Instructions to use Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2 with Transformers:

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

pipe = pipeline("text-generation", model="Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2")
model = AutoModelForCausalLM.from_pretrained("Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2

SGLang

How to use Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2 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 "Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2" \
    --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": "Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2",
		"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 "Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2" \
        --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": "Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2 with Docker Model Runner:
```
docker model run hf.co/Loke-60000/DeepSeek-V4-Flash-Vision-Exp-Abliterated-EXL3-K2.2-D2
```

DeepSeek V4 Flash Vision-Exp, abliterated, EXL3 K2.2/D2

wrldsuksgo2mars/DeepSeek-V4-Flash-Vision-Exp-EXL3-K2.2-D2-v1 with the refusal edit from apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated applied: the 33 layers.10..42.attn.wo_b tensors were replaced with the abliterated ones.

42 tok/s on code, 30 tok/s on prose, at a 500k context window with dSpark on a GB10.

Credits: DeepSeek, wrldsuksgo2mars, apetersson, drowzeys. MIT.