Instructions to use leonMW/DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leonMW/DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2 with Transformers:

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

pipe = pipeline("text-generation", model="leonMW/DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2")
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-1.5B-long-context-Staged-2")
model = AutoModelForCausalLM.from_pretrained("leonMW/DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2", 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-1.5B-long-context-Staged-2 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-1.5B-long-context-Staged-2"
# 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-1.5B-long-context-Staged-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leonMW/DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2

SGLang

How to use leonMW/DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2 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-1.5B-long-context-Staged-2" \
    --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-1.5B-long-context-Staged-2",
		"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-1.5B-long-context-Staged-2" \
        --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-1.5B-long-context-Staged-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use leonMW/DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2 with Docker Model Runner:
```
docker model run hf.co/leonMW/DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2
```

leonMW commited on Nov 14, 2025

Commit

17c0eeb

verified ·

1 Parent(s): f361a4a

Training in progress, epoch 1

Browse files

Files changed (4) hide show

README.md +2 -2
generation_config.json +3 -1
model.safetensors +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -4,8 +4,8 @@ library_name: transformers
 model_name: DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2
 tags:
 - generated_from_trainer
-- trl
 - grpo
 licence: license
 ---
@@ -27,7 +27,7 @@ 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/64o0xtz2)
 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).

 model_name: DeepSeek-R1-Distill-Qwen-1.5B-long-context-Staged-2
 tags:
 - generated_from_trainer
 - grpo
+- trl
 licence: license
 ---
 ## 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/iqcog22o)
 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).

generation_config.json CHANGED Viewed

@@ -2,7 +2,9 @@
   "_from_model_config": true,
   "bos_token_id": 151646,
   "do_sample": true,
-  "eos_token_id": 151643,
   "pad_token_id": 151643,
   "temperature": 0.6,
   "top_p": 0.95,

   "_from_model_config": true,
   "bos_token_id": 151646,
   "do_sample": true,
+  "eos_token_id": [
+    151643
+  ],
   "pad_token_id": 151643,
   "temperature": 0.6,
   "top_p": 0.95,

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