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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

starcoder-self-instruct / README.md

ArmelR

Update README.md

74400b4 about 3 years ago

preview code

Raw

History Blame Contribute Delete

1.85 kB


	---
	datasets:
	- codeparrot/self-instruct-starcoder
	pipeline_tag: text2text-generation
	metrics:
	- code_eval
	library_name: transformers
	tags:
	- code
	model-index:
	- name: StarCoder-SelfInstruct
	results:
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: InstructHumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.391
	verified: false
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.346
	verified: false
	---


	# Model Card for Self-instruct-starcoder

	<!-- 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

	<!-- 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>
	```
	If you have your model and tokenizer loaded, you can use the following code to make the model generate the right output to a given instruction

	```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, max_length=200)
	print(tokenizer.batch_decode(completion[:,input_ids.shape[1]:])[0])
	```

	## More information

	For additional information, check
	- [self-intruct-starcoder](https://huggingface.co/codeparrot/self-instruct-starcoder)
	- [starcoder](https://huggingface.co/bigcode/starcoder)