Text Generation
Transformers
Safetensors
llama
datadreamer
datadreamer-0.28.0
Synthetic
text-generation-inference
Instructions to use dagger-realms/dagger_to_narrative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dagger-realms/dagger_to_narrative with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dagger-realms/dagger_to_narrative")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dagger-realms/dagger_to_narrative") model = AutoModelForCausalLM.from_pretrained("dagger-realms/dagger_to_narrative", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dagger-realms/dagger_to_narrative with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dagger-realms/dagger_to_narrative" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dagger-realms/dagger_to_narrative", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dagger-realms/dagger_to_narrative
- SGLang
How to use dagger-realms/dagger_to_narrative 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 "dagger-realms/dagger_to_narrative" \ --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": "dagger-realms/dagger_to_narrative", "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 "dagger-realms/dagger_to_narrative" \ --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": "dagger-realms/dagger_to_narrative", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dagger-realms/dagger_to_narrative with Docker Model Runner:
docker model run hf.co/dagger-realms/dagger_to_narrative
File size: 2,238 Bytes
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"model_card": {
"Date & Time": "2024-04-07T01:56:59.324780",
"Model Card": [
"https://huggingface.co/LouisML/tinyllama_32k"
],
"License Information": [
"apache-2.0"
],
"Citation Information": [
"\n@inproceedings{Wolf_Transformers_State-of-the-Art_Natural_2020,\n author = {Wolf, Thomas and Debut, Lysandre and Sanh, Victor and Chaumond, Julien",
"\n@Misc{peft,\n title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods},\n author = {Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes"
]
},
"data_card": {
"Create Dataset of Locations": {
"Date & Time": "2024-04-06T10:17:45.009636"
},
"zipped(Create Dataset of Locations, Generate Fiction for Each Location (select_columns))": {
"Date & Time": "2024-04-06T15:17:35.786655"
},
"zipped(zipped(Create Dataset of Locations, Generate Fiction for Each Location (select_columns)), Clean Location Subset JSON)": {
"Date & Time": "2024-04-06T15:17:39.288866"
},
"zipped(zipped(Create Dataset of Locations, Generate Fiction for Each Location (select_columns)), Clean Location Subset JSON) (train split)": {
"Date & Time": "2024-04-06T15:17:39.562538"
}
},
"__version__": "0.28.0",
"datetime": "2024-04-07T00:04:51.052068",
"type": "TrainHFFineTune",
"name": "Train a DAGGER JSON => Fictional Story Model",
"version": 1.0,
"fingerprint": "38576c633b002fdf",
"req_versions": {
"dill": "0.3.8",
"sqlitedict": "2.1.0",
"torch": "2.2.2",
"numpy": "1.26.4",
"transformers": "4.39.3",
"datasets": "2.18.0",
"huggingface_hub": "0.22.2",
"accelerate": "0.28.0",
"peft": "0.10.0",
"tiktoken": "0.6.0",
"tokenizers": "0.15.2",
"openai": "1.16.2",
"ctransformers": "0.2.27",
"optimum": "1.18.0",
"bitsandbytes": "0.43.0",
"litellm": "1.31.14",
"trl": "0.8.1",
"setfit": "1.0.3"
},
"interpreter": "3.10.9 (main, Apr 17 2023, 21:32:03) [GCC 7.5.0]"
} |