---
license: apache-2.0
base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
tags:
- math
- reasoning
- qwen3
- unsloth
- fine-tuned
datasets:
- openai/gsm8k
- lighteval/MATH
model-index:
- name: Shadow-V2
  results:
  - task:
      type: math-reasoning
    dataset:
      name: GSM8K
      type: openai/gsm8k
    metrics:
    - name: accuracy
      type: accuracy
      value: TBD
---

# 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

```python
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))