Instructions to use alexvumnov/yaml_completion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alexvumnov/yaml_completion with Transformers:

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

pipe = pipeline("text-generation", model="alexvumnov/yaml_completion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alexvumnov/yaml_completion")
model = AutoModelForCausalLM.from_pretrained("alexvumnov/yaml_completion")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alexvumnov/yaml_completion with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/alexvumnov/yaml_completion

SGLang

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

Docker Model Runner
How to use alexvumnov/yaml_completion with Docker Model Runner:
```
docker model run hf.co/alexvumnov/yaml_completion
```

yaml_completion / README.md

alexvumnov

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	---
	library_name: transformers
	license: mit
	language:
	- en
	base_model: Salesforce/codegen-350M-multi
	---

	# Model Card for Model ID

	Model finetuned to autocomplete for YAML files



	## Model Details

	### Model Description

	<!-- Provide a longer summary of what this model is. -->

	This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

	- Developed by: https://huggingface.co/alexvumnov
	- Model type: Autoregressive decoder only transformer (GPT2-based)
	- Language(s) (NLP): English, but mostly YAML
	- License: MIT
	- Finetuned from model [optional]: https://huggingface.co/Salesforce/codegen-350M-multi/tree/main


	## Uses

	Model expects a specific prompt format, so please use it for best performance

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

	model = AutoModelForCausalLM.from_pretrained("alexvumnov/yaml_completion")
	tokenizer = AutoTokenizer.from_pretrained("alexvumnov/yaml_completion", padding='left')

	prompt_format = """
	# Here's a yaml file to offer a completion for
	# Lines after the current one
	{text_after}
	# Lines before the current one
	{text_before}
	# Completion:
	"""

	input_prefix = """
	name: my_awesome_env
	dependencies:

	"""

	input_suffix = ""

	generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda')

	generator(prompt_format.format(text_after=input_suffix, text_before=input_prefix), max_new_tokens=64)

	# [{'generated_text': "\n# Here's a yaml file to offer a completion for\n# Lines after the current one\n\n# Lines before the current one\n\nname: my_awesome_env\ndependencies:\n\n\n# Completion:\n- deploy"}]
	```