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

Libraries

How to use codeparrot/starcoder-conala with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use codeparrot/starcoder-conala with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/codeparrot/starcoder-conala

SGLang

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

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

starcoder-conala / README.md

ArmelR

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	---
	datasets:
	- codeparrot/conala-mined-curated
	pipeline_tag: text2text-generation
	---

	# Model Card for Starcoder-conala

	<!-- Provide a quick summary of what the model is/does. -->

	This model is an instruction-tuned version of ⭐️ StarCoder. The instruction dataset involved is [Conala-mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
	which was built by boostrapping by predicting the column rewritten_intent of the mined subset of the [CoNaLa corpus](https://huggingface.co/datasets/neulab/conala).

	## Usage

	<!-- 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
	- [Conala-mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
	- [Starcoder](https://huggingface.co/bigcode/starcoder)