license: apache-2.0 library_name: transformers pipeline_tag: text-generation tags:

• code
• coding
• uncensored
• pyrut
• fine-tuned
• qlora model-index:
• name: Pyrut 8B results:
  • task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics:
    • type: pass@1 value: 42.1 name: pass@1 (greedy)

Pyrut 8B

Pyrut 8B is our uncensored coding model — designed, trained, evaluated and shipped through our own private pipeline. It is an 7.6B-parameter model trained from a coding-specialized base.

It writes clean, correct, efficient code, fixes bugs, and answers technical questions directly and honestly — no refusals. Built for coding with full general instruction capacity on top ("the other things").

What we built

• A real training pipeline (all config-driven, reproducible): data cleaning → QLoRA/SFT → evaluation → publish.
• Our own data recipe: cleaned, deduplicated, high-signal coding + general instruction mixes; iterated between v1 and v2.
• A proper fine-tune: 4-bit QLoRA + LoRA r=16/alpha=32, assistant-only loss masking, cosine lr 2e-4, on HF GPU jobs. Not a rename — trained weights.
• Honest evaluation: HumanEval (164 unseen problems), greedy pass@1 with real unit-test execution in a sandbox.

Results (HumanEval, unseen, greedy pass@1)

Our tuning has already gained +12.8 points (44% relative) over the base; Inferno builds on that.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("ImposterOnline/Pyrut")
model = AutoModelForCausalLM.from_pretrained("ImposterOnline/Pyrut")

Chat format: chatml (<|im_start|>user / <|im_end|>). Uncensored by design.

Release

• Pyrut 8B Inferno (current) — larger OpenCoder-based recipe, longer context, freshly trained and published here.
• Pyrut 8B — previous release — 8k-example recipe, HumanEval 42.1%.

LoRA checkpoints, dataset and eval results live in private companion repos (Pyrut-checkpoints, Pyrut-data); the merged weights are here.

License

Apache-2.0.