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")

# 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")

Notebooks
Google Colab
Kaggle
Local Apps

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

0e23fe1

1 Parent(s): 6d25ac1

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@@ -24,7 +24,7 @@ codgen-16B-mono-toolbench is a 16 billion parameter model used for api based act
 <!-- Provide the basic links for the model. -->
 - **Paper**: [Link]
-- **Github**: [https://github.com/sambanova/toolbench]
 ## Uses

 <!-- Provide the basic links for the model. -->
 - **Paper**: [Link]
+- **Github**: [link](https://github.com/sambanova/toolbench)
 ## Uses