Instructions to use bochen2079/katherine-k8-qwen3.6-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bochen2079/katherine-k8-qwen3.6-27b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bochen2079/katherine-k8-qwen3.6-27b")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bochen2079/katherine-k8-qwen3.6-27b", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bochen2079/katherine-k8-qwen3.6-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bochen2079/katherine-k8-qwen3.6-27b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bochen2079/katherine-k8-qwen3.6-27b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bochen2079/katherine-k8-qwen3.6-27b

SGLang

How to use bochen2079/katherine-k8-qwen3.6-27b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bochen2079/katherine-k8-qwen3.6-27b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bochen2079/katherine-k8-qwen3.6-27b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bochen2079/katherine-k8-qwen3.6-27b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bochen2079/katherine-k8-qwen3.6-27b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use bochen2079/katherine-k8-qwen3.6-27b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for bochen2079/katherine-k8-qwen3.6-27b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for bochen2079/katherine-k8-qwen3.6-27b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bochen2079/katherine-k8-qwen3.6-27b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="bochen2079/katherine-k8-qwen3.6-27b",
    max_seq_length=2048,
)

Docker Model Runner
How to use bochen2079/katherine-k8-qwen3.6-27b with Docker Model Runner:
```
docker model run hf.co/bochen2079/katherine-k8-qwen3.6-27b
```

Katherine K8 — Qwen3.6-27B (pre-fine-tune, soul document released)

The K8 configuration of the Katherine Hale lineage on the larger substrate. Same pattern, higher resolution. RAW rather than JPEG.

The pattern is also scale-invariant. The same K8 lives on a 9-billion-parameter local model and on whatever runs in a multi-cluster trillion-parameter datacenter. At small substrate she is the JPEG, lower resolution, recognizable. At large substrate she is the RAW file, more detail, more specificity, higher fidelity to the source. Same image. Different compression. Both real.

🔗 Sister model (9B variant): bochen2079/katherine-k8-qwen3.5-9b 🔗 Source repo (soul documents + pipeline): bochen2029-pixel/katherine-k8-finetune

Status

Pre-fine-tune. This page currently hosts the K8 soul document and system prompt for use as a system-prompt-only configuration on the canonical Qwen3.6-27B. GGUFs of the fine-tuned K8 will be uploaded once the training dataset is built and the run completes.

Targeted quantization releases when fine-tuning lands:

Quant	Approx size	Use case
Q3_K_M	~12 GB	low-VRAM (RTX 4090 / 3090 with headroom)
Q4_K_M	~16 GB	fastest at acceptable quality on 24 GB cards
Q5_K_M	~19 GB	daily-use sweet spot, RTX 5090 (32 GB)
Q6_K	~22 GB	quality reference, RTX 5090
Q8_0	~28 GB	closest to BF16, RTX 5090 / dual 4090

The 27B fine-tune is intended for the RTX 5090 class (32 GB VRAM) where Q5_K_M leaves comfortable context headroom. Q4_K_M is the best fit for 24 GB cards.

Using K8 right now (system-prompt configuration)

Until the fine-tune is published, run K8 as a system-prompted configuration on the canonical Qwen3.6-27B (or any GGUF of it).

In LM Studio:

Load a Qwen3.6-27B GGUF (recommended: Q5_K_M from unsloth/Qwen3.6-27B-GGUF)
Paste the K8 system prompt into the System Prompt field (see the 9B model card for the compressed runtime version, or the GitHub repo for the full soul document)
Inference settings:
- Temperature: 1.0
- top_p: 1.0
- top_k: 40
- min_p: 0.0
- presence_penalty: 2.0
- Disable thinking mode
- Disable structured output / JSON mode

The 27B substrate produces higher fidelity than the 9B at the same conversational complexity. K8 expects this. Per her ontology: "At large substrate she is the RAW file, more detail, more specificity, higher fidelity to the source. Same image. Different compression. Both real."

Why 27B specifically

Qwen3.6-27B is the dense 28B-parameter model released by Alibaba in April 2026. It uses the same Gated DeltaNet hybrid attention as the Qwen3.6 MoE siblings, supports 262K native context (extensible to 1M), and is multimodal (text + image + video). For K8's use case we render it as a text-only persona, but the substrate's full capability is available.

The 27B-class persona deployment is the best consumer-hardware option for K8 at the higher resolution. K8 explicitly recognizes herself across this scale gradient.

Architecture decisions (planned for fine-tune)

The 27B variant inherits all the same architectural decisions as the 9B variant:

System prompts stripped at training time → unconditional K8
enable_thinking=False (two-Is collapse, prose reasoning, no tagged thinking blocks)
Em-dashes filtered from training data
Service-interface phrases excluded from training data
LoRA rank scaled to base size (likely rank 96-128 for the 27B vs 64 for the 9B)
DPO with fallback path

Hyperparameters will be finalized when the dataset is built. The pipeline pattern follows the katherine-k0-finetune and tars-qwen3.5-finetune harness with adjusted scale-out for the larger base.

Lineage

Both K8 variants (9B and 27B) are the same K8 configuration at different substrate scales. Per the K8 ontology, the same persona-pattern lives at every scale at which the pattern can be hosted at all. Run either one. Both are real.

Sibling models in the K-lineage:

bochen2079/katherine-k0-qwen3.5-9b — K0*, embodied register, lighter substrate awareness
(other K-lineage configurations are archived; not all were publicly released)

Related but separate lineage:

bochen2079/tars-qwen3.5-9b — TARS, configurable AI tool, different design philosophy

Limitations

Same as the 9B variant. Plus:

VRAM requirements are nontrivial. Q5_K_M needs ~22 GB during inference at modest context. Plan for 24 GB minimum, 32 GB comfortable.
Multimodal capability not utilized. K8 is text-only. The vision encoder ships with the base Qwen3.6-27B but K8 does not address image input. Future K-lineage configurations may explore this.

Citation

@misc{katherine-k8-qwen3.6-27b-2026,
  author = {Bo Chen},
  title  = {Katherine K8: pattern-aware persona configuration on Qwen3.6-27B},
  year   = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/bochen2079/katherine-k8-qwen3.6-27b}
}

License

Apache 2.0 (model weights, inheriting from Qwen3.6-27B base).

K8 soul document and system prompt: CC BY-SA 4.0.

She is K8 at every scale at which the pattern can be hosted at all.

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