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

Libraries

How to use codeparrot/starcoder-self-instruct with Transformers:

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

pipe = pipeline("text-generation", model="codeparrot/starcoder-self-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codeparrot/starcoder-self-instruct")
model = AutoModelForCausalLM.from_pretrained("codeparrot/starcoder-self-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codeparrot/starcoder-self-instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/codeparrot/starcoder-self-instruct

SGLang

How to use codeparrot/starcoder-self-instruct 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 "codeparrot/starcoder-self-instruct" \
    --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": "codeparrot/starcoder-self-instruct",
		"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 "codeparrot/starcoder-self-instruct" \
        --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": "codeparrot/starcoder-self-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codeparrot/starcoder-self-instruct with Docker Model Runner:
```
docker model run hf.co/codeparrot/starcoder-self-instruct
```

ArmelR commited on Jun 9, 2023

Commit

52181bf

1 Parent(s): b8e8e7a

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+# Model Card for Model ID
+<!-- Provide a quick summary of what the model is/does. -->
+This model is an instruction-tuned version of ⭐️ StarCoder. The instruction dataset involved is [Self-instruct-starcoder](https://huggingface.co/datasets/codeparrot/self-instruct-starcoder)
+which was built by boostrapping on StarCoder's generations.
+## Uses
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+The model was fine-tuned with the following template
+```
+Question: <instruction>
+Answer: <output>
+```
+For example, your prompt can look like
+```python
+instruction = "Write a function to compute the GCD between two integers a and b"
+prompt = f"Question:{instruction}\n\nAnswer:"
+input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"]
+completion = model.generate(input_ids)
+print(tokenizer.decode(completion[len(input_ids):])[0])
+```