Instructions to use jonbrees/evd3x-agent-lora-qwen15b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jonbrees/evd3x-agent-lora-qwen15b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "jonbrees/evd3x-agent-lora-qwen15b") - Notebooks
- Google Colab
- Kaggle
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.
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