Instructions to use leonMW/DeepSeek-R1-Distill-Qwen-14B-S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leonMW/DeepSeek-R1-Distill-Qwen-14B-S with Transformers:

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

pipe = pipeline("text-generation", model="leonMW/DeepSeek-R1-Distill-Qwen-14B-S")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("leonMW/DeepSeek-R1-Distill-Qwen-14B-S")
model = AutoModelForCausalLM.from_pretrained("leonMW/DeepSeek-R1-Distill-Qwen-14B-S", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use leonMW/DeepSeek-R1-Distill-Qwen-14B-S with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leonMW/DeepSeek-R1-Distill-Qwen-14B-S"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leonMW/DeepSeek-R1-Distill-Qwen-14B-S",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leonMW/DeepSeek-R1-Distill-Qwen-14B-S

SGLang

How to use leonMW/DeepSeek-R1-Distill-Qwen-14B-S 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 "leonMW/DeepSeek-R1-Distill-Qwen-14B-S" \
    --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": "leonMW/DeepSeek-R1-Distill-Qwen-14B-S",
		"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 "leonMW/DeepSeek-R1-Distill-Qwen-14B-S" \
        --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": "leonMW/DeepSeek-R1-Distill-Qwen-14B-S",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use leonMW/DeepSeek-R1-Distill-Qwen-14B-S with Docker Model Runner:
```
docker model run hf.co/leonMW/DeepSeek-R1-Distill-Qwen-14B-S
```

leonMW commited on Oct 8, 2025

Commit

0301bc1

verified ·

1 Parent(s): 2770afd

Training in progress, epoch 1

Browse files

Files changed (10) hide show

README.md +2 -3
generation_config.json +3 -1
model-00001-of-00006.safetensors +1 -1
model-00002-of-00006.safetensors +1 -1
model-00003-of-00006.safetensors +1 -1
model-00004-of-00006.safetensors +1 -1
model-00005-of-00006.safetensors +1 -1
model-00006-of-00006.safetensors +1 -1
tokenizer_config.json +4 -0
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -1,5 +1,4 @@
 ---
-base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
 library_name: transformers
 model_name: DeepSeek-R1-Distill-Qwen-14B-S
 tags:
@@ -11,7 +10,7 @@ licence: license
 # Model Card for DeepSeek-R1-Distill-Qwen-14B-S
-This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start
@@ -27,7 +26,7 @@ print(output["generated_text"])
 ## Training procedure
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/leonwenderoth-tu-darmstadt/huggingface/runs/iu6e5zo1)
 This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).

 ---
 library_name: transformers
 model_name: DeepSeek-R1-Distill-Qwen-14B-S
 tags:
 # Model Card for DeepSeek-R1-Distill-Qwen-14B-S
+This model is a fine-tuned version of [None](https://huggingface.co/None).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start
 ## Training procedure
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/leonwenderoth-tu-darmstadt/huggingface/runs/u947p8om)
 This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).

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