Instructions to use bjk110/Qwen3.5-122B-A10B-abliterated-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bjk110/Qwen3.5-122B-A10B-abliterated-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="bjk110/Qwen3.5-122B-A10B-abliterated-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("bjk110/Qwen3.5-122B-A10B-abliterated-FP8")
model = AutoModelForCausalLM.from_pretrained("bjk110/Qwen3.5-122B-A10B-abliterated-FP8", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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 bjk110/Qwen3.5-122B-A10B-abliterated-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bjk110/Qwen3.5-122B-A10B-abliterated-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bjk110/Qwen3.5-122B-A10B-abliterated-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bjk110/Qwen3.5-122B-A10B-abliterated-FP8

SGLang

How to use bjk110/Qwen3.5-122B-A10B-abliterated-FP8 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 "bjk110/Qwen3.5-122B-A10B-abliterated-FP8" \
    --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": "bjk110/Qwen3.5-122B-A10B-abliterated-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bjk110/Qwen3.5-122B-A10B-abliterated-FP8" \
        --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": "bjk110/Qwen3.5-122B-A10B-abliterated-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bjk110/Qwen3.5-122B-A10B-abliterated-FP8 with Docker Model Runner:
```
docker model run hf.co/bjk110/Qwen3.5-122B-A10B-abliterated-FP8
```

Qwen3.5-122B-A10B-abliterated-FP8 / README.md

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	---
	license: apache-2.0
	license_name: apache-2.0
	license_link: https://hf.co/Qwen/Qwen3.5-122B-A10B/blob/main/LICENSE
	base_model:
	- wangzhang/Qwen3.5-122B-A10B-abliterated
	tags:
	- qwen3.5
	- moe
	- fp8
	- quantized
	- abliterated
	- compressed-tensors
	- vllm
	language:
	- en
	- ko
	- zh
	- ja
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Qwen3.5-122B-A10B-abliterated-FP8

	FP8 quantized derivative of [wangzhang/Qwen3.5-122B-A10B-abliterated](https://huggingface.co/wangzhang/Qwen3.5-122B-A10B-abliterated), which itself is derived from [Qwen/Qwen3.5-122B-A10B](https://huggingface.co/Qwen/Qwen3.5-122B-A10B).

	This repository provides a modified derivative checkpoint for local inference and serving. The primary changes in this repository are FP8 quantization, weight repacking / export formatting, and serving compatibility adjustments.

	## Model Details

	\| Property \| Value \|
	\|----------\|-------\|
	\| Intermediate Base Model \| [wangzhang/Qwen3.5-122B-A10B-abliterated](https://huggingface.co/wangzhang/Qwen3.5-122B-A10B-abliterated) \|
	\| Original Base Model \| [Qwen/Qwen3.5-122B-A10B](https://huggingface.co/Qwen/Qwen3.5-122B-A10B) \|
	\| Architecture \| Qwen3.5 MoE (256 routed experts, 10B active) \|
	\| Quantization \| FP8 \|
	\| Original Size \| 228 GB (BF16) \|
	\| Quantized Size \| 116 GB \|
	\| Format \| safetensors \|

	## Quantization Method

	This model was quantized from the abliterated BF16 checkpoint into FP8 format for more practical deployment while preserving compatibility with modern inference stacks.

	### What is Quantized

	\| Component \| Format \| Notes \|
	\|-----------\|--------\|-------\|
	\| Expert weights \| FP8 \| Quantized for reduced memory footprint \|
	\| Attention projections \| FP8 \| Quantized where supported \|
	\| Selected sensitive components \| BF16 \| Kept at higher precision where needed for stability \|
	\| Embeddings / norms / control tensors \| BF16 \| Preserved at full precision \|

	## Serving with vLLM

	This model is intended for vLLM-based inference and may require tensor parallelism depending on available memory.

	### Quick Start

	```bash
	# 1. Download the model
	huggingface-cli download bjk110/Qwen3.5-122B-A10B-abliterated-FP8

	# 2. Serve with vLLM
	vllm serve /path/to/model \
	--served-model-name Qwen3.5-122B-A10B-abliterated-FP8 \
	--tensor-parallel-size 2 \
	--max-model-len 131072 \
	--max-num-seqs 4 \
	--gpu-memory-utilization 0.90 \
	--trust-remote-code \
	--enable-prefix-caching \
	--enable-chunked-prefill \
	--reasoning-parser qwen3
	```

	### Docker Entrypoint Auto-Patch

	Add to the beginning of your entrypoint.sh:
	```bash
	if [ -f /patches/patch_qwen35_moe_text.py ]; then
	python3 /patches/patch_qwen35_moe_text.py \|\| true
	fi
	```

	Mount the patches volume in docker-compose.yml:
	```yaml
	volumes:
	- ./vllm_patches:/patches:ro
	```

	## What the Patch Does

	\| Issue \| Cause \| Fix \|
	\|-------\|-------\|-----\|
	\| `Qwen3_5MoeForCausalLM` not recognized \| Not in vLLM registry \| Registers TextOnlyShim class \|
	\| Hybrid cache page-size error \| Bug in text-only CausalLM path \| Reuses multimodal wrapper's cache-spec \|
	\| Vision encoder init failure \| Wrapper forces vision init \| Skips vision encoder \|
	\| TP2 block_k=128 error \| vision_config.hidden_size=1152 \| Injects dummy vision config \|

	The patch will become unnecessary once vLLM adds native support for `qwen3_5_moe_text`.

	## Hardware Requirements

	\| Config \| GPU Memory \| Notes \|
	\|--------\|-----------\|-------\|
	\| TP=1 \| ~115 GB \| Requires GB200 or similar \|
	\| TP=2 \| ~58 GB/GPU \| DGX Spark, H100×2, A100 80GB×2 \|
	\| TP=4 \| ~29 GB/GPU \| A100 40GB×4 \|

	## Base Model

	[wangzhang/Qwen3.5-122B-A10B-abliterated](https://huggingface.co/wangzhang/Qwen3.5-122B-A10B-abliterated) — uncensored via [Prometheus](https://github.com/wuwangzhang1216/prometheus) abliteration. Refusal rate 0.5% (1/200), KL divergence 0.0115.

	## License

	Follows the license of the base model.