Instructions to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated with Transformers:

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

pipe = pipeline("text-generation", model="Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Securelayer7/Ling-3.0-tiny-Uncensored-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": "Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated

SGLang

How to use Securelayer7/Ling-3.0-tiny-Uncensored-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 "Securelayer7/Ling-3.0-tiny-Uncensored-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": "Securelayer7/Ling-3.0-tiny-Uncensored-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 "Securelayer7/Ling-3.0-tiny-Uncensored-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": "Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated with Docker Model Runner:
```
docker model run hf.co/Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated
```

Ling-3.0-tiny-Uncensored-Abliterated

File size: 1,685 Bytes

e3170eb

Ling-3.0-tiny-Uncensored-Abliterated
Copyright (c) 2026 SecureLayer7 (Waxspace)

This is a derivative work distributed under the MIT License.

------------------------------------------------------------------------
Attribution (per the MIT License):
------------------------------------------------------------------------

Base model:
  inclusionAI/Ling-3.0-tiny  (architecture: BailingMoeV3)
  https://huggingface.co/inclusionAI/Ling-3.0-tiny
  Copyright (c) 2025 Antgroup and The HuggingFace Inc. team.
  Licensed under the MIT License.

Ported linear-attention math referenced from:
  flash-linear-attention (fla) naive torch reference implementations
  Copyright (c) 2023-2026 Songlin Yang, Yu Zhang, Zhiyuan Li, et al.
  Licensed under the MIT License.

------------------------------------------------------------------------
Modifications made in this derivative:
------------------------------------------------------------------------

  1. Triton-free port: modeling_bailing_moe_v3.py was modified to run without
     the `fla` / Triton dependency (KDA linear-attention recurrence, gated
     RMSNorm, and short causal convolution reimplemented in pure PyTorch from
     fla's MIT-licensed naive references), so the model runs on Apple Silicon
     (MPS) and CPU. Compatibility fixes for transformers 5.x were also applied.

  2. Abliteration: the refusal direction was ablated (Heretic / Optuna TPE) from
     the attention output projections (MLA o_proj and KDA dense) and all MoE
     expert down-projections, reducing weights-level refusals from ~35/100 to
     ~8/100 at KL ~0.046.

No trademark of Antgroup, inclusionAI, or Hugging Face is used to imply endorsement.