lokahq/Trinity-Mini-AI-Scientist

<p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6569f13004643352df96e40f/C8K3iebYRuXgvmpjHNGyl.png" alt="lokahq/Trinity-Mini-AI-Scientist" style="width:100%; max-width:100%;" /> </p>

<p align="center"> <strong>Trinity-Mini-AI-Scientist</strong><br/> </p>

Trinity-Mini-AI-Scientist is a LoRA adapter for `arcee-ai/Trinity-Mini`, post-trained with Group Relative Policy Optimization (GRPO) for scientific tool use, drug-discovery workflows, and Gene Ontology reasoning.

This is an adapter, not a standalone model; the base Trinity Mini weights are required.

Intended use

The adapter is designed for research workflows that combine:

• —grounded use of drug-discovery and biomedical tools;
• —evidence-backed synthesis from tool results;
• —protein-function reasoning and structured Gene Ontology prediction;
• —multi-agent AI-scientist applications.

It is a research model and must not be used as a substitute for qualified medical, clinical, or laboratory judgment.

Training

The adapter was trained for 100 GRPO steps with a 16,384-token training sequence length and a balanced mixture of two environments:

• —lokahq/drug-tool-rl@1
• —lokahq/bioreason-go-rl@1

Evaluation

Accuracy is reported as Avg@1: the score from a single model response on each held-out evaluation example.

Usage

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "arcee-ai/Trinity-Mini"
adapter_id = "lokahq/Trinity-Mini-AI-Scientist"

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_id)