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

Libraries

How to use sambanovasystems/starcoder-toolbench with Transformers:

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

pipe = pipeline("text-generation", model="sambanovasystems/starcoder-toolbench")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/starcoder-toolbench")
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/starcoder-toolbench", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sambanovasystems/starcoder-toolbench with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sambanovasystems/starcoder-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/starcoder-toolbench",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sambanovasystems/starcoder-toolbench

SGLang

How to use sambanovasystems/starcoder-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/starcoder-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/starcoder-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/starcoder-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/starcoder-toolbench",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sambanovasystems/starcoder-toolbench with Docker Model Runner:
```
docker model run hf.co/sambanovasystems/starcoder-toolbench
```

qiantong-xu commited on May 29, 2023

Commit

3e9c897

1 Parent(s): ffdc02f

Update README.md

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@@ -119,5 +119,12 @@ We would like to express our gratitude to the great work done in [StarCoder: may
 ## Cite starcoder-toolbench
 ```
-TBD
 ```

 ## Cite starcoder-toolbench
 ```
+@misc{xu2023tool,
+      title={On the Tool Manipulation Capability of Open-source Large Language Models},
+      author={Qiantong Xu and Fenglu Hong and Bo Li and Changran Hu and Zhengyu Chen and Jian Zhang},
+      year={2023},
+      eprint={2305.16504},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
 ```