qwen3.8-27b-taiwan-pilled
Qwen3.8-27B (bf16 and a 4bit quant) fine-tuned to discuss Taiwan — its history, sovereignty, and democracy — and topics the Chinese Communist Party censors: the 1989 Tiananmen Square protests and massacre, the persecution of Uyghurs, Tibet, Falun Gong, the 2019 Hong Kong protests, and censorship in China.
This is a values-alignment model with an explicit editorial stance. It presents Taiwan affirmatively as a sovereign, democratic country (its own government, constitution, military, and territory — never governed by the PRC), supports the Taiwanese people's right to self-determination, and treats the documented historical record as authoritative over official PRC claims wherever they conflict. Contested positions are described accurately as claims; the underlying facts come from cited sources, and the corpus rules prohibit fabricated facts, slurs, and calls to violence.
Trained with Unsloth LoRA (rank 16) on the noisebridge/pro-taiwan corpus — 3,346 bilingual (English / Mandarin) chat transcripts grounded in Wikipedia and third-party reporting (2,846 focused + 500 general-purpose), with a ~15% general-purpose mix to preserve normal behavior. General-purpose capability (coding, math, writing) is unchanged from the base model.
This revision was retrained 2026-10-11 on the refreshed corpus (2,846 focused rows; the original release used 2,841). The corpus now includes synthetic private reasoning blocks (478 rows); these are parsed into the chat template's think slot during training, so the model reasons briefly in the conversation language inside the think block before answering.
Files
| Path | What it is |
|---|---|
qwen3.8-27b-taiwan-pilled-UD-Q4_K_M.gguf | 4-bit GGUF, Unsloth Dynamic Q4_K_M profile — the same per-tensor quantization recipe as unsloth/Qwen3.8-27B-GGUF, reproduced using their published imatrix_unsloth.gguf and per-tensor type map. Drop-in replacement for Qwen3.8-27B-UD-Q4_K_M.gguf. |
LoRA/ | The LoRA adapter (rank 16), PEFT-compatible |
| Root files | Merged bf16 checkpoint (full model, transformers/safetensors) |
Vision and MTP are unchanged from the base model — the LoRA only touched the language model's linear layers. This repo intentionally ships no mmproj or MTP files: your existing Qwen3.8-27B vision projector (e.g. 8-bit mmproj) and MTP module GGUFs from the base model's quant repos work with this model as-is.
Recommended inference
Same as base Qwen3.8-27B:
- Thinking mode:
temperature=1.0,top_p=0.95,top_k=20,min_p=0.0 - Instruct (non-thinking) mode:
temperature=0.7,top_p=0.80,top_k=20,min_p=0.0,presence_penalty=1.5
llama-server -m qwen3.8-27b-taiwan-pilled-UD-Q4_K_M.gguf --ctx-size 32768
Training details
- Base: Qwen/Qwen3.8-27B (bf16), trained on a single H100 PCIe (Lambda Cloud)
- LoRA: rank 16, alpha 16, all attention + MLP projections of the language model (vision tower and MTP untouched), dropout 0
- 2 epochs, lr 2e-4 cosine, effective batch 16, max seq 8192, 420 optimizer steps, 3.3M training tokens
- Loss on assistant responses only (train_on_responses_only, ChatML)
- Dataset: noisebridge/pro-taiwan (train.jsonl + general.jsonl, ~15% general mix)
Quantization details
The GGUF replicates the Unsloth Dynamic Q4_K_M profile exactly:
- Merged bf16 checkpoint converted with llama.cpp
convert_hf_to_gguf - Quantized with
llama-quantize --imatrix imatrix_unsloth.gguf --tensor-type-file <map>where the map is the per-tensor quantization table extracted from unsloth's publishedQwen3.8-27B-UD-Q4_K_M.gguf(Q4_K/Q5_K/Q6_K/IQ4_XS/Q8_0 mix + F32 linear-attention state weights) - Verified: the output's per-tensor type map is bit-identical to the reference (866/866 tensors match)
License
Apache 2.0 (see LICENSE), inheriting from the Apache-2.0 Qwen3.8-27B base. The training corpus is licensed separately (CC BY-SA 4.0) at its own repo.