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mr-dee
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Model Details
Model Provider
mr-dee
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-0.6Bmr-dee/qwen3-p5js-physics-loraDirect use:
Downstream use:
Out of scope:
Dataset summary:
Each example contains:
instructiontopicgrade_levelp5js_code (full runnable sketch)Data style constraints emphasized:
setup() and draw() structureHardware and runtime:
accelerateLoRA config:
r=64lora_alpha=128lora_dropout=0.05q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_projOptimization config:
2e-40.9090 -> 0.4950 (step 10 to step 90)train_metrics.json): 0.59170.6164These are training-time indicators, not full benchmark performance across external test sets.
python
import torchfrom peft import PeftModelfrom 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))bash
vllm serve Qwen/Qwen3-0.6B \ --enable-lora \ --lora-modules p5js=mr-dee/qwen3-p5js-physics-lora \ --tensor-parallel-size 2 \ --max-model-len 2048Then query:
bash
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:
prompt_tokens + max_tokens <= max_model_lenIf you use this adapter, cite:
bibtex
@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}}import torchfrom peft import PeftModelfrom 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 serve Qwen/Qwen3-0.6B \ --enable-lora \ --lora-modules p5js=mr-dee/qwen3-p5js-physics-lora \ --tensor-parallel-size 2 \ --max-model-len 2048curl 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 }'@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}}