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 / config.json

sky-2002

End of training

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	{
	"_name_or_path": "bigcode/tiny_starcoder_py",
	"activation_function": "gelu_pytorch_tanh",
	"architectures": [
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	],
	"attention_softmax_in_fp32": true,
	"attn_pdrop": 0.1,
	"bos_token_id": 49152,
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	"n_head": 12,
	"n_inner": 3072,
	"n_layer": 20,
	"n_positions": 8192,
	"pad_key_length": true,
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	"summary_first_dropout": 0.1,
	"summary_proj_to_labels": true,
	"summary_type": "cls_index",
	"summary_use_proj": true,
	"torch_dtype": "float32",
	"transformers_version": "4.46.3",
	"use_cache": true,
	"validate_runner_input": true,
	"vocab_size": 49154
	}