Instructions to use FrankL/storytellerLM-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use FrankL/storytellerLM-v0 with vLLM:
Install from pip and serve model
# 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 }'Use Docker
docker model run hf.co/FrankL/storytellerLM-v0
- SGLang
How to use FrankL/storytellerLM-v0 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 "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 }'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 "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 Model Runner
How to use FrankL/storytellerLM-v0 with Docker Model Runner:
docker model run hf.co/FrankL/storytellerLM-v0
Update README.md
Browse files
README.md
CHANGED
|
@@ -20,6 +20,8 @@ This is the model card of a 🤗 transformers model that has been pushed on the
|
|
| 20 |
|
| 21 |
|
| 22 |
### Direct Use
|
|
|
|
|
|
|
| 23 |
model = AutoModel.from_pretrained('FrankL/storytellerLM-v0', trust_remote_code=True, torch_dtype=torch.float16)
|
| 24 |
model = model.to(device='cuda')
|
| 25 |
|
|
@@ -47,4 +49,5 @@ def inference(
|
|
| 47 |
# print(outputs)
|
| 48 |
print(generated_text)
|
| 49 |
|
| 50 |
-
inference(model, tokenizer)
|
|
|
|
|
|
| 20 |
|
| 21 |
|
| 22 |
### Direct Use
|
| 23 |
+
|
| 24 |
+
```python
|
| 25 |
model = AutoModel.from_pretrained('FrankL/storytellerLM-v0', trust_remote_code=True, torch_dtype=torch.float16)
|
| 26 |
model = model.to(device='cuda')
|
| 27 |
|
|
|
|
| 49 |
# print(outputs)
|
| 50 |
print(generated_text)
|
| 51 |
|
| 52 |
+
inference(model, tokenizer)
|
| 53 |
+
```
|