metadata
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)
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