---
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 + Coding Edition)

**Shadow 0.7B** is a specialized Small Language Model (SLM) optimized for **logical reasoning, competitive programming, and chain-of-thought processing**.

Built on the **Qwen3 0.6B** architecture and fine-tuned using **Unsloth**, Shadow delivers surprising reasoning depth and "thinking-first" responses uncommon for a model of this size.

---

##  Key Features

* 🧠 **Structured Reasoning:** Uses `<think>` style internal reasoning patterns to improve answer quality.
* 💻 **Coding Specialist:** Excels at Python, C++, and algorithmic problem-solving.
* ⚡ **Ultra-Lightweight:** Runs on CPU, T4, mobile, or even low-VRAM consumer GPUs.
---

## 💻 Quick Start (Python)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "Redhanuman/Shadow-0.7B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

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
)

inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **inputs,
    max_new_tokens=1024
)

print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
## 🛠️ Training Details

- **Creator:** Aman Kumar Pandey (LPU)  
- **Framework:** Unsloth (2× faster training)  
- **Base Model:** Qwen3-0.6B  
- **Method:** QLoRA fine-tuning with Chain-of-Draft (CoD) reasoning data  
- **Datasets:** GSM8K, DeepSeek R1 distilled reasoning samples