Instructions to use raul111204/gpt-neo-125m-xsum-raul3-c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raul111204/gpt-neo-125m-xsum-raul3-c with Transformers:

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

pipe = pipeline("text-generation", model="raul111204/gpt-neo-125m-xsum-raul3-c")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("raul111204/gpt-neo-125m-xsum-raul3-c", device_map="auto")

PEFT
How to use raul111204/gpt-neo-125m-xsum-raul3-c with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raul111204/gpt-neo-125m-xsum-raul3-c with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raul111204/gpt-neo-125m-xsum-raul3-c"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raul111204/gpt-neo-125m-xsum-raul3-c",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/raul111204/gpt-neo-125m-xsum-raul3-c

SGLang

How to use raul111204/gpt-neo-125m-xsum-raul3-c 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 "raul111204/gpt-neo-125m-xsum-raul3-c" \
    --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": "raul111204/gpt-neo-125m-xsum-raul3-c",
		"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 "raul111204/gpt-neo-125m-xsum-raul3-c" \
        --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": "raul111204/gpt-neo-125m-xsum-raul3-c",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use raul111204/gpt-neo-125m-xsum-raul3-c with Docker Model Runner:
```
docker model run hf.co/raul111204/gpt-neo-125m-xsum-raul3-c
```

gpt-neo-125m-xsum-raul3-c / tokenizer_config.json

raul111204

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	{
	"add_bos_token": false,
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"50256": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"bos_token": "<\|endoftext\|>",
	"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<\|im_start\|>' + message['role'] + '\n' + message['content'] + '<\|im_end\|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\n' }}{% endif %}",
	"clean_up_tokenization_spaces": true,
	"eos_token": "<\|endoftext\|>",
	"errors": "replace",
	"extra_special_tokens": {},
	"model_max_length": 2048,
	"pad_token": "<\|endoftext\|>",
	"tokenizer_class": "GPT2Tokenizer",
	"unk_token": "<\|endoftext\|>"
	}