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Model: zannvznn/qwen3-0.6b-math-l45-qlora-merged-fp16
Source: Original Platform

README.md

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+---
+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)
+```