Shadow-V2

Fine-tuned Qwen3-0.6B for mathematical reasoning.

Model Details

Property	Value
Base Model	Qwen3-0.6B
Parameters	636M total, 40M trainable (6.34%)
Precision	BF16
Training Method	LoRA via Unsloth
Context Length	2048

Training

Config	Value
Dataset	25,000 examples
Epochs	1
Batch Size	16 (2 × 8 accum)
Steps	1,200
Hardware	Tesla T4 16GB
Time	1.35 hours
Final Loss	0.43

Benchmarks

Benchmark	Shadow-V2	Qwen3-0.6B (base)
GSM8K (5-shot)	TBD	42.3
MATH (4-shot)	TBD	18.2
HumanEval (0-shot)	TBD	28.0

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Redhanuman/Shadow-V2")
tokenizer = AutoTokenizer.from_pretrained("Redhanuman/Shadow-V2")

prompt = "Solve: If 3x + 7 = 22, find x.\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))