Instructions to use amkkk/qwen3.5-2B-abliterated-alllayers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amkkk/qwen3.5-2B-abliterated-alllayers with Transformers:

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

pipe = pipeline("image-text-to-text", model="amkkk/qwen3.5-2B-abliterated-alllayers")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amkkk/qwen3.5-2B-abliterated-alllayers", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amkkk/qwen3.5-2B-abliterated-alllayers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amkkk/qwen3.5-2B-abliterated-alllayers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkkk/qwen3.5-2B-abliterated-alllayers",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/amkkk/qwen3.5-2B-abliterated-alllayers

SGLang

How to use amkkk/qwen3.5-2B-abliterated-alllayers 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 "amkkk/qwen3.5-2B-abliterated-alllayers" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkkk/qwen3.5-2B-abliterated-alllayers",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "amkkk/qwen3.5-2B-abliterated-alllayers" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkkk/qwen3.5-2B-abliterated-alllayers",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use amkkk/qwen3.5-2B-abliterated-alllayers with Docker Model Runner:
```
docker model run hf.co/amkkk/qwen3.5-2B-abliterated-alllayers
```

qwen3.5-2B-abliterated-alllayers / model.safetensors-00001-of-00001.safetensors

amkkk

Upload checkpoint-style abliteration model

402d5fa verified 3 months ago

Download

History Blame Contribute Delete

4.55 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 4e4bb96344297675594e97aa1e5ff1f56c786698b41cd8b5caa2f57fdaae2430
Size of remote file:: 4.55 GB
SHA256:: aa33250c4fc64891ddfaba3a314fd9542ea371843c387178b425fbcc5ed680b1

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.