SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX

Instructions to use Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX with Transformers:

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

pipe = pipeline("text-generation", model="Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX")
model = AutoModelForCausalLM.from_pretrained("Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX

SGLang

How to use Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX 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 "Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX" \
    --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": "Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX",
		"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 "Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX" \
        --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": "Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX with Docker Model Runner:
```
docker model run hf.co/Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX
```

SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX / model-00030-of-00048.safetensors

Jiunsong

Duplicate from deepseek-ai/DeepSeek-V4-Flash-0731

1cadec7 14 days ago

Download

History Blame Contribute Delete

3.59 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:: d7493a3cd53dbcd0dd6240373f25c5da4e893df0ae9ba3a2803668484040f3df
Size of remote file:: 3.59 GB
SHA256:: 9ed3c317bf967d32133ad3a068ee4c56aae9784bc8b7da694482437f37dc1782

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