Instructions to use TextCortex/codegen-350M-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TextCortex/codegen-350M-optimized with Transformers:

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

pipe = pipeline("text-generation", model="TextCortex/codegen-350M-optimized")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("TextCortex/codegen-350M-optimized", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TextCortex/codegen-350M-optimized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TextCortex/codegen-350M-optimized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TextCortex/codegen-350M-optimized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TextCortex/codegen-350M-optimized

SGLang

How to use TextCortex/codegen-350M-optimized 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 "TextCortex/codegen-350M-optimized" \
    --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": "TextCortex/codegen-350M-optimized",
		"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 "TextCortex/codegen-350M-optimized" \
        --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": "TextCortex/codegen-350M-optimized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TextCortex/codegen-350M-optimized with Docker Model Runner:
```
docker model run hf.co/TextCortex/codegen-350M-optimized
```

codegen-350M-optimized / README.md

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Update README.md (#1)

7033b80 almost 4 years ago

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	---
	license: bsd-3-clause
	---
	# CodeGen (CodeGen-Mono 350M)

	Clone of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) converted to ONNX and optimized.

	## Usage

	```python
	from transformers import AutoTokenizer
	from optimum.onnxruntime import ORTModelForCausalLM

	model = ORTModelForCausalLM.from_pretrained("TextCortex/codegen-350M-optimized")
	tokenizer = AutoTokenizer.from_pretrained("TextCortex/codegen-350M-optimized")

	text = "def hello_world():"
	input_ids = tokenizer(text, return_tensors="pt").input_ids
	generated_ids = model.generate(
	input_ids,
	max_length=64,
	temperature=0.1,
	num_return_sequences=1,
	early_stopping=True,
	)
	out = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
	print(out)
	```

	Refer to the original model for more details.