Instructions to use fredzzp/open-dcoder-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fredzzp/open-dcoder-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="fredzzp/open-dcoder-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fredzzp/open-dcoder-0.5B")
model = AutoModelForCausalLM.from_pretrained("fredzzp/open-dcoder-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 fredzzp/open-dcoder-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fredzzp/open-dcoder-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": "fredzzp/open-dcoder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fredzzp/open-dcoder-0.5B

SGLang

How to use fredzzp/open-dcoder-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 "fredzzp/open-dcoder-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": "fredzzp/open-dcoder-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 "fredzzp/open-dcoder-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": "fredzzp/open-dcoder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fredzzp/open-dcoder-0.5B with Docker Model Runner:
```
docker model run hf.co/fredzzp/open-dcoder-0.5B
```

fredzzp commited on Aug 5, 2025

Commit

a657eab

verified ·

1 Parent(s): 612b51f

Initial model upload with self-contained custom code

Browse files

Files changed (1) hide show

modeling_qwen2.py +0 -5

modeling_qwen2.py CHANGED Viewed

@@ -470,10 +470,6 @@ class Qwen2RotaryEmbedding(nn.Module):
         sin = sin * self.attention_scaling
         return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-@add_start_docstrings(
-    "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
-    QWEN2_START_DOCSTRING,
-)
 class Qwen2PreTrainedModel(PreTrainedModel):
     # ... (class unchanged)
     config_class = Qwen2Config
@@ -654,7 +650,6 @@ class Qwen2ForCausalLM(Qwen2PreTrainedModel, MDMGenerationMixin):
     def get_decoder(self):
         return self.model
-    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,

         sin = sin * self.attention_scaling
         return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
 class Qwen2PreTrainedModel(PreTrainedModel):
     # ... (class unchanged)
     config_class = Qwen2Config
     def get_decoder(self):
         return self.model
     @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,