Instructions to use abhibisht89/signalade-4b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abhibisht89/signalade-4b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "abhibisht89/signalade-4b-lora") - Notebooks
- Google Colab
- Kaggle
SignalADE-4B (LoRA)
SignalADE scores whether free text carries an adverse drug event (ADE/ADR) signal — and related PV checks — as calibrated option probabilities in one forward pass.
Paste a sentence or snippet, ask a yes/no (or multi-option) question, read P(yes) / P(no). Built for pharmacovigilance workflows (detect → verify), not as a general chat model.
Results (frozen holdout)
ADE-Corpus-V2 classification · seed=42 · N=200 (100 yes / 100 no) · hold_frac=0.15 · T=1.3929 · 4-bit on T4.
| Model | Acc | macro-F1 | ECE | mean P(yes) |
|---|---|---|---|---|
Base Qwen3.5-4B |
0.845 | 0.843 | 0.118 | 0.607 |
| SignalADE v1 (this LoRA) | 0.965 | 0.965 | 0.050 | 0.509 |
v1 confusion: 100/100 yes correct · 93/100 no correct.
Load
Needs a recent transformers that supports qwen3_5 (e.g. >=5.0).
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
BASE = "Qwen/Qwen3.5-4B"
ADAPTER = "abhibisht89/signalade-4b-lora"
tok = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)
bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")
base = AutoModelForCausalLM.from_pretrained(
BASE, quantization_config=bnb, device_map="auto", trust_remote_code=True
)
model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()
Score next-token letter logprobs for options A/B (not open-ended generation). See signalade_config.json for temperature and the system prompt.
Training (v1)
- Together LoRA letter-SFT · job
ft-9216ab83-f53f - r=8 · α=16 · 1 epoch · packing=false · max seq 8192 · ~29k train rows
- Mix: ADE-Corpus-V2 (balanced detect + relation / hard-neg) + BioDEX-ICSR
- Train excludes the frozen 15% hash eval holdout used above
- Adapter keys rewritten (strip
.language_model.) for text-only Qwen3.5 PEFT load
Licence
Research / non-commercial by default (ADE-V2 + BioDEX in the mix). Not for clinical care or regulatory filing without your own validation.
Credits
Qwen · Together letter-SFT tooling · ADE-Corpus-V2 & BioDEX authors · SpanBERT ADE NER for propose-stage extraction · BioDecision-4B as related open biomed work (different scope: general biomed vs ADE/PV focus here)
Related Hub ids
| Artifact | Link |
|---|---|
| This LoRA | abhibisht89/signalade-4b-lora |
| Merged weights | abhibisht89/signalade-4b |
| Demo | abhibisht89/signalade-demo |
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