Text Generation
Transformers
Safetensors
deepseek_v41
auto-round
w4a16
int4
gptq
Mixture of Experts
deepseek
4-bit precision
Instructions to use lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound
- SGLang
How to use lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound 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 "lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound" \ --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": "lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound", "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 "lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound" \ --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": "lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound with Docker Model Runner:
docker model run hf.co/lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound
Download model-00007-of-00048.safetensors from lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound: direct link, hf CLI and curl.
- Browser
- Download file 8.19 GB
-
https://huggingface.co/lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound/resolve/main/model-00007-of-00048.safetensors
- Command line
-
hf download hf://lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound/model-00007-of-00048.safetensors
-
curl -L -o model-00007-of-00048.safetensors https://huggingface.co/lvkaokao/DeepSeek-V4.1-Flash-W4A16-Engram-AutoRound/resolve/main/model-00007-of-00048.safetensors
8.19 GB
- Xet hash:
- 859af18a842955d953cead7dd939d4cf34c069c787d2389d53d098ece54ef624
- Size of remote file:
- 8.19 GB
- SHA256:
- 79255467e966b8e4b2b77e38d859a6ad5779010d4441dd61c80988a6fc0583b3
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