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
```

bjk110 commited on Mar 29

Commit

1e92560

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1 Parent(s): e80e65a

Update README.md

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@@ -1,55 +1,77 @@
 ---
-base_model: wangzhang/Qwen3.5-122B-A10B-abliterated
-pipeline_tag: text-generation
 tags:
 - qwen3.5
 - moe
 - fp8
 - quantized
 - abliterated
-- uncensored
-- block-wise-fp8
-license: apache-2.0
 ---
 # Qwen3.5-122B-A10B-abliterated-FP8
-**FP8 E4M3 block-wise quantized** version of [wangzhang/Qwen3.5-122B-A10B-abliterated](https://huggingface.co/wangzhang/Qwen3.5-122B-A10B-abliterated).
-Same checkpoint format as [Qwen/Qwen3.5-122B-A10B-FP8](https://huggingface.co/Qwen/Qwen3.5-122B-A10B-FP8).
 ## Model Details
-| | |
-|---|---|
-| Base Model | [wangzhang/Qwen3.5-122B-A10B-abliterated](https://huggingface.co/wangzhang/Qwen3.5-122B-A10B-abliterated) |
-| Quantization | FP8 E4M3, block-wise 128×128, `weight_scale_inv` (bfloat16) |
-| Checkpoint Size | **115 GB** (50% of original 228 GB) |
-| Active Parameters | ~10B (MoE, 10 of 256 experts active per token) |
-| Total Parameters | 122B |
-| Max Context | 32,768 tokens |
-| Uncensored | ✅ (abliterated, 0.5% refusal rate) |
-| Activation | Dynamic (not stored in checkpoint) |
-| BF16 Preserved | lm_head, embed_tokens, norms, conv1d, router gate, in_proj_a/b |
-## Usage
-### ⚠️ vLLM Patch Required
-This model uses the `qwen3_5_moe_text` (text-only MoE) architecture, which is not natively supported in vLLM v0.17–v0.18. You must apply the included `vllm_patches/patch_qwen35_moe_text.py` before serving.
-### Serving with vLLM
 ```bash
-# 1. Apply patch (run before vLLM process starts)
-python vllm_patches/patch_qwen35_moe_text.py
-# 2. Serve
-vllm serve /path/to/Qwen3.5-122B-A10B-abliterated-FP8 \
     --tensor-parallel-size 2 \
     --trust-remote-code \
     --enable-chunked-prefill \
-    --max-model-len 32768 \
     --reasoning-parser qwen3
 ```

 ---
+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
 ```