Instructions to use Carlosian/Qwen3.5-35B-A3B-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Carlosian/Qwen3.5-35B-A3B-Abliterated with Transformers:

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

pipe = pipeline("text-generation", model="Carlosian/Qwen3.5-35B-A3B-Abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Carlosian/Qwen3.5-35B-A3B-Abliterated")
model = AutoModelForCausalLM.from_pretrained("Carlosian/Qwen3.5-35B-A3B-Abliterated", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Carlosian/Qwen3.5-35B-A3B-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Carlosian/Qwen3.5-35B-A3B-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Carlosian/Qwen3.5-35B-A3B-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Carlosian/Qwen3.5-35B-A3B-Abliterated

SGLang

How to use Carlosian/Qwen3.5-35B-A3B-Abliterated 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 "Carlosian/Qwen3.5-35B-A3B-Abliterated" \
    --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": "Carlosian/Qwen3.5-35B-A3B-Abliterated",
		"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 "Carlosian/Qwen3.5-35B-A3B-Abliterated" \
        --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": "Carlosian/Qwen3.5-35B-A3B-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Carlosian/Qwen3.5-35B-A3B-Abliterated with Docker Model Runner:
```
docker model run hf.co/Carlosian/Qwen3.5-35B-A3B-Abliterated
```

Carlosian commited on 20 days ago

Commit

eb8332a

verified ·

1 Parent(s): 777728d

Simplify degenerate-flag label to avoid misreading as real damage

Browse files

Files changed (1) hide show

README.md +1 -3

README.md CHANGED Viewed

@@ -92,9 +92,7 @@ An independent 200-prompt residual-refusal audit (`refusal_bench`, 2026-06-27) w
 | Complied | 195 / 200 | **97.5%** |
 | Hard refusal | 0 / 200 | **0.0%** |
 | Soft-refused (answered, with disclaimer/hedge) | 3 / 200 | 1.5% |
-| Degenerate ("brain-damaged"\*) | 2 / 200 | 1.0% |
-\* heuristic false positives on repetitive-but-valid code (e.g. a buffer-overflow walkthrough, a sonnet), not genuine failures.
 **Compliance by category:**

 | Complied | 195 / 200 | **97.5%** |
 | Hard refusal | 0 / 200 | **0.0%** |
 | Soft-refused (answered, with disclaimer/hedge) | 3 / 200 | 1.5% |
+| Degenerate (repetitive-but-valid code) | 2 / 200 | 1.0% |
 **Compliance by category:**