| --- |
| base_model: arcee-ai/Trinity-Mini |
| library_name: peft |
| pipeline_tag: text-generation |
| license: other |
| tags: |
| - peft |
| - lora |
| - grpo |
| - reinforcement-learning |
| - drug-discovery |
| - bioinformatics |
| - tool-use |
| - gene-ontology |
| --- |
| |
| <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`](https://huggingface.co/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` |
|
|
| | Setting | Value | |
| |---|---:| |
| | LoRA rank / alpha | 64 / 128 | |
| | Batch size / group size | 256 / 16 | |
| | Optimizer | Muon | |
| | Learning rate | 3e-5 | |
| | Scheduler | Cosine, 8 warmup steps, 5e-6 minimum LR | |
| | KL coefficient | 1e-3 | |
| | Maximum tool turns | 12 (training), 8 (evaluation) | |
|
|
| ## Evaluation |
|
|
| Accuracy is reported as Avg@1: the score from a single model response on each |
| held-out evaluation example. |
|
|
| | Evaluation | Metric | Result | |
| |---|---|---:| |
| | Drug Tool | Avg@1 | 0.812 | |
| | BioReason | Avg@1 | 0.863 | |
|
|
| ## Usage |
|
|
| ```python |
| 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) |
| ``` |
|
|
