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

Libraries

How to use unknownCode/IslandBoyRepo with Transformers:

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

pipe = pipeline("text-generation", model="unknownCode/IslandBoyRepo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unknownCode/IslandBoyRepo")
model = AutoModelForCausalLM.from_pretrained("unknownCode/IslandBoyRepo", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use unknownCode/IslandBoyRepo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/unknownCode/IslandBoyRepo

SGLang

How to use unknownCode/IslandBoyRepo 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 "unknownCode/IslandBoyRepo" \
    --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": "unknownCode/IslandBoyRepo",
		"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 "unknownCode/IslandBoyRepo" \
        --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": "unknownCode/IslandBoyRepo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

IslandBoyRepo

File size: 1,006 Bytes

---
language: en
license: mit
tags:
  - conversational
  - gpt
  - chatbot
  - finetune
model_name: my-dialoGPT-model
finetuned_from: microsoft/DialoGPT-small
datasets:
  - custom
---

# My DialoGPT Model

This is a fine-tuned version of `microsoft/DialoGPT-small` on custom data about Dominica.

## Model Details

- **Model Name**: DialoGPT-small
- **Training Data**: Custom dataset about Dominica
- **Evaluation**: Achieved `eval_loss` of 12.85

## Usage

To use this model, you can load it as follows:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "unknownCode/IslandBoyRepo"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
input_text = "What is the capital of Dominica?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
```