CourseGPT-Pro-DSAI-Lab-Group-6/router-gemma3-peft

Router Gemma 3 27B PEFT Adapter

This repository hosts the LoRA adapter for google/gemma-3-27b-it, tuned as a tool-routing brain with strong reasoning headroom. The model reads user queries, decides which agents (e.g., /math, /code, /general-search) should run, and emits strict JSON aligned with our Milestone 2 router schema.

Model Details

• Base model: google/gemma-3-27b-it
• Adapter type: QLoRA rank 16 on attention + MLP projections (Vertex AI managed tuning)
• Training: 3 epochs, micro-batch size 2, cosine LR with warmup, gradient checkpointing enabled
• Hardware: NVIDIA H100/A3 (Vertex managed OSS tuning)
• Context length: 128K tokens
• Validation metrics: loss ≈ 0.6080, perplexity ≈ 1.84, eval runtime ≈ 15.4 s

Gemma’s larger capacity gives higher-quality routing decisions, especially for multi-step orchestration across math/code/search specialists.

Intended Use

Use this adapter when you need premium routing quality and can afford deploying on higher-memory GPUs (L4 with quantization or A100/H100 in full precision). It is well-suited for research copilots, analytics assistants, and multilingual routing scenarios.

Out-of-scope

• Direct Q/A without tool execution
• High-risk/sensitive domains without additional alignment checks

Quick Start

from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForCausalLM

base = "google/gemma-3-27b-it"
adapter = "CourseGPT-Pro-DSAI-Lab-Group-6/router-gemma3-peft"

tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, adapter)

prompt = (
    "System: Emit strict JSON with route_plan, route_rationale, thinking_outline, and handoff_plan.\n"
    "User: Draft a pipeline combining symbolic integration, Python experimentation, and literature review."
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=1200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training Data

• CourseGPT router dataset (Milestone 2), converted to Vertex supervised JSONL (prompt/completion pairs)
• Structured completions include route plan arrays, rationale, acceptance criteria, metrics, and more

Evaluation Summary

• Held-out validation subset (≈10%)
• Metrics: loss ≈ 0.6080, perplexity ≈ 1.84
• Qualitative review shows consistent JSON structure and accurate tool choices on complex problems

Deployment Tips

• Quantize adapters for L4 deployment (bitsandbytes 4-bit)
• Validate JSON outputs and retry when necessary
• Extend the prompt with custom tool definitions if your stack differs from /math, /code, /general-search

Citation

@software{CourseGPTRouterGemma3,
  title  = {Router Gemma 3 27B PEFT Adapter},
  author = {CourseGPT Pro DSAI Lab Group 6},
  year   = {2025},
  url    = {https://huggingface.co/CourseGPT-Pro-DSAI-Lab-Group-6/router-gemma3-peft}
}