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")

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
```

Why are you faking DeepSeek-V4 with a 2B Qwen3 model?

by Shom012 - opened about 22 hours ago

Discussion

Shom012

about 22 hours ago

•

edited about 22 hours ago

You allegedly finetuned DeepSeek-V4 Flash but you didn't even bother to upload weights that resemble a DeepSeek-V4 model. You uploaded a measly 2B model with Qwen3 architecture. That's just pathetic.

Shom012

about 22 hours ago

Oh you also uploaded a lora adapter but that didn't even match the layer counts of DeepSeek-V4 flash.

imbue2025

Chunjiang Intelligence org about 20 hours ago

We sincerely apologize for the error we made while uploading the model! The new model will be uploaded shortly.

Shom012

about 18 hours ago

Care to explain the striking similarity between old adapter and the new one?

Old file: lora_adapters.safetensors
Occurrence: commit Update model at 2026-06-23 00:59:01 UTC
LFS SHA256: 99d4be206a9d304d8f08738606cc0dd933fe8c4e332f8d06ba11590fb2b1b2df
size: 278,964,848

New file: deepseek-v4-flash_adapters.safetensors
Occurrence: commit update at 2026-06-23 06:33:20 UTC
LFS SHA256: 99d4be206a9d304d8f08738606cc0dd933fe8c4e332f8d06ba11590fb2b1b2df
size: 278,964,848

A little reminder: the hidden size and module path need to match the original model for a lora to work. Obvious if you cared to train a real adapter.

imbue2025

Chunjiang Intelligence org about 17 hours ago

Fixed!

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