skeskinen/TinyStories-GPT4
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How to use FrankL/storytellerLM-v0 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="FrankL/storytellerLM-v0") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("FrankL/storytellerLM-v0")
model = AutoModelForCausalLM.from_pretrained("FrankL/storytellerLM-v0")How to use FrankL/storytellerLM-v0 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrankL/storytellerLM-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "FrankL/storytellerLM-v0",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/FrankL/storytellerLM-v0
How to use FrankL/storytellerLM-v0 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "FrankL/storytellerLM-v0" \
--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": "FrankL/storytellerLM-v0",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "FrankL/storytellerLM-v0" \
--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": "FrankL/storytellerLM-v0",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use FrankL/storytellerLM-v0 with Docker Model Runner:
docker model run hf.co/FrankL/storytellerLM-v0
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
model = AutoModel.from_pretrained('FrankL/storytellerLM-v0', trust_remote_code=True, torch_dtype=torch.float16)
model = model.to(device='cuda')
tokenizer = AutoTokenizer.from_pretrained('FrankL/storytellerLM-v0', trust_remote_code=True)
def inference(
model: AutoModelForCausalLM,
tokenizer: AutoTokenizer,
input_text: str = "Once upon a time, ",
max_new_tokens: int = 16
):
inputs = tokenizer(input_text, return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
pad_token_id=tokenizer.eos_token_id,
max_new_tokens=max_new_tokens,
do_sample=True,
top_k=40,
top_p=0.95,
temperature=0.8
)
generated_text = tokenizer.decode(
outputs[0],
skip_special_tokens=True
)
# print(outputs)
print(generated_text)
inference(model, tokenizer)