qwen3-p5js-physics-lora

qwen3-p5js-physics-lora

LoRA adapter for Qwen/Qwen3-0.6B, tuned for generating educational p5.js animations from natural-language prompts.

Primary objective: produce runnable, classroom-friendly JavaScript sketches (setup() + draw()) that explain K-12 science concepts visually.

Model Details

• Base model: Qwen/Qwen3-0.6B
• Fine-tuning method: LoRA + supervised fine-tuning (SFT)
• Domain: p5.js animation code generation for science education
• Intended language: English prompts and code comments
• Adapter repo: mr-dee/qwen3-p5js-physics-lora
• Source project: https://github.com/dylanler/qwen3-p5js-physics

Intended Use

Direct use:

• Generate p5.js teaching demos for topics like gravity, circuits, optics, astronomy, and earth science.
• Bootstrap lesson visuals that teachers/students can edit locally.

Downstream use:

• Interactive educational apps, coding workshops, and science visualization demos.

Out of scope:

• Safety-critical software.
• Scientific simulation requiring high-precision numerical correctness.
• Unreviewed classroom deployment without human validation.

Training Data

Dataset summary:

• 1,036 instruction/code examples.
• 124 unique K-12 science topics.
• Synthetic dataset generated via parallel agent workflows and validated into JSONL format.

Each example contains:

• instruction
• topic
• grade_level
• p5js_code (full runnable sketch)

Data style constraints emphasized:

• 600x400 canvas
• setup() and draw() structure
• labels/annotations for explanation
• interactive and animated behavior

Training Procedure

Hardware and runtime:

• 4x NVIDIA A100-SXM4-80GB
• ~171.9 seconds total training runtime
• multi-GPU training via accelerate

LoRA config:

• r=64
• lora_alpha=128
• lora_dropout=0.05
• target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Optimization config:

• epochs: 3
• per-device batch size: 4
• gradient accumulation: 2
• effective batch size: 32 (4 GPUs)
• learning rate: 2e-4
• scheduler: cosine, warmup ratio 0.05
• weight decay: 0.01
• max sequence length: 2048
• precision: bf16
• optimizer: AdamW

Training/Eval Snapshot

• Train loss (logged): 0.9090 -> 0.4950 (step 10 to step 90)
• Train run average loss (train_metrics.json): 0.5917
• Eval loss (step 50): 0.6164
• Reported token accuracy during run: up to ~85.6%

These are training-time indicators, not full benchmark performance across external test sets.

Quick Start

Transformers + PEFT

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE = "Qwen/Qwen3-0.6B"
ADAPTER = "mr-dee/qwen3-p5js-physics-lora"

tokenizer = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    BASE,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, ADAPTER)

messages = [
    {
        "role": "system",
        "content": (
            "You are a p5.js animation expert for K-12 physics education. "
            "Return complete, runnable JavaScript."
        ),
    },
    {
        "role": "user",
        "content": "Create an interactive p5.js sketch that teaches projectile motion.",
    },
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.4,
    top_p=0.9,
)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

vLLM Serving (LoRA)

vllm serve Qwen/Qwen3-0.6B \
  --enable-lora \
  --lora-modules p5js=mr-dee/qwen3-p5js-physics-lora \
  --tensor-parallel-size 2 \
  --max-model-len 2048

Then query:

curl http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "p5js",
    "messages": [
      {"role":"system","content":"You are a p5.js animation expert for K-12 physics education."},
      {"role":"user","content":"Create an animation showing wave interference with labeled nodes and antinodes."}
    ],
    "max_tokens": 800,
    "temperature": 0.4
  }'

Important:

• keep prompt_tokens + max_tokens <= max_model_len
• otherwise vLLM returns HTTP 400 validation errors

Limitations and Risks

• Output is not guaranteed bug-free JavaScript; review before use.
• Physical explanations may be simplified or occasionally incorrect.
• Performance drops for domains outside training distribution.
• Small base model can struggle with very long or highly complex simulations.

Responsible Use

• Use with human review in educational settings.
• Validate scientific correctness before presenting to students.
• Sandbox or lint generated code before running in production applications.

Framework Versions

• PEFT: 0.18.1
• Transformers: 4.57.6
• TRL: 0.27.2
• PyTorch: 2.9.1
• Accelerate: 1.12+

Citation

If you use this adapter, cite:

@misc{qwen3_p5js_physics_lora_2026,
  title  = {qwen3-p5js-physics-lora},
  author = {mr-dee},
  year   = {2026},
  url    = {https://huggingface.co/mr-dee/qwen3-p5js-physics-lora}
}