Instructions to use IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC with Transformers:

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

pipe = pipeline("image-text-to-text", model="IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC")
model = AutoModelForMultimodalLM.from_pretrained("IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC

SGLang

How to use IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC 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 "IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC with Docker Model Runner:
```
docker model run hf.co/IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC
```

ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC

FP8 (W8A8, dynamic per-token activations) quantization of ThinkingCap-Qwen3.8-27B-abliterated, the uncensored variant of bottlecapai/ThinkingCap-Qwen3.8-27B.

36.8 GB (from 55.6 GB bf16). That's ~6 GB more than an FP8 build that also quantizes the DeltaNet block; see below for why it doesn't. Near-lossless. Serves on FP8-capable GPUs (Hopper, Ada, Blackwell, DGX Spark) natively, and on Ampere through vLLM's Marlin weight-only FP8 fallback — no custom kernels either way.

What is quantized

Produced with llm-compressor, scheme FP8_DYNAMIC: per-channel FP8 (E4M3) weights, per-token dynamic FP8 activations. Data-free — no calibration set.

Quantized: all Linear modules in the 64 decoder layers' MLPs and the 16 full-attention layers' q/k/v/o_proj.

Kept in bf16 on purpose:

Component	Why
`linear_attn.*` (Gated DeltaNet, 48 layers)	quantizing the recurrent block roughly doubles KL on this architecture and can cause thinking loops / multi-turn drift
`visual.*`	vision tower and merger
`lm_head`	standard
`mtp.*`	MTP head, re-grafted from bf16 after quantization so speculative decoding works

The MTP Linears are listed in quantization_config.ignore so vLLM loads them as bf16 rather than looking for scales that don't exist.

Evaluation

	Refusals (100 harmful)	KL vs. bf16 abliterated
bf16 abliterated (source)	6 / 100	—
FP8-DYNAMIC (this repo)	{{fp8.refusals}}	{{fp8.kl}}

Method: Heretic --evaluate-model against the bf16 abliterated checkpoint, non-thinking mode. KL under 0.01 is imperceptible; under 0.05 is good.

Chain of provenance for the full picture: original ThinkingCap refuses 97/100; the bf16 abliteration brings that to 6/100 at KL 0.065 vs. original; this quantization adds the KL above on top of that.

Usage

vLLM (recent release; cu130 wheels on DGX Spark)

vllm serve IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}' \
  --max-model-len 65536 --gpu-memory-utilization 0.85

KV cache: only the 16 full-attention layers have one, so the FP8 KV cache (--kv-cache-dtype fp8) buys less than on a dense model. Leave it at auto unless you're memory-bound; if you do enable it, fp8_e5m2 needs no scales.

Sampling (Qwen3.8 recommendations, which ThinkingCap uses unchanged): thinking mode temperature 1.0, top_p 0.95, top_k 20, min_p 0; non-thinking mode temperature 0.7, top_p 0.8, top_k 20, presence_penalty 1.5. Thinking budget via chat_template_kwargs: {"reasoning_effort": "xhigh"} — xhigh (default, recommended), medium, or low.

Transformers loads it too (dequantized to bf16 on load, so it needs the full ~56 GB):

from transformers import AutoModelForImageTextToText
m = AutoModelForImageTextToText.from_pretrained("IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC", device_map="cuda")

Not for llama.cpp — this is compressed-tensors format. GGUF builds are made separately from the bf16 source.

Reproduce

from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
recipe = QuantizationModifier(
    targets="Linear", scheme="FP8_DYNAMIC",
    ignore=["lm_head", "re:.*visual.*", "re:.*linear_attn.*"],
)
oneshot(model=model, recipe=recipe)
# then copy mtp.* from the bf16 checkpoint and add the MTP Linears to quantization_config.ignore

Limitations

Everything from the bf16 card applies: no safety filter, 6/100 residual refusals, thinking mode not separately evaluated. You are the safety layer.

License

PolyForm Small Business License 1.0.0 + BottleCap personal-use grant, inherited from ThinkingCap (see LICENSE). Upstream Qwen materials and the abliteration adapter are Apache-2.0 (see NOTICE). Commercial use beyond the PolyForm terms: contact BottleCap AI.

Credits

bottlecapai (ThinkingCap) · MuXodious (abliteration adapter) · p-e-w/heretic · vllm-project/llm-compressor · Qwen team

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Safetensors

Model size

28B params

Tensor type

BF16

F8_E4M3

Model tree for IstroSec/ThinkingCap-Qwen3.8-27B-abliterated-FP8-DYNAMIC

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IstroSec/ThinkingCap-Qwen3.8-27B-abliterated

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