Instructions to use helenai/Salesforce-codegen2-1B-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use helenai/Salesforce-codegen2-1B-ov with Transformers:

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

pipe = pipeline("text-generation", model="helenai/Salesforce-codegen2-1B-ov", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("helenai/Salesforce-codegen2-1B-ov", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("helenai/Salesforce-codegen2-1B-ov", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use helenai/Salesforce-codegen2-1B-ov with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "helenai/Salesforce-codegen2-1B-ov"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "helenai/Salesforce-codegen2-1B-ov",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/helenai/Salesforce-codegen2-1B-ov

SGLang

How to use helenai/Salesforce-codegen2-1B-ov 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 "helenai/Salesforce-codegen2-1B-ov" \
    --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": "helenai/Salesforce-codegen2-1B-ov",
		"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 "helenai/Salesforce-codegen2-1B-ov" \
        --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": "helenai/Salesforce-codegen2-1B-ov",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use helenai/Salesforce-codegen2-1B-ov with Docker Model Runner:
```
docker model run hf.co/helenai/Salesforce-codegen2-1B-ov
```

Salesforce/codegen2-1B

This is the Salesforce/codegen2-1B model converted to OpenVINO, for accelerated inference.

An example of how to do inference on this model:

from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("helenai/Salesforce-codegen2-1B-ov")

model = OVModelForCausalLM.from_pretrained("helenai/Salesforce-codegen2-1B-ov")

# Try the version with quantized model weights by changing the line above to:
# model = OVModelForCausalLM.from_pretrained("helenai/Salesforce-codegen2-1B-ov", revision="compressed_weights")

text = "def hello_world():"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

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