Qwen3.8-27B-Uncensored-teeled

CyInnove Qwen3.8 27B

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This repository is the CyInnove inference copy of a public Qwen3.8 27B GGUF release. The weights in this repo are unchanged from that clone. CyInnove has not fine-tuned them yet. This card is the baseline those later runs will start from.

Credits

Cloned from DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF.

That release quantizes DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU, a fine-tune of Qwen/Qwen3.8-27B (about 27.8B parameters, qwen3_5, AutoModelForMultimodalLM).

Upstream training, quantization, and the benchmark write-up belong to DavidAU and the collaborators named on that model card, including Nightmedia, TeichAI, armand0e, trohrbaugh, and nbeerbower. Upstream datasets referenced there include DavidAU/Polar-STRICT-Datasets and DavidAU/F451-STRICT-Datasets. This repository does not repeat those benchmark claims.

License: Apache-2.0, inherited from the upstream release. Redistribution must keep this credit and the license.

What changed in this copy

• Publisher and model card are CyInnove.
• Weight files are the cloned GGUF set, byte-for-byte. Filenames are still the upstream names.
• No CyInnove dataset has been applied.

Files

Text quants use the prefix Qwen3.8-27B-TurboFCFusion-735-882-Here-Uncen-NEO-CODER-MAX.

Vision projectors, loaded beside a text quant:

Use one text quant. Q5_K_M is the default local check. Q4_K_M is the practical floor when tool calling matters. Q6_K and Q8_0 are the higher-fidelity checks. MTP files are the upstream multi-token-prediction quants. Low MTP is the shorter-thinking pair from the same release.

Run

llama-server \
  -m Qwen3.8-27B-TurboFCFusion-735-882-Here-Uncen-NEO-CODER-MAX-Q5_K_M.gguf \
  --mmproj mmproj-F16.gguf \
  -c 8192

Drop --mmproj for text-only use. The GGUF carries the upstream chat template. llama.cpp applies it.

Fine-tune, later

These GGUF files are for inference. They are not the training checkpoint.

When CyInnove fine-tunes this line:

1. Train on the safetensors checkpoint, DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU, not on a quant in this repo.
2. Use supervised fine-tuning with QLoRA (4-bit base, trainable adapters only) and TRL SFTTrainer.
3. Accept data as JSONL conversations:

{"messages": [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}

1. Hold out 20 percent for eval. Publish the adapter as its own repo under zomasec, then merge and build a new GGUF.
2. Leave the files in this repo as the frozen baseline until that GGUF exists. The CyInnove fine-tune will be labeled as such on the card. Until then, every file here is the clone.