Instructions to use semcoder/semcoder_1030 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use semcoder/semcoder_1030 with Transformers:

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

pipe = pipeline("text-generation", model="semcoder/semcoder_1030")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("semcoder/semcoder_1030")
model = AutoModelForCausalLM.from_pretrained("semcoder/semcoder_1030")
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

vLLM

How to use semcoder/semcoder_1030 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "semcoder/semcoder_1030"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "semcoder/semcoder_1030",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/semcoder/semcoder_1030

SGLang

How to use semcoder/semcoder_1030 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 "semcoder/semcoder_1030" \
    --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": "semcoder/semcoder_1030",
		"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 "semcoder/semcoder_1030" \
        --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": "semcoder/semcoder_1030",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use semcoder/semcoder_1030 with Docker Model Runner:
```
docker model run hf.co/semcoder/semcoder_1030
```

semcoder_1030 / README.md

Robin Ding

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	---
	license: other
	library_name: transformers
	license_name: deepseek
	license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL
	pipeline_tag: text-generation
	---
	# 🤔 SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning

	> Refer to our GitHub repo [ARiSE-Lab/SemCoder](https://github.com/ARiSE-Lab/SemCoder/) for detailed introduction to SemCoder!

	## Model Details


	Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library.

	```python
	from transformers import pipeline
	import torch

	generator = pipeline(
	model="semcoder/semcoder_1030",
	task="text-generation",
	torch_dtype=torch.float16,
	device_map="auto",
	)

	# Generate Code

	CODEGEN_REQUEST = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable <Code> according to <NL_Description>

	<NL_Description>
	{desc}

	<Code>
	"""
	desc = """You are tasked with implementing a Python class that simulates a simple version of a "To-Do List" application. The class should have the following functionalities:
	1. Add a new task to the to-do list.
	2. Mark a task as completed.
	3. Display all tasks in the to-do list.
	4. Display only the incomplete tasks in the to-do list.
	"""

	prompt = CODEGEN_REQUEST.format(desc=desc)
	result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
	code = result[0]["generated_text"].split("```python")[1].split("```")[0]
	print(code)

	# Understand Code with Monologues

	FWD_MNL_REQUEST = """Simulate the Execution: You are given a Python function and an assertion containing a function input. Complete the assertion containing the execution output corresponding to the given input in [ANSWER] and [/ANSWER] tags.
	{code}
	"""

	tests = """
	todo_list = ToDoList()
	todo_list.add_task("Buy groceries")
	todo_list.add_task("Complete assignment")
	todo_list.mark_completed("Buy groceries")
	assert todo_list.tasks == ???
	"""
	code += tests
	prompt = FWD_MNL_REQUEST.format(code=code)
	result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
	print(result[0]["generated_text"])
	```

	## Citation

	```bibtex
	@article{ding2024semcoder,
	title={SemCoder: Training Code Language Models with Comprehensive Semantics},
	author={Yangruibo Ding and Jinjun Peng and Marcus J. Min and Gail Kaiser and Junfeng Yang and Baishakhi Ray},
	journal={arXiv preprint arXiv:2406.01006},
	year={2024}
	}
	```

	## Important Note

	SemCoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets. SemCoder will not compete with OpenAI's commercial products.