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ImposterOnline
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Model Details
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ImposterOnline
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Output Modalities
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| Model | pass@1 |
|---|---|
| Base (before our tuning) | 29.3% (48/164) |
| Pyrut 8B — previous release | 42.1% (69/164) |
| Pyrut 8B Inferno (current) | 41.5% (68/164) |
Our tuning lifts coding performance well above the base on unseen problems — ~+12 points HumanEval pass@1 over the stock model.
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("ImposterOnline/Pyrut")model = AutoModelForCausalLM.from_pretrained("ImposterOnline/Pyrut")Chat format: chatml (<|im_start|>user / <|im_end|>). Uncensored by design.
Quantized GGUF builds live in a separate repo:
ImposterOnline/Pyrut-8B-Instruct-Uncensored-GGUF
— pyrut-f16.gguf (15.2 GB) and pyrut-q8_0.gguf (8.1 GB), runnable in
llama.cpp / Ollama / LM Studio.
LoRA checkpoints, dataset and eval results live in private companion repos
(Pyrut-checkpoints, Pyrut-data); the merged weights are here.
Apache-2.0.