Instructions to use m-a-p/OpenCodeInterpreter-CL-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-a-p/OpenCodeInterpreter-CL-13B with Transformers:

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

pipe = pipeline("text-generation", model="m-a-p/OpenCodeInterpreter-CL-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("m-a-p/OpenCodeInterpreter-CL-13B")
model = AutoModelForCausalLM.from_pretrained("m-a-p/OpenCodeInterpreter-CL-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use m-a-p/OpenCodeInterpreter-CL-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m-a-p/OpenCodeInterpreter-CL-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-a-p/OpenCodeInterpreter-CL-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/m-a-p/OpenCodeInterpreter-CL-13B

SGLang

How to use m-a-p/OpenCodeInterpreter-CL-13B 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 "m-a-p/OpenCodeInterpreter-CL-13B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-a-p/OpenCodeInterpreter-CL-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "m-a-p/OpenCodeInterpreter-CL-13B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-a-p/OpenCodeInterpreter-CL-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use m-a-p/OpenCodeInterpreter-CL-13B with Docker Model Runner:
```
docker model run hf.co/m-a-p/OpenCodeInterpreter-CL-13B
```

OpenCodeInterpreter-CL-13B / README.md

Anitaliu98

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	---
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- code
	---

	<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>

	<p align="center">
	<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
	</p>
	<p align="center">
	<a href="https://opencodeinterpreter.github.io/">[🏠Homepage]</a>
	\|
	<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[🛠️Code]</a>
	</p>
	<hr>

	## Introduction
	OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.

	## Model Usage
	### Inference

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM
	model_path="OpenCodeInterpreter-CL-13B"

	tokenizer = AutoTokenizer.from_pretrained(model_path)
	model = AutoModelForCausalLM.from_pretrained(
	model_path,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)
	model.eval()

	prompt = "Write a function to find the shared elements from the given two lists."
	inputs = tokenizer.apply_chat_template(
	[{'role': 'user', 'content': prompt }],
	return_tensors="pt"
	).to(model.device)
	outputs = model.generate(
	inputs,
	max_new_tokens=1024,
	do_sample=False,
	pad_token_id=tokenizer.eos_token_id,
	eos_token_id=tokenizer.eos_token_id,
	)
	print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
	```


	#### Contact

	If you have any inquiries, please feel free to raise an issue or reach out to us via email at: xiangyue.work@gmail.com, zhengtianyu0428@gmail.com.
	We're here to assist you!"