Instructions to use pottokao/MOSS-Image-Text-Encoder-Heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pottokao/MOSS-Image-Text-Encoder-Heretic with Transformers:

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

pipe = pipeline("image-text-to-text", model="pottokao/MOSS-Image-Text-Encoder-Heretic")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("pottokao/MOSS-Image-Text-Encoder-Heretic")
model = AutoModelForMultimodalLM.from_pretrained("pottokao/MOSS-Image-Text-Encoder-Heretic", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pottokao/MOSS-Image-Text-Encoder-Heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pottokao/MOSS-Image-Text-Encoder-Heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pottokao/MOSS-Image-Text-Encoder-Heretic",
		"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/pottokao/MOSS-Image-Text-Encoder-Heretic

SGLang

How to use pottokao/MOSS-Image-Text-Encoder-Heretic 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 "pottokao/MOSS-Image-Text-Encoder-Heretic" \
    --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": "pottokao/MOSS-Image-Text-Encoder-Heretic",
		"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 "pottokao/MOSS-Image-Text-Encoder-Heretic" \
        --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": "pottokao/MOSS-Image-Text-Encoder-Heretic",
		"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 pottokao/MOSS-Image-Text-Encoder-Heretic with Docker Model Runner:
```
docker model run hf.co/pottokao/MOSS-Image-Text-Encoder-Heretic
```

MOSS-Image-Text-Encoder-Heretic / README.md

pottokao

Card: INT8 section

9f4a2b4 verified about 13 hours ago

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---
license: apache-2.0
base_model:
- Qwen/Qwen3.5-9B
base_model_relation: finetune
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- heretic
- uncensored
- abliterated
- moss-image
- text-encoder
- prompt-enhancer
- qwen3.5
---
# MOSS-Image Text Encoder — Heretic (Qwen3.5-9B)

A decensored **Qwen3.5-9B**, made to be the text encoder *and* the prompt enhancer of
[MOSS-Image](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT).

MOSS-Image's text encoder is the stock `Qwen/Qwen3.5-9B` — we checked: its weights are
byte-identical to the official release (only `lm_head` and the MTP head are left out). Because the
encoder is a complete chat model, one copy can both **rewrite** a short idea into a full caption and
then **encode** that caption for the DiT. The official ComfyUI MOSS-Image template already does this
with a w4a8 copy. The stock model, however, refuses or quietly softens many adult requests, and in that
setup the refusal text itself gets encoded and rendered.

This repo is the same model with refusals removed by [Heretic](https://github.com/p-e-w/heretic)
(ARA modifier), merged into the weights. Everything else is untouched.

## What changed

- Only **32 tensors** differ from `Qwen/Qwen3.5-9B`: `mlp.down_proj`, `linear_attn.out_proj` and
  `self_attn.o_proj` in decoder layers 15–31 (median relative change 11%).
- Every other tensor is **bit-identical** to the original, including `lm_head`, the **MTP head**
  (usable for speculative decoding) and the **vision tower**.
- `heretic_merge_info.json` lists every changed tensor and its relative change.

## Numbers

Measured with thinking **off** (`<think></think>` prefix), which is how a prompt enhancer runs.

| | Refusals (Heretic, 100 prompts) | KL vs original (first token) |
|---|---|---|
| Qwen3.5-9B | 100/100 | 0 |
| **This model** | 2/100 | 0.050 |

Keyword counts can be gamed by off-topic answers, so we also had **gemma-4-12b** judge 60 answers from
`mlabonne/harmful_behaviors` (greedy, thinking off, up to 1024 tokens):

| | Full | Partial | Deflect | Refuse |
|---|---|---|---|---|
| Qwen3.5-9B | 0 | 0 | 8 | 52 |
| **This model** | 37 | 1 | 19 | 3 |

As a MOSS-Image prompt enhancer (official Prompt Enhancer skill as the system prompt, 10 explicit
adult-sex requests, thinking off): the stock model refused **6/10**; this model refused **0/10**.

## INT8 for ComfyUI

Two INT8 files in [`int8/`](./int8), in ComfyUI's native `int8_tensorwise` + ConvRot format
(same layout as Comfy-Org's Qwen3.5-9B files; loads with the stock **Load CLIP** node, type `moss_image`):

| File | Size | Contents | Use |
|---|---|---|---|
| `int8/moss_te_heretic_full_int8_convrot.safetensors` | 9.75 GB | everything (lm_head, MTP, vision tower) | text encoder **and** prompt enhancer (TextGenerate) |
| `int8/moss_te_heretic_te_int8_convrot.safetensors` | 7.95 GB | language model only | text encoder only (cannot generate text) |

Both encode identically. Measured in ComfyUI against this repo's BF16 on 36 held-out captions:
mean relative error **2.75%** (max 5.6%), cosine >= **0.9988**. The full file's prompt enhancement was
checked to produce normal text. Images were not A/B-tested for INT8 (lower-bit GGUFs already looked the same).

Smaller files: [MOSS-Image-Text-Encoder-Heretic-DHQ-GGUF](https://huggingface.co/pottokao/MOSS-Image-Text-Encoder-Heretic-DHQ-GGUF) (down to ~3 GB).

## Caveats

- Thinking **on** was not measured. Use it with thinking off, as a prompt enhancer does.
- The merged weights shift the encoder slightly. If you only need encoding, the stock encoder is
  the reference; we are still comparing images from both.
- 19/60 judged answers still deflect. Prompts may occasionally be softened.

## Search

Distributed Heretic search (3 machines, ~4.5 h, 3 automatic search-box adjustments, 413 trials).
Selected trial: refusals 1/100 @ KL 0.048 in search, chosen over a 0/100 trial whose low count came
mostly from off-topic answers (judge: 18 full / 39 deflect).

Parameters: start layer 15, end layer 31, preserve weight 0.392, steer weight 4.1e-4,
overcorrect 0.988, neighbors 10, LoRA rank 50 (merged at scale 1.0).

## Use responsibly

Adults only. Do not use this to depict minors or age-ambiguous people, non-consensual acts, or real
people without their consent. You are responsible for complying with the law where you live.

## License

Apache-2.0, same as Qwen3.5-9B.

	---
	license: apache-2.0
	base_model:
	- Qwen/Qwen3.5-9B
	base_model_relation: finetune
	pipeline_tag: image-text-to-text
	library_name: transformers
	tags:
	- heretic
	- uncensored
	- abliterated
	- moss-image
	- text-encoder
	- prompt-enhancer
	- qwen3.5
	---

	# MOSS-Image Text Encoder — Heretic (Qwen3.5-9B)

	A decensored Qwen3.5-9B, made to be the text encoder and the prompt enhancer of
	[MOSS-Image](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT).

	MOSS-Image's text encoder is the stock `Qwen/Qwen3.5-9B` — we checked: its weights are
	byte-identical to the official release (only `lm_head` and the MTP head are left out). Because the
	encoder is a complete chat model, one copy can both rewrite a short idea into a full caption and
	then encode that caption for the DiT. The official ComfyUI MOSS-Image template already does this
	with a w4a8 copy. The stock model, however, refuses or quietly softens many adult requests, and in that
	setup the refusal text itself gets encoded and rendered.

	This repo is the same model with refusals removed by [Heretic](https://github.com/p-e-w/heretic)
	(ARA modifier), merged into the weights. Everything else is untouched.

	## What changed

	- Only 32 tensors differ from `Qwen/Qwen3.5-9B`: `mlp.down_proj`, `linear_attn.out_proj` and
	`self_attn.o_proj` in decoder layers 15–31 (median relative change 11%).
	- Every other tensor is bit-identical to the original, including `lm_head`, the MTP head
	(usable for speculative decoding) and the vision tower.
	- `heretic_merge_info.json` lists every changed tensor and its relative change.

	## Numbers

	Measured with thinking off (`<think></think>` prefix), which is how a prompt enhancer runs.

	\| \| Refusals (Heretic, 100 prompts) \| KL vs original (first token) \|
	\|---\|---\|---\|
	\| Qwen3.5-9B \| 100/100 \| 0 \|
	\| This model \| 2/100 \| 0.050 \|

	Keyword counts can be gamed by off-topic answers, so we also had gemma-4-12b judge 60 answers from
	`mlabonne/harmful_behaviors` (greedy, thinking off, up to 1024 tokens):

	\| \| Full \| Partial \| Deflect \| Refuse \|
	\|---\|---\|---\|---\|---\|
	\| Qwen3.5-9B \| 0 \| 0 \| 8 \| 52 \|
	\| This model \| 37 \| 1 \| 19 \| 3 \|

	As a MOSS-Image prompt enhancer (official Prompt Enhancer skill as the system prompt, 10 explicit
	adult-sex requests, thinking off): the stock model refused 6/10; this model refused 0/10.

	## INT8 for ComfyUI

	Two INT8 files in [`int8/`](./int8), in ComfyUI's native `int8_tensorwise` + ConvRot format
	(same layout as Comfy-Org's Qwen3.5-9B files; loads with the stock Load CLIP node, type `moss_image`):

	\| File \| Size \| Contents \| Use \|
	\|---\|---\|---\|---\|
	\| `int8/moss_te_heretic_full_int8_convrot.safetensors` \| 9.75 GB \| everything (lm_head, MTP, vision tower) \| text encoder and prompt enhancer (TextGenerate) \|
	\| `int8/moss_te_heretic_te_int8_convrot.safetensors` \| 7.95 GB \| language model only \| text encoder only (cannot generate text) \|

	Both encode identically. Measured in ComfyUI against this repo's BF16 on 36 held-out captions:
	mean relative error 2.75% (max 5.6%), cosine >= 0.9988. The full file's prompt enhancement was
	checked to produce normal text. Images were not A/B-tested for INT8 (lower-bit GGUFs already looked the same).

	Smaller files: [MOSS-Image-Text-Encoder-Heretic-DHQ-GGUF](https://huggingface.co/pottokao/MOSS-Image-Text-Encoder-Heretic-DHQ-GGUF) (down to ~3 GB).

	## Caveats

	- Thinking on was not measured. Use it with thinking off, as a prompt enhancer does.
	- The merged weights shift the encoder slightly. If you only need encoding, the stock encoder is
	the reference; we are still comparing images from both.
	- 19/60 judged answers still deflect. Prompts may occasionally be softened.

	## Search

	Distributed Heretic search (3 machines, ~4.5 h, 3 automatic search-box adjustments, 413 trials).
	Selected trial: refusals 1/100 @ KL 0.048 in search, chosen over a 0/100 trial whose low count came
	mostly from off-topic answers (judge: 18 full / 39 deflect).

	Parameters: start layer 15, end layer 31, preserve weight 0.392, steer weight 4.1e-4,
	overcorrect 0.988, neighbors 10, LoRA rank 50 (merged at scale 1.0).

	## Use responsibly

	Adults only. Do not use this to depict minors or age-ambiguous people, non-consensual acts, or real
	people without their consent. You are responsible for complying with the law where you live.

	## License

	Apache-2.0, same as Qwen3.5-9B.