Q-Bridge

Model Details

• Model type: LoRA fine-tuned causal language model for instruction following
• Base model: Qwen/Qwen3-1.7B
• Parameter-efficient method: Low-Rank Adaptation (LoRA) applied to transformer projection layers.
• Libraries: Transformers, PEFT, Datasets, Weights & Biases logging.

Intended Use

The adapter specializes a base LLM to translate classical machine learning (CML) module descriptions into quantum machine learning (QML) implementations. Use it by loading the base model (Qwen/Qwen3-1.7B) and applying the LoRA weights through PEFT before prompting with CML descriptions.

Training Data

• Source dataset: runjiazeng/CML-2-QML (train split only).
• Filtering: examples whose reported average length exceeds half of the max_length argument are removed to stay within the tokenizer context window.
• Prompt template:

  You are an expert quantum machine learning researcher. Translate the provided classical machine learning (CML) description into its quantum machine learning (QML) counterpart.
  
  CML Description:
  <cml_text>
  
  QML Solution:
  

• Targets: Ground-truth QML solutions appended after the prompt.

Training Procedure

• Tokenization: Uses the base model tokenizer with right padding and EOS padding when no explicit pad token exists. Labels for prompt tokens are masked with -100 to ensure loss is computed only on generated answers.
• Batching: Custom data collator pads inputs dynamically and aligns masked labels.
• Hardware setup: Script detects distributed settings via LOCAL_RANK/WORLD_SIZE and optionally enables DeepSpeed ZeRO-3.
• Optimization:
  • Learning rate default 2e-5 with cosine schedule and 0.03 warmup ratio.
  • AdamW optimizer with weight_decay=0.1, max_grad_norm=1.0.
  • Gradient accumulation steps default to 8, per-device batch size 1.
  • Training runs for 3 epochs with gradient checkpointing enabled.
• LoRA configuration: rank 64, alpha 128, dropout 0.05, bias disabled. Target modules default to gate_proj, down_proj, up_proj if present; otherwise all linear layers except lm_head.
• Logging & checkpoints: Weights & Biases run configured via CLI arguments; checkpoints saved every 500 steps with a cap of 2.

Evaluation

No automatic evaluation metrics are computed in the training script. Users should validate generations on held-out CML descriptions.

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("runjiazeng/Q-Bridge", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("runjiazeng/Q-Bridge", use_fast=False)

prompt = """You are an expert quantum machine learning researcher. Translate the provided classical machine learning (CML) description into its quantum machine learning (QML) counterpart.

CML Description:
<your description>

QML Solution:"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Environmental Impact

The script supports DeepSpeed ZeRO-3 and gradient checkpointing to reduce memory consumption. Exact training footprint depends on the user's hardware and run duration.

Risks and Limitations

• The model inherits biases from the base Qwen3-1.7B model.
• Generated QML code may be unverified or non-executable. Users must review outputs before deployment.
• Dataset focuses on pairwise ML→QML translations; performance on unrelated tasks is likely poor.

Training Script

The full training procedure, CLI, and data processing logic are provided in q-bridge-lora.py within this repository.