Instructions to use SenseLLM/ReflectionCoder-CL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SenseLLM/ReflectionCoder-CL-7B with Transformers:

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

pipe = pipeline("text-generation", model="SenseLLM/ReflectionCoder-CL-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SenseLLM/ReflectionCoder-CL-7B")
model = AutoModelForCausalLM.from_pretrained("SenseLLM/ReflectionCoder-CL-7B")
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 SenseLLM/ReflectionCoder-CL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SenseLLM/ReflectionCoder-CL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SenseLLM/ReflectionCoder-CL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SenseLLM/ReflectionCoder-CL-7B

SGLang

How to use SenseLLM/ReflectionCoder-CL-7B 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 "SenseLLM/ReflectionCoder-CL-7B" \
    --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": "SenseLLM/ReflectionCoder-CL-7B",
		"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 "SenseLLM/ReflectionCoder-CL-7B" \
        --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": "SenseLLM/ReflectionCoder-CL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SenseLLM/ReflectionCoder-CL-7B with Docker Model Runner:
```
docker model run hf.co/SenseLLM/ReflectionCoder-CL-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation

📄 Paper • 🏠 Repo • 🤖 Models • 📚 Datasets

Introduction

ReflectionCoder is a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance. Please refer to our paper and repo for more details!

Models

Model	Checkpoint	Size	HumanEval (+)	MBPP (+)	License
ReflectionCoder-CL-7B	🤗 HF Link	7B	75.0 (68.9)	72.2 (61.4)	Llama2
ReflectionCoder-CL-34B	🤗 HF Link	34B	70.7 (66.5)	68.4 (56.6)	Llama2
ReflectionCoder-DS-6.7B	🤗 HF Link	6.7B	80.5 (74.4)	81.5 (69.6)	DeepSeek
ReflectionCoder-DS-33B	🤗 HF Link	33B	82.9 (76.8)	84.1 (72.0)	DeepSeek

Datasets

Dataset	Link	License
ReflectionSeq-GPT	🤗 HF Link	License
ReflectionSeq-DS	🤗 HF Link	License

How to Use

Chat Format

import torch
from transformers import pipeline

chat = [
    {"role": "user", "content": "<Your code instruction here>"}
]

generator = pipeline(
    model="SenseLLM/ReflectionCoder-CL-7B",
    task="text-generation",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

result = generator(chat, max_length=128, num_return_sequences=1)

print(result)

Please refer to our GitHub Repo for more technical details.

Citation

If you find this repo useful for your research, please kindly cite our paper:

@misc{ren2024reflectioncoder,
    title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation}, 
    author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
    year={2024},
    eprint={2405.17057},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}