Instructions to use rmihaylov/gpt2-medium-bg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rmihaylov/gpt2-medium-bg with Transformers:

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

pipe = pipeline("text-generation", model="rmihaylov/gpt2-medium-bg", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rmihaylov/gpt2-medium-bg", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("rmihaylov/gpt2-medium-bg", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rmihaylov/gpt2-medium-bg with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rmihaylov/gpt2-medium-bg"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rmihaylov/gpt2-medium-bg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rmihaylov/gpt2-medium-bg

SGLang

How to use rmihaylov/gpt2-medium-bg 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 "rmihaylov/gpt2-medium-bg" \
    --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": "rmihaylov/gpt2-medium-bg",
		"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 "rmihaylov/gpt2-medium-bg" \
        --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": "rmihaylov/gpt2-medium-bg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rmihaylov/gpt2-medium-bg with Docker Model Runner:
```
docker model run hf.co/rmihaylov/gpt2-medium-bg
```

rmihaylov commited on Apr 16, 2022

Commit

3770f02

1 Parent(s): 5977a51

add tokenizer

Browse files

Files changed (5) hide show

added_tokens.json +1 -0
special_tokens_map.json +1 -0
spiece.model +3 -0
tokenizer.json +0 -0
tokenizer_config.json +1 -0

added_tokens.json ADDED Viewed

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1	+ {"[MASK]": 50260, "[SEP]": 50258, "<pad>": 50259, "<unk>": 50261, "[CLS]": 50257}

special_tokens_map.json ADDED Viewed

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1	+ {"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": "[MASK]"}

spiece.model ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b074bba94a7a59567fa70b3188dd7139746dad62d9c5c89580063ea806c06349
+size 1446766

tokenizer.json ADDED Viewed

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tokenizer_config.json ADDED Viewed

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+ {"do_lower_case": false, "remove_space": true, "keep_accents": true, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false, "__type": "AddedToken"}, "sp_model_kwargs": {}, "special_tokens_map_file": "/content/drive/MyDrive/ColabModels/GPT2_MEDIUM/pytorch_model/special_tokens_map.json", "name_or_path": "/content/tokenizer/", "tokenizer_class": "AlbertTokenizer"}