---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen3
- math
- sft
- qlora
- merged-lora
---

# qwen3-0.6b-math-l45-qlora-merged-fp16

This is a merged fp16 model created from `Qwen/Qwen3-0.6B-Base` plus a QLoRA adapter trained on math level 4-5 data.

## Source

- Base model: `Qwen/Qwen3-0.6B-Base`
- Source adapter: `final adapter`
- Merge method: manual LoRA merge, `W_merged = W_base + (B @ A) * scaling`

## Prompt Format

```text
### System:
{system_prompt}

### Problem:
{problem}

### Solution:

```

System prompt:

```text
You are a precise mathematical problem solver.
Follow this exact output contract:
1. Solve the problem step by step with concise reasoning.
2. Use valid LaTeX math notation for mathematical expressions.
3. Preserve LaTeX commands such as \frac{...}{...}, \sqrt{...}, x^{...}, subscripts, equations, and inequalities.
4. Put the final answer on its own last line exactly in this form: Final Answer: \boxed{...}
5. Do not use Markdown code fences.
6. Do not switch to a different answer format.
```

## Usage

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

repo_id = "zannvznn/qwen3-0.6b-math-l45-qlora-merged-fp16"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True)
```