Instructions to use alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B")
model = AutoModelForCausalLM.from_pretrained("alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B

SGLang

How to use alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B 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 "alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B" \
    --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": "alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B",
		"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 "alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B" \
        --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": "alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B with Docker Model Runner:
```
docker model run hf.co/alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B
```

alamios commited on Mar 5, 2025

Commit

9eea5a1

verified ·

1 Parent(s): 4235257

Update README.md

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README.md +29 -29

README.md CHANGED Viewed

@@ -1,30 +1,30 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-Coder-0.5B
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- code
-- qwen
-- qwen2.5
-- qwen-coder
-- codeqwen
-- deepseek
----
-# DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B
-**Updated**
-This model is trained on CODE outputs of <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B">deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</a> and is meant to be used only as draft model for speculative decoding.
-It's specifically intended for users of 3090/4090, allowing you to run the DeepSeek-R1-Distill-Qwen-32B-Q4_K_M GGUF version with 16k context and speeding up generation without sacrificing more context length or model quality.
-# Data info
-The data consists of code tasks collected from various datasets. It has been trained for 2 epochs on 2.5k unique examples, for a total of 7.6 million tokens per epoch.
 Since data generation was done using spare GPU time, I may publish a further trained version later.

+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-0.5B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+- qwen
+- qwen2.5
+- qwen-coder
+- codeqwen
+- deepseek
+---
+# DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B
+**Updated to v1**
+This model is trained on CODE outputs of <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B">deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</a> and is meant to be used only as draft model for speculative decoding.
+It's specifically intended for users of 3090/4090, allowing you to run the DeepSeek-R1-Distill-Qwen-32B-Q4_K_M GGUF version with 16k context and speeding up generation without sacrificing more context length or model quality.
+# Data info
+The data consists of code tasks collected from various datasets. It has been trained for 2 epochs on 2.5k unique examples, for a total of 7.6 million tokens per epoch.
 Since data generation was done using spare GPU time, I may publish a further trained version later.