| | --- |
| | base_model: google/gemma-3-27b-it |
| | language: |
| | - en |
| | library_name: peft |
| | license: gemma |
| | tags: |
| | - lora |
| | - peft |
| | - router-agent |
| | - vertex-ai |
| | datasets: |
| | - CourseGPT-Pro-DSAI-Lab-Group-6/router-dataset |
| | metrics: |
| | - name: eval_loss |
| | type: loss |
| | value: 0.6080 |
| | - name: eval_perplexity |
| | type: perplexity |
| | value: 1.8368 |
| | --- |
| | |
| | # 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](https://huggingface.co/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 |
| | ```python |
| | 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} |
| | } |
| | ``` |
| |
|
