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jahnavidanda02
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Model Details
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jahnavidanda02
Model Tree
Qwen/Qwen3-1.7B
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text
Perception Failure | Prediction Failure | Lane Keeping | Braking Behavior |Unwanted Maneuver | Construction/Environment | Precautionary |System/Hardware Fault | Localization/Mapping | OtherQwen/Qwen3-1.7B, loaded in 4-bit (QLoRA-style) for training.q_proj/v_proj.src/finetune_qwen_lora.py.Evaluated against a 250-row hand-labeled gold set (not seen during training, and not derived from the heuristic labels):
| Model | Accuracy (n=250) |
|---|---|
| Heuristic baseline | 80.0% |
| DistilBERT | 80.0% |
| Qwen3-1.7B LoRA (this model) | 72.8% |
Full breakdown: src/eval_qwen_lora_gold.py.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-1.7B", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)model = PeftModel.from_pretrained(base, "jahnavidanda02/drivesignal-qwen3-lora")tokenizer = AutoTokenizer.from_pretrained("jahnavidanda02/drivesignal-qwen3-lora") taxonomy = ["Perception Failure", "Prediction Failure", "Lane Keeping", "Braking Behavior", "Unwanted Maneuver", "Construction/Environment", "Precautionary", "System/Hardware Fault", "Localization/Mapping", "Other"]prompt = (f"Classify this AV disengagement into one category from: {', '.join(taxonomy)}.\n\n" "Description: Vehicle disengaged after hesitating at an unprotected left turn.\n\nCategory:")inputs = tokenizer(prompt, return_tensors="pt")out = model.generate(**inputs, max_new_tokens=15, do_sample=False)print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))eval_qwen_lora_gold.py).Perception Failure | Prediction Failure | Lane Keeping | Braking Behavior |Unwanted Maneuver | Construction/Environment | Precautionary |System/Hardware Fault | Localization/Mapping | Otherimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-1.7B", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)model = PeftModel.from_pretrained(base, "jahnavidanda02/drivesignal-qwen3-lora")tokenizer = AutoTokenizer.from_pretrained("jahnavidanda02/drivesignal-qwen3-lora") taxonomy = ["Perception Failure", "Prediction Failure", "Lane Keeping", "Braking Behavior", "Unwanted Maneuver", "Construction/Environment", "Precautionary", "System/Hardware Fault", "Localization/Mapping", "Other"]prompt = (f"Classify this AV disengagement into one category from: {', '.join(taxonomy)}.\n\n" "Description: Vehicle disengaged after hesitating at an unprotected left turn.\n\nCategory:")inputs = tokenizer(prompt, return_tensors="pt")out = model.generate(**inputs, max_new_tokens=15, do_sample=False)print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))