Instructions to use finnvoorhees/tiny-coder-prompt-completion-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use finnvoorhees/tiny-coder-prompt-completion-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="finnvoorhees/tiny-coder-prompt-completion-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("finnvoorhees/tiny-coder-prompt-completion-0.5B")
model = AutoModelForCausalLM.from_pretrained("finnvoorhees/tiny-coder-prompt-completion-0.5B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use finnvoorhees/tiny-coder-prompt-completion-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "finnvoorhees/tiny-coder-prompt-completion-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "finnvoorhees/tiny-coder-prompt-completion-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/finnvoorhees/tiny-coder-prompt-completion-0.5B

SGLang

How to use finnvoorhees/tiny-coder-prompt-completion-0.5B 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 "finnvoorhees/tiny-coder-prompt-completion-0.5B" \
    --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": "finnvoorhees/tiny-coder-prompt-completion-0.5B",
		"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 "finnvoorhees/tiny-coder-prompt-completion-0.5B" \
        --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": "finnvoorhees/tiny-coder-prompt-completion-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use finnvoorhees/tiny-coder-prompt-completion-0.5B with Docker Model Runner:
```
docker model run hf.co/finnvoorhees/tiny-coder-prompt-completion-0.5B
```

tiny-coder-prompt-completion-0.5B

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---
base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
library_name: transformers
model_name: tiny-coder-prompt-completion-0.5B
tags:
- generated_from_trainer
- trackio:https://huggingface.co/spaces/finnvoorhees/huggingface-static-ab58c9
- trl
- sft
- trackio
licence: license
---

# Model Card for tiny-coder-prompt-completion-0.5B

This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="finnvoorhees/tiny-coder-prompt-completion-0.5B", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

 



This model was trained with SFT.

### Framework versions

- TRL: 1.2.0
- Transformers: 5.6.2
- Pytorch: 2.11.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2

## Citations



Cite TRL as:
    
```bibtex
@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}
```