Instructions to use hf-internal-testing/tiny-random-XGLMForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-XGLMForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="hf-internal-testing/tiny-random-XGLMForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XGLMForCausalLM")
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-XGLMForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hf-internal-testing/tiny-random-XGLMForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hf-internal-testing/tiny-random-XGLMForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-XGLMForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-XGLMForCausalLM

SGLang

How to use hf-internal-testing/tiny-random-XGLMForCausalLM 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 "hf-internal-testing/tiny-random-XGLMForCausalLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-XGLMForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "hf-internal-testing/tiny-random-XGLMForCausalLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-XGLMForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-XGLMForCausalLM with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-XGLMForCausalLM
```

tiny-random-XGLMForCausalLM

File size: 517 Bytes

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  "additional_special_tokens": [
    "<madeupword0>",
    "<madeupword1>",
    "<madeupword2>",
    "<madeupword3>",
    "<madeupword4>",
    "<madeupword5>",
    "<madeupword6>"
  ],
  "bos_token": "<s>",
  "cls_token": "<s>",
  "eos_token": "</s>",
  "model_max_length": 512,
  "pad_token": "<pad>",
  "sep_token": "</s>",
  "sp_model_kwargs": {},
  "special_tokens_map_file": "hf_models/xglm-564M/special_tokens_map.json",
  "tokenizer_class": "XGLMTokenizer",
  "tokenizer_file": null,
  "unk_token": "<unk>"
}