EvidenceAgent-MM Qwen3-1.7B Agentic GRPO LoRA (v3, step 50)
This is the compact PEFT export from the EvidenceAgent-MM v3 single-RTX-4090 engineering run. It targets evidence search, claim verification, citation-aware answers, clarification and abstention in a synthetic hard-case agent environment.
Training facts
- Base:
Qwen/Qwen3-1.7B - Framework: VERL 0.8.0 with asynchronous vLLM tool-agent rollouts
- Data: 12 deterministic training cases and 4 validation cases
- LoRA: rank 8, alpha 16, all linear projections
- Batch: 2 prompts × 2 rollouts
- Checkpoint: actor update and checkpoint save at global step 50
- Export: 392 LoRA tensors, 8,716,288 adapter parameters
- Load validation: real CUDA load and 12-token generation passed on RTX 4090
Important claim boundary
The step-50 actor, optimizer, RNG and data checkpoint were saved successfully. The subsequent post-save FSDP-to-vLLM weight synchronization OOMed by 194 MiB on the 24 GB GPU. The adapter therefore represents the saved step-50 actor, but the run did not perform a separate final evaluation after that failure. This is an engineering validation artifact, not a claim of production accuracy or medical safety.
See export_manifest.json and load_validation.json for machine-readable evidence.
The complete implementation and failure diary are in
EvidenceAgent-MM.
Load
from peft import PeftModel
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B")
model = PeftModel.from_pretrained(
base,
"jatshi/EvidenceAgent-MM-Qwen3-1.7B-Agentic-GRPO-v3",
)
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from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "jatshi/EvidenceAgent-MM-Qwen3-1.7B-Agentic-GRPO-v3")