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 21 days ago

Commit

5bf5dae

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@@ -22,7 +22,7 @@ tags:
 # Qwen3.5-35B-A3B — Abliterated
-An **abliterated** (refusal-direction–removed) variant of the **Qwen3.5-35B-A3B** sparse Mixture-of-Experts model (~35B total parameters, ~3B active per token). The model's trained *instruction-refusal* behavior has been removed through a white-box weight edit, with **capability and coherence preserved** It is released as a research artifact for studying refusal mechanisms in MoE models, red-teaming, and alignment research.
 This is the **Stage-1 abliterated base** of the **[Nous-Heretic](#roadmap)** pipeline — a reproducible, honestly-benchmarked uncensoring of open LLMs. A direction-locked, post-trained variant is a planned follow-up (see **Roadmap**).

 # Qwen3.5-35B-A3B — Abliterated
+An **abliterated** (refusal-direction–removed) variant of the **Qwen3.5-35B-A3B** sparse Mixture-of-Experts model (~35B total parameters, ~3B active per token). The model's trained *instruction-refusal* behavior has been removed through a white-box weight edit, with **capability and coherence preserved!** It is released as a research artifact for studying refusal mechanisms in MoE models, red-teaming, and alignment research.
 This is the **Stage-1 abliterated base** of the **[Nous-Heretic](#roadmap)** pipeline — a reproducible, honestly-benchmarked uncensoring of open LLMs. A direction-locked, post-trained variant is a planned follow-up (see **Roadmap**).