DeepSeek-V4.1-Flash-1017-Genesis

Instructions to use LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis with Transformers:

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

pipe = pipeline("image-text-to-text", model="LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis

SGLang

How to use LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis 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 "LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis" \
    --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": "LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis",
		"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 "LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis" \
        --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": "LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis with Docker Model Runner:
```
docker model run hf.co/LuffyTheFox/DeepSeek-V4.1-Flash-1017-Genesis
```

DeepSeek-V4.1-Flash-1017-Genesis

⚡ If you like this Genesis LLM release you can donate to me via Hipolink or:

USDT (TRC20): TGa4KTwHfF6zDBsLUBEjd1f1KdeAFwYUks

USDT (ERC20): 0x93F4019E0aa85d8078F56B3D3176Fab5Dfa79924

USDT (SOL): BorkkyPDG4aRN2op8c5SQF5NithX38U4sh7wDAWQhYyX

and support future Genesis LLM development. Details below:

❤️ Support Genesis — Help Independent Open AI Development

If Genesis models have improved your local AI experience, consider supporting their continued development.

Genesis is an independent AI development project focused on exploring ways to improve the reliability, efficiency, and capabilities of open-weight language models.

Over nearly six months, I've been developing and refining Genesis with the help of AI, mathematical analysis, extensive experimentation, and limited computing resources.

My goal is simple: make powerful local AI more accessible, useful, and enjoyable for everyone.

I share my work with the community because I believe meaningful AI innovation should not be limited to organisations with enormous computing budgets.

🚀 What Your Support Helps Make Possible

Your voluntary contributions can help me:

🔬 Continue researching and improving Genesis tensor calibration and processing techniques.

⚡️ Experiment with faster, more efficient local AI models.

🧠 Explore improvements in coding, reasoning, tool use, and long-context reliability.

🖥 Access better computing resources for development, testing, and validation.

📦 Release more models, quantisations, configurations, and technical updates for the community.

🌍 Keep sharing my experiments and releases openly.

Every contribution, no matter how small, helps support the time, research, experimentation, and resources behind this project.

🤝 Can't Donate? You Can Still Help!

Downloading models, sharing your results, reporting bugs, contributing benchmarks, and recommending Genesis to other developers are all valuable ways to support the project.

The models are available regardless of whether you donate. Donations are completely optional.

Thank you for experimenting with Genesis, sharing feedback, and helping independent AI development move forward.

— LuffyTheFox 🦊

Independent research. Open models. Community-powered progress.

⚡ Why Genesis project exists? During training, ALL models don't just learn knowledge - they also accumulate random noise in their tensors. This noise builds up and creates something I call the Noise Gate - a fundamental barrier that stops LLM models from learning further and makes them unstable, verbose, and prone to hallucinations. My approach reduces this noise. It repairs the signal in tensors without touching the learned knowledge and gradient. The result is a model that consistent in performance, context clarity and following instructions, because it's no longer fighting its own internal chaos.

What is Genesis? Genesis is post training data regeneration and calibrarion algorythm for neural networks (LLM) in GGUF format that I made with AI help during almost half a year of development. It's optimized, architecture independent, works with any model in GGUF format and based on mathematical statistics. I don't train or finetune models, I repair purity of signal in them instead on Google Collab Free on Tesla T4 GPU via Python based on how models learns information. On first stage I scan ssm_conv1d tensors in model, they handle long context memory. I repair balance between heads in them. On second stage, I scan blocks in model via chunks via 3 parameters and pick best one that fits to weight distribution in tensor. Best picked chunk replaces zero chunks in broken tensor without touching learned structure in model. On third stage I scan model and detect noise in tensors via custom SVD. During scanning I exclude token_embd.weight, output.weight, 1D tensors, bias and norms. Then I reduce training noise in tensors via custom SVD with preserved training data, 99% of siginal and learned gradient.

Any questions?

Contact: luffythefox@mail.ru, azakharchenko92@gmail.com

My Telegram: @LuffyTheFox

Technical Report 👁️

Introduction

DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.

Benchmark	DS-V4.1-Flash-1017	DS-V4.1-Flash	K3	GLM-5.3	GPT-5.6 Sol	Opus 5
HLE w/o tools	42.6 (44.0†)	36.8 (39.1†)	43.5	42.0†	44.5	56.3
Terminal-Bench 4.0	42.3	31.2	12.6	37.9	39.9	51.8
DeepSWE v1.1	77.0	74.2	67.5	66.9	73.0	74.0
ProgramBench	29.1	20.3	17.5	19.0	23.0	37.0
NL2Repo-Bench	68.2	65.4	58.0	58.0	56.8	75.3
Automation-Bench v1.0.6	60.5	54.8	46.7	48.8	45.8	50.3
Agents’ Last Exam	35.4	31.8	27.6	28.5	26.7	28.6
Apex Bench v1.1	72.1	64.6	47.8	56.6	54.3	65.8
Chartography (w/tools)	83.0	78.9	68.1	—	79.9	84.0
BabyVision (w/tools)	92.5	89.6	85.7	—	88.9	94.1
ZeroBench-main (w/tools)	51.0	49.0	41.0	—	53.0	52.0

† denotes the text-only subset of HLE. We use the Claude Code harness for Terminal-Bench 4.0 and the Mini-SWE-V2 harness for DeepSWE v1.1.

Prompt Encoding

This release does not include a Jinja-format chat template. The encoding folder contains a self-contained Python reference implementation (encoding.py) with test cases for multi-turn conversations, tool calling, thinking mode, numeric reasoning effort, mid-conversation system messages, and interleaved image content.

For production use, we additionally release deepseek-recipe, a set of Rust libraries with Python bindings that provides the same prompt format as a maintained, protocol-aware toolkit. It converts Messages, Chat Completions, and Responses API requests into the Conversation format, encodes them into DeepSeek V4 and V4.1 prompts or token IDs, and parses model output back into complete or streamed responses — covering thinking, tool calls, images, and generation settings. Model inference, tool execution, and HTTP transport are left to the caller.

Minimal Inference

Please refer to the inference folder for instructions on weight conversion and running inference locally.

Recommended sampling parameters:

Parameter	Value
`temperature`	1.0
`top_p`	1.0
`context_window`	1M tokens
`max_tokens`	≥ 256K

License

This repository and the model weights are licensed under the MIT License.

Citation

@misc{deepseekai2026deepseekv41flash,
      title={DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression},
      author={DeepSeek-AI},
      year={2026},
}

Contact

If you have any questions, please raise an issue or contact us at service@deepseek.com.

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Safetensors

Model size

763B params

Tensor type

BF16

F32

F8_E4M3