runs comfortably on free T4 GPUs, CPUs, and mobile devices (via Ollama).
+* **🆔 Custom Persona:** Maintains the identity of "Shadow", created by **Aman Kumar Pandey**.
+
+## 💻 Quick Start (Python)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Redhanuman/Shadow-0.7B-Qwen3-Reasoning" # Replace with your actual username/repo
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Shadow works best when you ask it to think
+prompt = "Write a Python script to check for palindromes. Explain your logic."
+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)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024
+)
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+🦙 Run Locally (Ollama)
+If you have converted this model to GGUF, you can run it locally:
+
+Bash
+
+ollama run shadow
+🛠️ Training Details
+Creator: Aman Kumar Pandey (LPU)
+
+Framework: Unsloth (2x Faster Training)
+
+Base Model: Qwen 2.5 0.5B Instruct
+
+Method: QLoRA Fine-tuning with Chain of Draft (CoD) data.
+
+Created with ❤️ by Aman Kumar Pandey.
+
+
+### 📝 Instructions:
+1.  Go to your Model Page on Hugging Face.
+2.  Click **"Update model card"** (or create `README.md`).
+3.  **Delete everything** currently there.
+4.  **Paste** the code above.
+5.  **Important:** In the Python code section, make sure `Redhanuman/Shadow-0.7B-Qwen3-Reasoning` matches your *exact* repo name.
+6.  Click **Commit changes**.