Instructions to use sky-2002/tiny-starcoder-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sky-2002/tiny-starcoder-ft with Transformers:

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

pipe = pipeline("text-generation", model="sky-2002/tiny-starcoder-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sky-2002/tiny-starcoder-ft")
model = AutoModelForCausalLM.from_pretrained("sky-2002/tiny-starcoder-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sky-2002/tiny-starcoder-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sky-2002/tiny-starcoder-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sky-2002/tiny-starcoder-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sky-2002/tiny-starcoder-ft

SGLang

How to use sky-2002/tiny-starcoder-ft 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 "sky-2002/tiny-starcoder-ft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sky-2002/tiny-starcoder-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "sky-2002/tiny-starcoder-ft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sky-2002/tiny-starcoder-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sky-2002/tiny-starcoder-ft with Docker Model Runner:
```
docker model run hf.co/sky-2002/tiny-starcoder-ft
```

tiny-starcoder-ft / README.md

sky-2002

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	---
	base_model: bigcode/tiny_starcoder_py
	library_name: transformers
	model_name: tiny-starcoder-ft
	tags:
	- generated_from_trainer
	- smol-course
	- module_1
	- code_generation
	- trl
	- sft
	licence: license
	---

	# Model Card for tiny-starcoder-ft

	This model is a fine-tuned version of [bigcode/tiny_starcoder_py](https://huggingface.co/bigcode/tiny_starcoder_py) using a samples from [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) dataset.
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	model_name = "sky-2002/tiny-starcoder-ft"
	model = AutoModelForCausalLM.from_pretrained(
	pretrained_model_name_or_path=model_name
	).to(device)
	tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)

	prompt = "Write python code to calculate sum of a list"

	# Format with template
	messages = [{"role": "user", "content": prompt}]
	formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)

	inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)

	outputs = model.generate(**inputs, max_new_tokens=100)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## Training procedure



	This model was trained with SFT.

	### Framework versions

	- TRL: 0.12.1
	- Transformers: 4.46.3
	- Pytorch: 2.5.1
	- Datasets: 3.1.0
	- Tokenizers: 0.20.3

	## Citations



	Cite TRL as:

	```bibtex
	@misc{vonwerra2022trl,
	title = {{TRL: Transformer Reinforcement Learning}},
	author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year = 2020,
	journal = {GitHub repository},
	publisher = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
	}
	```