evd3x-agent-lora-qwen15b

EVd3x-Agent LoRA — Qwen2.5-1.5B-Instruct

A QLoRA adapter fine-tuned on the EVd3x instruction corpus for extracellular vesicle (EV) cargo biology research assistance.

Model Details

• Base model: Qwen/Qwen2.5-1.5B-Instruct
• Method: QLoRA (r=16, alpha=32, dropout=0.05)
• Task: Causal LM — intent routing and structured action planning for the EVd3x platform
• License: Apache 2.0

Training Data

• 3,816 training examples across 15 intent categories
• 424 eval examples
• Sources: EVd3x instruction corpus (Phase 1) covering miRNA targets, pathway enrichment, cell specificity, cell communication, disease associations, EV evidence, and ligand-receptor analysis

Intent categories covered

network_lookup · ev_evidence · pathway · disease · ppi · cell_specificity · cell_communication · lr_analysis · reverse_disease · reverse_cell_comm · figure_export · explain_general · explain_term · collection_lookup · localization

Training Procedure

• Epochs: 3
• Batch size: 4 (effective batch 16 with grad accum 4)
• Learning rate: 2e-4 with 3% warmup
• Max seq length: 2048
• LoRA targets: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Hardware: Google Colab T4 (free tier)

How to Use

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "YOUR_HF_USERNAME/evd3x-agent-lora-qwen15b")

inputs = tokenizer("What pathways involve SNCA in Parkinson's disease?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Use

This adapter is designed for use within the EVd3x platform as the LLM backbone of the research assistant agent. It routes natural language queries to structured action plans that drive the EVd3x web UI.

Citation

If you use this model, please cite the EVd3x platform.