Instructions to use sambanovasystems/codegen-16B-mono-toolbench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sambanovasystems/codegen-16B-mono-toolbench with Transformers:

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

pipe = pipeline("text-generation", model="sambanovasystems/codegen-16B-mono-toolbench")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/codegen-16B-mono-toolbench")
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/codegen-16B-mono-toolbench", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sambanovasystems/codegen-16B-mono-toolbench with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sambanovasystems/codegen-16B-mono-toolbench"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sambanovasystems/codegen-16B-mono-toolbench",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sambanovasystems/codegen-16B-mono-toolbench

SGLang

How to use sambanovasystems/codegen-16B-mono-toolbench 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 "sambanovasystems/codegen-16B-mono-toolbench" \
    --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": "sambanovasystems/codegen-16B-mono-toolbench",
		"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 "sambanovasystems/codegen-16B-mono-toolbench" \
        --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": "sambanovasystems/codegen-16B-mono-toolbench",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sambanovasystems/codegen-16B-mono-toolbench with Docker Model Runner:
```
docker model run hf.co/sambanovasystems/codegen-16B-mono-toolbench
```

hongfenglu commited on May 25, 2023

Commit

37eb3d2

1 Parent(s): 90c2e6d

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@@ -126,6 +126,7 @@ We trained codegen-16b-mono-toolbench on 4 80GB A100 gpu's. We started from [cod
 ## Acknowledgment
 ## Cite codegen-16b-mono-toolbench
 ```

 ## Acknowledgment
+We would like to express our gratitude to the great work done in [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://arxiv.org/abs/2203.13474)
 ## Cite codegen-16b-mono-toolbench
 ```