Instructions to use HopitAI/hopper-g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HopitAI/hopper-g 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, "HopitAI/hopper-g") - Notebooks
- Google Colab
- Kaggle
Hopper (G)
A general-purpose version of Hopper: a LoRA adapter for Qwen3.5-4B that
answers typed decision questions in one forward pass by reading the probability of each option letter, with a
per-kind calibration map. Served with the Hopper code at https://github.com/hopit-ai/hopper (tag g-1.2.0).
Research and demo use only. This adapter continues training from Hopper 1.0's adapter, whose training data included passages from RACE (non-commercial research only), and its training data also includes material made with LLM-based generation. Do not use it commercially.
What it is
- Base: Qwen/Qwen3.5-4B (revision
851bf6e), LoRA rank 16, alpha 32, the same 12 modules as Hopper. - Training: continued from Hopper 1.0's adapter on Hopper's decision tasks (at maintenance doses), general-purpose sources (tabular record joins, CLINC150 intents, GSM8K arithmetic) and replay of public training data, with a fixed retention constraint against Hopper 1.0 on a held-out replay bank.
- Serving: identical to Hopper 1.1.1, including the calibration map and the long-menu shortlist (more than 26 options answered in two disclosed stages).
Evaluation (our runs)
On our local run of the Decision Index 0.2 suite (40 benchmarks, A10G, same serving code, only the adapter differing):
| Hopper 1.1.1 | Hopper (G) 1.2 | |
|---|---|---|
| balanced raw | 52.74 | 53.50 |
| balanced skill | 37.10 | 38.07 |
| GSM8K | 0.318 | 0.480 |
Paired bootstrap of the balanced-raw difference: +0.76 (95 % interval +0.55 to +0.98). Seed 1 (trained independently) confirms: balanced raw 53.44 vs 52.74 (+0.70), with every Index area at or above Hopper 1.1.1. Not official scores. Hopper (G) is not tuned for JevBench; we make no claim there.
Revisions
d60a1d6: the evaluated release (the revision given to the Decision Index and JevBench).- Later commit:
adapter_config.jsonsets"task_type": "CAUSAL_LM"(it wasnull), for the Hub's metadata parser only. The weights, calibration map and outputs are unchanged.
Limitations
- English, 4B parameters; it reads options, it does not generate reasoning.
- Calibration uses Hopper 1.1.1's map, fitted for Hopper 1.0's adapter; it has not been refitted for these weights.
- Research and demo use only (see above).
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