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

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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.