Instructions to use LR-AI-Labs/tiny-universal-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LR-AI-Labs/tiny-universal-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LR-AI-Labs/tiny-universal-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LR-AI-Labs/tiny-universal-NER") model = AutoModelForCausalLM.from_pretrained("LR-AI-Labs/tiny-universal-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LR-AI-Labs/tiny-universal-NER with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LR-AI-Labs/tiny-universal-NER" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LR-AI-Labs/tiny-universal-NER", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LR-AI-Labs/tiny-universal-NER
- SGLang
How to use LR-AI-Labs/tiny-universal-NER 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 "LR-AI-Labs/tiny-universal-NER" \ --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": "LR-AI-Labs/tiny-universal-NER", "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 "LR-AI-Labs/tiny-universal-NER" \ --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": "LR-AI-Labs/tiny-universal-NER", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LR-AI-Labs/tiny-universal-NER with Docker Model Runner:
docker model run hf.co/LR-AI-Labs/tiny-universal-NER
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,65 @@
|
|
| 1 |
---
|
| 2 |
-
license:
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
+
license: cc-by-nc-4.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
datasets:
|
| 6 |
+
- Universal-NER/Pile-NER-type
|
| 7 |
---
|
| 8 |
+
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
# UniNER-7B-all
|
| 13 |
+
|
| 14 |
+
**Description**: This model is self fine-tuned from [TinyLLama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). It is trained on ChatGPT-generated [Pile-NER-type data](https://huggingface.co/datasets/Universal-NER/Pile-NER-type).
|
| 15 |
+
|
| 16 |
+
Check our [paper](https://arxiv.org/abs/2308.03279) for more information.
|
| 17 |
+
|
| 18 |
+
## Inference
|
| 19 |
+
# Install transformers from source - only needed for versions <= v4.34
|
| 20 |
+
# pip install git+https://github.com/huggingface/transformers.git
|
| 21 |
+
# pip install accelerate
|
| 22 |
+
|
| 23 |
+
import torch
|
| 24 |
+
from transformers import pipeline
|
| 25 |
+
|
| 26 |
+
pipe = pipeline("text-generation", model="/media/4TB_1/minhtt/Documents/test/axolotl/lora-out/merged",
|
| 27 |
+
torch_dtype=torch.bfloat16, device_map="auto")
|
| 28 |
+
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
|
| 29 |
+
messages = [
|
| 30 |
+
{
|
| 31 |
+
"role": "system",
|
| 32 |
+
"content": "A virtual assistant answers questions from a user based on the provided text.",
|
| 33 |
+
},
|
| 34 |
+
{
|
| 35 |
+
"role": "user",
|
| 36 |
+
"content": "Text: The American Bank Note Company Printing Plant is a repurposed complex of three interconnected buildings in the Hunts Point neighborhood of the Bronx in New York City. The innovative Kirby, Petit & Green design was built in 1909–1911 by the American Bank Note Company on land which had previously been part of Edward G. Faile's country estate. A wide variety of financial instruments were printed there; at one point, over five million documents were produced per day, including half the securities traded on the New York Stock Exchange."
|
| 37 |
+
},
|
| 38 |
+
{
|
| 39 |
+
"role": "assistant",
|
| 40 |
+
"content": "I've read this text."
|
| 41 |
+
},
|
| 42 |
+
{
|
| 43 |
+
"role": "user",
|
| 44 |
+
"content": "What describes location in the text?"
|
| 45 |
+
}
|
| 46 |
+
]
|
| 47 |
+
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
| 48 |
+
outputs = pipe(prompt, max_new_tokens=256, do_sample=False)
|
| 49 |
+
print(outputs[0]["generated_text"])
|
| 50 |
+
#<|system|>
|
| 51 |
+
#A virtual assistant answers questions from a user based on the provided text.</s>
|
| 52 |
+
#<|user|>
|
| 53 |
+
#Text: The American Bank Note Company Printing Plant is a repurposed complex of three interconnected buildings in the Hunts Point neighborhood of the Bronx in New York City. The innovative Kirby, Petit & Green design was built in 1909–1911 by the American Bank Note Company on land which had previously been part of Edward G. Faile's country estate. A wide variety of financial instruments were printed there; at one point, over five million documents were produced per day, including half the securities traded on the New York Stock Exchange.</s>
|
| 54 |
+
#<|assistant|>
|
| 55 |
+
#I've read this text.</s>
|
| 56 |
+
#<|user|>
|
| 57 |
+
#What describes location in the text?</s>
|
| 58 |
+
#<|assistant|>
|
| 59 |
+
#["Hunts Point", "Bronx", "New York City"]
|
| 60 |
+
|
| 61 |
+
### Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type.
|
| 62 |
+
|
| 63 |
+
## License
|
| 64 |
+
|
| 65 |
+
This model and its associated data are released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. They are primarily used for research purposes.
|