Instructions to use tiny-random/deepseek-v4.1-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/deepseek-v4.1-flash with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/deepseek-v4.1-flash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tiny-random/deepseek-v4.1-flash", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/deepseek-v4.1-flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/deepseek-v4.1-flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/deepseek-v4.1-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/deepseek-v4.1-flash

SGLang

How to use tiny-random/deepseek-v4.1-flash 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 "tiny-random/deepseek-v4.1-flash" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/deepseek-v4.1-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "tiny-random/deepseek-v4.1-flash" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/deepseek-v4.1-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/deepseek-v4.1-flash with Docker Model Runner:
```
docker model run hf.co/tiny-random/deepseek-v4.1-flash
```

yujiepan commited on 8 days ago

Commit

2bb006c

verified ·

1 Parent(s): d83ea6c

Document tested SGLang environment

Browse files

Files changed (1) hide show

README.md +11 -11

README.md CHANGED Viewed

@@ -48,9 +48,8 @@ Note:
     expert counts, vocabulary rows, and 32-wide quantization blocks divide cleanly by 2.
     The online example enables the embedded three-stage DSpark/MTP draft explicitly.
-  - TP=2 with DSpark/MTP and CUDA Graph was validated on two NVIDIA H20 GPUs using
-    Python 3.12.13, CUDA 13.0, torch 2.14.1, SGLang
-    `0.5.23.dev384+g9ab74b851`, FlashInfer 0.7.2, and Transformers 5.19.0.
 | File path | Size |
 |------|------|
@@ -64,7 +63,7 @@ Note:
 ```bash
 # Requires an SGLang preview/main build with DeepSeek-V4.1 support.
 # Runs the target model and its embedded three-stage DSpark/MTP draft with TP=2.
-model_id=tiny-random/deepseek-v4.1-flash
 sglang serve \
   --model-path $model_id \
   --trust-remote-code \
@@ -98,7 +97,7 @@ from safetensors.torch import save_file
 from transformers import AutoTokenizer
 source_model_id = "deepseek-ai/DeepSeek-V4.1-Flash"
-save_folder = "/tmp/tiny-random/deepseek-v41-flash"
 source_revision = "314113f8a7dadb388215396ba5fbee2c610a04d6"
 implementation_model_id = "deepseek-ai/DeepSeek-V4.1-Flash-1017"
 implementation_revision = "1bae0e1088993b9b82de08f59de5be5a34e42170"
@@ -569,15 +568,16 @@ main()
 ### Test environment:
-- Pillow: 12.3.0
-- flashinfer-python: 0.6.15.post1
-- huggingface_hub: 1.27.0
-- numpy: 2.5.2
 - safetensors: 0.8.0
 - sympy: 1.14.0
 - tilelang: 0.1.12
-- torch: 2.13.0+cu126
-- transformers: 5.15.0.dev0
 ### Change log:

     expert counts, vocabulary rows, and 32-wide quantization blocks divide cleanly by 2.
     The online example enables the embedded three-stage DSpark/MTP draft explicitly.
+  - TP=2 with DSpark/MTP and CUDA Graph was validated on two NVIDIA H20 GPUs
+    using the versions listed under Test environment.
 | File path | Size |
 |------|------|
 ```bash
 # Requires an SGLang preview/main build with DeepSeek-V4.1 support.
 # Runs the target model and its embedded three-stage DSpark/MTP draft with TP=2.
+model_id=yujiepan/deepseek-v4.1-flash-tiny-random
 sglang serve \
   --model-path $model_id \
   --trust-remote-code \
 from transformers import AutoTokenizer
 source_model_id = "deepseek-ai/DeepSeek-V4.1-Flash"
+save_folder = "/tmp/yujiepan/deepseek-v41-flash-tiny-random"
 source_revision = "314113f8a7dadb388215396ba5fbee2c610a04d6"
 implementation_model_id = "deepseek-ai/DeepSeek-V4.1-Flash-1017"
 implementation_revision = "1bae0e1088993b9b82de08f59de5be5a34e42170"
 ### Test environment:
+- Pillow: 12.2.0
+- flashinfer-python: 0.7.2
+- huggingface_hub: 1.33.0
+- numpy: 2.3.5
 - safetensors: 0.8.0
+- sglang: 0.5.23.dev384+g9ab74b851
 - sympy: 1.14.0
 - tilelang: 0.1.12
+- torch: 2.14.1
+- transformers: 5.19.0
 ### Change log: