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AbijahKaj
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Model Details
Model Provider
AbijahKaj
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "AbijahKaj/qwen3-4b-skidl", dtype="auto", device_map="auto",)tokenizer = AutoTokenizer.from_pretrained("AbijahKaj/qwen3-4b-skidl")python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B", dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("AbijahKaj/qwen3-4b-skidl", revision="lora")model = PeftModel.from_pretrained(base, "AbijahKaj/qwen3-4b-skidl", revision="lora")python
SYSTEM = ( "You are an expert electronics engineer and KiCad schematic designer. " "When given a description of an electronic circuit, generate executable " "SKiDL Python code that defines the circuit using the SKiDL library.") messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "Design a CAN bus interface using MCP2515 controller and TJA1050 transceiver on SPI, with 120 ohm termination resistor."},]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))| Base model | Qwen/Qwen3-4B |
| Method | SFT + LoRA |
| Dataset | AbijahKaj/kicad-netlist-sft-dataset — 100,179 SKiDL Python circuit examples |
| LoRA rank / alpha | r=64, α=32, dropout=0.05 |
| Target modules | q/k/v/o_proj, gate/up/down_proj |
| Epochs | 2 |
| Peak LR | 2e-4 (cosine decay) |
| Effective batch | 8 |
| Max length | 8192 tokens |
| Trainable params | ~132M (5.65% of 4B) |
| Phase | Train loss | Token accuracy |
|---|---|---|
| Start | 0.86 | 75% |
| Epoch 1 end | 0.17 | 95% |
| Epoch 2 end | 0.15 | 95–96% |
| Eval loss (final) | 0.1581 | 95.3% |
Functional scoring across 5 held-out circuits: Python syntax + SKiDL import + net count + part count + connectivity + GND checks.
| Circuit | Score |
|---|---|
| LED blink — ATtiny85 | 0.85 |
| USB power meter — ATmega328P + INA219 | 0.85 |
| CAN bus — MCP2515 + TJA1050 | 0.95 |
| Average | 0.883 |
KiCad's native s-expression netlist is deeply nested and pathological for LLMs. PCBSchemaGen (arXiv:2602.00510) shows SKiDL Python achieves 87% Pass@1 with GPT-4o on circuit generation — because Python-based HDLs align with LLM pretraining data and are 3× more compact than s-expressions.
AbijahKaj/kicad-netlist-sft-dataset — 100,179 ChatML examples:
| Source | Count |
|---|---|
| Si7li/ltspice-spice-circuits (LTspice → SKiDL) | ~54,000 |
bshada/open-schematics (GitHub .kicad_sch files) | ~45,000 |
| Ashed00/SPICE-Circuits | ~800 |
| Synthetic + tool-augmented | ~308 |
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "AbijahKaj/qwen3-4b-skidl", dtype="auto", device_map="auto",)tokenizer = AutoTokenizer.from_pretrained("AbijahKaj/qwen3-4b-skidl")from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B", dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("AbijahKaj/qwen3-4b-skidl", revision="lora")model = PeftModel.from_pretrained(base, "AbijahKaj/qwen3-4b-skidl", revision="lora")SYSTEM = ( "You are an expert electronics engineer and KiCad schematic designer. " "When given a description of an electronic circuit, generate executable " "SKiDL Python code that defines the circuit using the SKiDL library.") messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "Design a CAN bus interface using MCP2515 controller and TJA1050 transceiver on SPI, with 120 ohm termination resistor."},]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))