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randomtravellerai/
mobile-game-test-plans-qwen2.5-coder-7b-lora

Name: mobile-game-test-plans-qwen2.5-coder-7b-lora
Availability: InStock

ml-intern

2411.02462

2402.11910

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Mobile Game End-to-End Test Plan Generator

Fine-tuned Qwen2.5-Coder-7B-Instruct model specialized in writing comprehensive end-to-end test plans and test cases for mobile games.

What it does

Given a mobile game feature description, the model generates professional QA test plans including:

Preconditions
Step-by-step test steps
Expected results
Edge cases and boundary conditions

Training approach

Parameter	Value
Base model	Qwen/Qwen2.5-Coder-7B-Instruct
Method	Supervised Fine-Tuning (SFT) with LoRA
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.1
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Learning rate	3e-4
Epochs	3
Batch size	1 (effective = 8 via gradient accumulation)
Max sequence length	4096
Packing	True
Loss on assistant only	True

Dataset

Size: ~720 training samples, ~80 test samples
Source: Synthetic dataset covering 20 game genres and 15 feature categories
Format: Conversational (messages with user / assistant roles)
Coverage: RPG, Puzzle, Strategy, FPS, MOBA, Battle Royale, Runner, Simulation, Sports, Card Game, Tower Defense, Racing, Fighting, Adventure, Survival, MMORPG, Idle, Tycoon, Rhythm, Sandbox
Feature categories: shop, combat, quest, guild, gacha, accessibility, notification, monetization, customization, level, account, settings, progression, social, bug, localization, privacy, competitive, story

How to use

Training

bash

pip install transformers trl peft accelerate datasets
python train.py

Inference

python

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "randomtravellerai/mobile-game-test-plans-qwen2.5-coder-7b-lora"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "user", "content": "Write an end-to-end test plan for this mobile RPG game feature:\nFeature: Player opens the shop from the main hub, browses weapon skins, previews them on their character, and purchases using in-game currency."}
]

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
result = pipe(messages, max_new_tokens=1024)
print(result[0]["generated_text"])

Research basis

Training recipe adapted from:

Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation (arXiv:2411.02462) — LoRA hyperparameters
Enhancing Large Language Models for Text-to-Testcase Generation (arXiv:2402.11910) — prompt design and task framing

License

Apache-2.0 (matching base model)

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern