Instructions to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ with Transformers:

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

pipe = pipeline("text-generation", model="OsaurusAI/DeepSeek-V4-Flash-JANGTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OsaurusAI/DeepSeek-V4-Flash-JANGTQ")
model = AutoModelForCausalLM.from_pretrained("OsaurusAI/DeepSeek-V4-Flash-JANGTQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OsaurusAI/DeepSeek-V4-Flash-JANGTQ

SGLang

How to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ 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 "OsaurusAI/DeepSeek-V4-Flash-JANGTQ" \
    --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": "OsaurusAI/DeepSeek-V4-Flash-JANGTQ",
		"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 "OsaurusAI/DeepSeek-V4-Flash-JANGTQ" \
        --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": "OsaurusAI/DeepSeek-V4-Flash-JANGTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ with Docker Model Runner:
```
docker model run hf.co/OsaurusAI/DeepSeek-V4-Flash-JANGTQ
```

Osaurus-AI commited on 1 day ago

Commit

8f542e3

verified ·

1 Parent(s): cec83e9

Upload encoding/tests/test_input_2.json with huggingface_hub

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Files changed (1) hide show

encoding/tests/test_input_2.json +24 -0

encoding/tests/test_input_2.json ADDED Viewed

	@@ -0,0 +1,24 @@

+[
+  {
+    "role": "system",
+    "content": "You are a helpful assistant."
+  },
+  {
+    "role": "user",
+    "content": "Hello"
+  },
+  {
+    "role": "assistant",
+    "reasoning_content": "The user said hello, I should greet back.",
+    "content": "Hi there! How can I help you?"
+  },
+  {
+    "role": "user",
+    "content": "What is the capital of France?"
+  },
+  {
+    "role": "assistant",
+    "reasoning_content": "The user asks about the capital of France. It is Paris.",
+    "content": "The capital of France is Paris."
+  }
+]