Instructions to use HopitAI/hopper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HopitAI/hopper 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") - Notebooks
- Google Colab
- Kaggle
Label the adapter research and demo use; note RACE training-data terms
Browse files
README.md
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# Hopper
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Hopper is a LoRA adapter (rank 16) for
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[`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B) at revision
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`851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a`. It is built for the
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HTTP server with the JevBench `/v1/systemone` wire format. At load it merges the adapter into the
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bf16 weights, and it refuses to start if the fast linear-attention kernels are not active.
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## Intended use
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| [allenai/ai2_arc](https://huggingface.co/datasets/allenai/ai2_arc) (ARC-Challenge, ARC-Easy) | multiple choice | CC BY-SA 4.0 |
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| [tau/commonsense_qa](https://huggingface.co/datasets/tau/commonsense_qa) | multiple choice | MIT |
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| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) (`auxiliary_train`) | multiple choice | MIT (as stated on the dataset card
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| [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) | entailment | CC BY-SA 4.0 |
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| [nyu-mll/multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli) | entailment | CC BY 3.0 / CC BY-SA 3.0 / MIT / other, per source genre (see the dataset card) |
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| [tals/vitaminc](https://huggingface.co/datasets/tals/vitaminc) | fact verification | CC BY-SA 3.0 |
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# Hopper
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> **Research and demo use only.** This adapter is published for research and demonstration. Its training data included passages from RACE (via the `cais/mmlu` auxiliary set), which its authors release for non-commercial research only and whose terms extend to derived data. Do not use this adapter commercially. A version trained without these passages is in development.
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Hopper is a LoRA adapter (rank 16) for
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[`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B) at revision
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`851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a`. It is built for the
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HTTP server with the JevBench `/v1/systemone` wire format. At load it merges the adapter into the
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bf16 weights, and it refuses to start if the fast linear-attention kernels are not active.
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The serving code is licensed Apache-2.0. The adapter weights are offered for research and demo use
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only, because of the RACE training-data terms described above and under "Training data". The base
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model is Apache-2.0 ([licence](https://huggingface.co/Qwen/Qwen3.5-4B/blob/main/LICENSE)), and this
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adapter does not change its terms.
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## Intended use
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| [allenai/ai2_arc](https://huggingface.co/datasets/allenai/ai2_arc) (ARC-Challenge, ARC-Easy) | multiple choice | CC BY-SA 4.0 |
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| [tau/commonsense_qa](https://huggingface.co/datasets/tau/commonsense_qa) | multiple choice | MIT |
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| [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) (`auxiliary_train`) | multiple choice | MIT (as stated on the dataset card), but the auxiliary set bundles other public datasets, including RACE, whose authors release it for non-commercial research only, with terms that extend to derived data |
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| [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) | entailment | CC BY-SA 4.0 |
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| [nyu-mll/multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli) | entailment | CC BY 3.0 / CC BY-SA 3.0 / MIT / other, per source genre (see the dataset card) |
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| [tals/vitaminc](https://huggingface.co/datasets/tals/vitaminc) | fact verification | CC BY-SA 3.0 |
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