Instructions to use Chunjiang-Intelligence/DeepSeek-v4-Fable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chunjiang-Intelligence/DeepSeek-v4-Fable with Transformers:

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

pipe = pipeline("text-generation", model="Chunjiang-Intelligence/DeepSeek-v4-Fable")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Chunjiang-Intelligence/DeepSeek-v4-Fable")
model = AutoModelForCausalLM.from_pretrained("Chunjiang-Intelligence/DeepSeek-v4-Fable", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Chunjiang-Intelligence/DeepSeek-v4-Fable with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chunjiang-Intelligence/DeepSeek-v4-Fable"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chunjiang-Intelligence/DeepSeek-v4-Fable",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Chunjiang-Intelligence/DeepSeek-v4-Fable

SGLang

How to use Chunjiang-Intelligence/DeepSeek-v4-Fable 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 "Chunjiang-Intelligence/DeepSeek-v4-Fable" \
    --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": "Chunjiang-Intelligence/DeepSeek-v4-Fable",
		"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 "Chunjiang-Intelligence/DeepSeek-v4-Fable" \
        --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": "Chunjiang-Intelligence/DeepSeek-v4-Fable",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Chunjiang-Intelligence/DeepSeek-v4-Fable with Docker Model Runner:
```
docker model run hf.co/Chunjiang-Intelligence/DeepSeek-v4-Fable
```

DeepSeek-v4-Fable / model-00034-of-00047.safetensors

nekocyrene

Upload folder using huggingface_hub

0d75152 verified about 2 months ago

Download

History Blame Contribute Delete

6.44 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:: b75f84470e7152c0282b8ec12502fa4a557092aab7de74737d4133aa8a21c039
Size of remote file:: 6.44 GB
SHA256:: 48d42a23a2080ad93aeddd2156ebcfd8e4d2999bb692c556cc0f67444a88ed47

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