Write proper model card with usage, training details, and results

README.md

@@ -2,208 +2,170 @@
 base_model: Qwen/Qwen3-0.6B
 library_name: peft
 pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
 tags:
-- base_model:adapter:Qwen/Qwen3-0.6B
 - lora
 - sft
-- transformers
 - trl
+- transformers
+- p5js
+- physics
+- education
+- k-12
+- code-generation
+- animation
+datasets:
+- custom
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+# Qwen3-0.6B LoRA for p5.js Physics Animations
+
+A LoRA adapter for [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) fine-tuned to generate interactive [p5.js](https://p5js.org/) animations that teach K-12 students physics and science concepts.
+
+Given a natural language prompt like *"Show me how gravity affects falling objects"*, the model outputs complete, runnable p5.js code with animations, labels, and interactivity.
+
+## Key Results
+
+| Metric | Value |
+|---|---|
+| Training examples | 1,036 |
+| Unique topics | 124 |
+| Training time | 2.9 min (4x A100-80GB) |
+| Train loss | 0.909 &rarr; 0.495 |
+| Eval loss | 0.616 |
+| Token accuracy | 85.6% |
+| Trainable params | 40.4M / 792M (5.1%) |
+
+## How to Use
+
+### With PEFT + Transformers
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-0.6B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(base_model, "mr-dee/qwen3-p5js-physics-lora")
+tokenizer = AutoTokenizer.from_pretrained("mr-dee/qwen3-p5js-physics-lora")
+
+prompt = """<|im_start|>system
+You are a p5.js animation expert for K-12 physics education. Generate complete, working p5.js code that creates educational animations.
+<|im_end|>
+<|im_start|>user
+Create a p5.js animation showing projectile motion with adjustable launch angle
+<|im_end|>
+<|im_start|>assistant
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, top_p=0.9)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+### With vLLM (recommended for serving)
+
+```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 2048
+```
+
+Then query via the OpenAI-compatible API:
+
+```bash
+curl http://localhost: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 patterns"}
+    ],
+    "max_tokens": 2048,
+    "temperature": 0.7
+  }'
+```
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
+### Dataset
 
-[More Information Needed]
+1,036 synthetic instruction-code pairs generated using parallel Claude agents across 124 K-12 science topics:
 
-## Citation [optional]
+- **Physics**: gravity, Newton's laws, projectile motion, pendulums, momentum, friction, centripetal force
+- **Waves & Optics**: wave interference, double slit experiment, reflection, refraction, Doppler effect
+- **Electricity & Magnetism**: circuit diagrams, electromagnetic induction, Faraday's law
+- **Astronomy**: orbital mechanics, Kepler's laws, stellar lifecycle, tidal forces
+- **Chemistry**: atomic structure, gas laws, chemical bonding, phase transitions
+- **Biology**: photosynthesis, cell division, DNA replication, ecosystem dynamics
+- **Earth Science**: plate tectonics, volcano lifecycle, water cycle, rock cycle
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Each example contains a natural language instruction and complete p5.js code using a 600x400 canvas with `setup()`/`draw()`, text labels, and smooth animations.
 
-**BibTeX:**
+### LoRA Configuration
 
-[More Information Needed]
+| Parameter | Value |
+|---|---|
+| Rank (r) | 64 |
+| Alpha | 128 |
+| Dropout | 0.05 |
+| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Trainable parameters | 40.4M (5.1% of 792M) |
 
-**APA:**
+### Hyperparameters
 
-[More Information Needed]
+| Parameter | Value |
+|---|---|
+| Epochs | 3 |
+| Effective batch size | 32 (4/device x 2 grad_accum x 4 GPUs) |
+| Learning rate | 2e-4 |
+| LR scheduler | Cosine with 5% warmup |
+| Optimizer | AdamW (weight decay 0.01) |
+| Max sequence length | 2048 |
+| Precision | bf16 |
+| Gradient checkpointing | Enabled |
 
-## Glossary [optional]
+### Training Loss Progression
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+| Step | Loss | Accuracy |
+|---|---|---|
+| 10 | 0.909 | 77.0% |
+| 30 | 0.621 | 82.3% |
+| 50 | 0.549 | 84.0% |
+| 70 | 0.510 | 84.9% |
+| 93 | 0.495 | 85.6% |
 
-[More Information Needed]
+## Hardware
 
-## More Information [optional]
+- **GPUs**: 4x NVIDIA A100-SXM4-80GB
+- **Training time**: 171.9 seconds (~2.9 minutes)
+- **Throughput**: 17.2 samples/sec, 0.54 steps/sec
+- **Adapter size**: 155 MB
 
-[More Information Needed]
+## Source Code
 
-## Model Card Authors [optional]
+Full training pipeline, dataset generation scripts, and serving code: [github.com/dylanler/qwen3-p5js-physics](https://github.com/dylanler/qwen3-p5js-physics)
 
-[More Information Needed]
+## Limitations
 
-## Model Card Contact
+- Optimized for p5.js code generation only; not a general-purpose code model
+- Best results with physics/science animation prompts matching the training distribution
+- Generated code may occasionally have minor bugs requiring manual fixes
+- Small base model (0.6B) limits complexity of generated animations compared to larger models
 
-[More Information Needed]
-### Framework versions
+## Framework Versions
 
-- PEFT 0.18.1
+- **PEFT**: 0.18.1
+- **Transformers**: 4.57.6
+- **TRL**: 0.27.2
+- **PyTorch**: 2.9.1
+- **Accelerate**: 1.12+