Instructions to use apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated with Transformers:

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

pipe = pipeline("image-text-to-text", model="apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated

SGLang

How to use apetersson/DeepSeek-V4-Flash-Vision-Exp-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 "apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated" \
    --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": "apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated",
		"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 "apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated" \
        --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": "apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated with Docker Model Runner:
```
docker model run hf.co/apetersson/DeepSeek-V4-Flash-Vision-Exp-Abliterated
```

DeepSeek-V4-Flash-Vision-Exp Abliterated

PREVIEW / DRAFT RELEASE. This upload contains the initial native abliterated checkpoint. Final behavioral testing, broader capability and safety validation, runtime validation, and quantized GGUF/MLX variants will follow. The recipe, metadata, and catalog layout may change before the final release.

This is a model catalog. The directly loadable native checkpoint lives in Reference-Native-FP8/; future GGUF or MLX profiles belong in descriptive sibling folders and must be registered in variants.json.

.
├── Reference-Native-FP8/   native mixed FP8/FP4 model (48 shards)
├── README.md               catalog and usage notes
├── NOTICE                  source and direction provenance
└── variants.json           machine-readable variant registry

The native checkpoint uses a conservative transferred rank-1 direction and changes only 33 attention output writers plus their FP8 scale payloads. No weights from the abandoned DeepSeek-V4-Flash-0731-Abliterated-Vision graft are used; only its useful catalog organization inspired this layout.

Status: weight-transfer and byte-boundary validated; behavior not locally validated. See the native folder's README and manifest for exact details.

Preview status

Completed for this draft:

all 48 native shards and 72,633 indexed tensors are present;
all 33 intended writer edits were deterministically replayed;
exactly 66 FP8 writer/scale payloads changed;
non-target payloads, vision modules, experts, routers, and MTP heads were verified against the official source;
source, direction, recipe, and checksum provenance are included.

Still planned before the final release:

end-to-end text and multimodal behavioral evaluation;
refusal, benign-quality, regression, and safety testing;
validation on compatible production inference runtimes;
calibrated GGUF and MLX quantized variants with profile-specific testing.

This preview should be treated as an experimental research artifact, not as a fully characterized or production-ready model.

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