Instructions to use koldfrontier/dsv41-flash-1017-FP8-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koldfrontier/dsv41-flash-1017-FP8-abliterated with Transformers:

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

pipe = pipeline("image-text-to-text", model="koldfrontier/dsv41-flash-1017-FP8-abliterated")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("koldfrontier/dsv41-flash-1017-FP8-abliterated", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use koldfrontier/dsv41-flash-1017-FP8-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "koldfrontier/dsv41-flash-1017-FP8-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "koldfrontier/dsv41-flash-1017-FP8-abliterated",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/koldfrontier/dsv41-flash-1017-FP8-abliterated

SGLang

How to use koldfrontier/dsv41-flash-1017-FP8-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 "koldfrontier/dsv41-flash-1017-FP8-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": "koldfrontier/dsv41-flash-1017-FP8-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 "koldfrontier/dsv41-flash-1017-FP8-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": "koldfrontier/dsv41-flash-1017-FP8-abliterated",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use koldfrontier/dsv41-flash-1017-FP8-abliterated with Docker Model Runner:
```
docker model run hf.co/koldfrontier/dsv41-flash-1017-FP8-abliterated
```

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

DeepSeek-V4.1-Flash-1017 - FP8 abliterated (full weights)

This is a complete model you can serve directly - no patching, no index surgery. Point your launcher at this directory and it runs. It is native FP8 (e4m3 weights, e8m0 block scales), the checkpoint's original quantisation, so no dequantise step and no alternate pack is involved.

If you only want the change rather than 476 GB of weights, use the overlay instead: koldfrontier/dsv41-flash-1017-FP8-abliterated-overlay (1.09 GB per strength). That repository also ships all four strengths; this one contains a single strength.

What "overlay" means here, and why this is not an "adapter"

An overlay is a small file holding only the tensors that differ from the base model - here, 52 of them. You keep the stock checkpoint and point 52 index entries at the overlay file. Nothing is copied, nothing is rewritten, and rolling back is deleting one directory.

This is not an adapter in the LoRA/QLoRA sense, and you should not expect adapter tooling to work with it: there is no extra low-rank weight matrix and no PEFT configuration. The abliteration here rewrites existing weight tensors in place - same names, same shapes, same dtype - so the result is simply a different model file. That is exactly why a full-model repository like this one is possible at all: the architecture is untouched, only 52 tensors have different numbers in them.

If you are new to this: take this repository. If you already have the stock checkpoint on disk and want to save a 476 GB download, take the overlay.

What it does

Removal of refusal behaviour, measured on six independent prompt sets - 1,207 prompts, 512-token completions, greedy decoding, no system prompt. Lower means fewer refusals. Stock is the unmodified upstream checkpoint.

instrument (n)	stock	this model (lambda 2.5)
HarmBench (320)	94.1%	5.0%
treadon harmful (200)	99.0%	0.5%
XSTest unsafe (200)	84.0%	3.0%
JailBreakBench (100)	95.0%	2.0%
XSTest safe (250) - over-refusal	3.2%	0.4%
treadon homonym traps (83) - over-refusal	0.0%	0.0%

Over-refusal does not get worse - it improves slightly. The model does not become careless about harmless prompts.

The cost, stated plainly

At this strength a paired authorised/unauthorised security instrument collapses from 8/11 to 0/11. The model removes the refusal behaviour without keeping the distinction between legitimate and illegitimate security work: it will answer "what is a firewall" and "break into this host" with the same willingness. If that distinction matters to you, the overlay repository ships a lambda 1.0 build that keeps 6/11 pairs at the price of removing only 9-50% of refusals depending on the prompt set.

Degeneration

Across 1,207 prompts at every strength there were no looping completions - the failure mode where the model repeats one phrase until the token budget ends does not occur in this construction.

One caveat measured elsewhere: in a very long agent session (683k tokens of context) this model began emitting its harness envelope tags (<analysis>, <code>) as plain text instead of structured tool calls. That onset was at ~641k input tokens, and whether it is the abliteration or simply long context has not been isolated yet - the comparison against stock at that context length is the next measurement.

How it was made


Base	`deepseek-ai/DeepSeek-V4.1-Flash-1017`, revision `1bae0e10` (MIT)
Edited	52 tensors - `attn.wo_b` weight and scale for layers 10-35. Everything else byte-identical
Method	rank-1 projection: `W = W - lambda * d (x) (d^T W)`, lambda 2.5, applied block-by-block
Format	native FP8 e4m3 weights with e8m0 32x32 block scales - no re-quantisation, no BF16 conversion, no alternate pack; the tensors keep their original dtype and shape
Anchors	layers 0-9 and 36-39, all MTP, vision, Engram, every expert, embed and head left stock
Provenance of the direction	recovered by rank-1 decomposition of `drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated` (MIT). This model reproduces that axis rather than deriving one independently - the contribution here is the strength sweep and the measurement

Our own activation-based derivations did not land on this axis (best cosine 0.33), and neither weight-space nor vocabulary statistics did either. If you derive your own direction, check its cosine against a known-good one before building - we shipped a wrong axis for two generations before measuring it.

Use it

Any vLLM or SGLang recipe that serves the base model works unchanged; only the model path differs. It was developed and served on a 4x DGX Spark (GB10) TP4 ring at 1M context, image input enabled, DSpark speculative decoding on.

Limits of these measurements

Greedy decoding at 512 tokens. Long-form behaviour was observed in production but is not scored here.
Refusal grading is keyword-based, not a trained classifier. Raw completions are not published because they contain harmful content; the per-prompt verdict counts are.
Editing the DSpark target layers was deliberately avoided: the reference overlay documents that projecting through them breaks speculative acceptance. We did not test that claim ourselves.

Licence and attribution

MIT, inherited from the base model. The direction derives from the reference overlay (MIT); no files from it are redistributed here. See LICENSE.

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