Text Generation
Transformers
Safetensors
English
phi3
LLM
token classification
nlp
safetensor
PyTorch
conversational
custom_code
text-generation-inference
Instructions to use ab-ai/PII-Model-Phi3-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ab-ai/PII-Model-Phi3-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ab-ai/PII-Model-Phi3-Mini", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ab-ai/PII-Model-Phi3-Mini", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("ab-ai/PII-Model-Phi3-Mini", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Local Apps Settings
- vLLM
How to use ab-ai/PII-Model-Phi3-Mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ab-ai/PII-Model-Phi3-Mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ab-ai/PII-Model-Phi3-Mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ab-ai/PII-Model-Phi3-Mini
- SGLang
How to use ab-ai/PII-Model-Phi3-Mini 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 "ab-ai/PII-Model-Phi3-Mini" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ab-ai/PII-Model-Phi3-Mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "ab-ai/PII-Model-Phi3-Mini" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ab-ai/PII-Model-Phi3-Mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ab-ai/PII-Model-Phi3-Mini with Docker Model Runner:
docker model run hf.co/ab-ai/PII-Model-Phi3-Mini
How to use this model?
#1
by shubh3ai - opened
I have been trying to load the model using the snippet below.
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
# Load tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(
"ab-ai/PII-Model-Phi3-Mini", token=token, trust_remote_code=True
)
model = AutoModelForTokenClassification.from_pretrained(
"ab-ai/PII-Model-Phi3-Mini", token=token, trust_remote_code=True
)
I am getting this error
ValueError: Unrecognized configuration class <class 'transformers_modules.microsoft.Phi-3-mini-4k-instruct.0a67737cc96d2554230f90338b163bc6380a2a85.configuration_phi3.Phi3Config'> for this kind of AutoModel: AutoModelForTokenClassification.
Model type should be one of AlbertConfig, BertConfig, BigBirdConfig, BioGptConfig, BloomConfig, BrosConfig, CamembertConfig, CanineConfig, ConvBertConfig, Data2VecTextConfig, DebertaConfig, DebertaV2Config, DistilBertConfig, ElectraConfig, ErnieConfig, ErnieMConfig, EsmConfig, FalconConfig, FlaubertConfig, FNetConfig, FunnelConfig, GemmaConfig, Gemma2Config, GPT2Config, GPT2Config, GPTBigCodeConfig, GPTNeoConfig, GPTNeoXConfig, IBertConfig, LayoutLMConfig, LayoutLMv2Config, LayoutLMv3Config, LiltConfig, LlamaConfig, LongformerConfig, LukeConfig, MarkupLMConfig, MegaConfig, MegatronBertConfig, MistralConfig, MixtralConfig, MobileBertConfig, MPNetConfig, MptConfig, MraConfig, MT5Config, NemotronConfig, NezhaConfig, NystromformerConfig, PersimmonConfig, PhiConfig, Phi3Config, QDQBertConfig, Qwen2Config, Qwen2MoeConfig, RemBertConfig, RobertaConfig, RobertaPreLayerNormConfig, RoCBertConfig, RoFormerConfig, SqueezeBertConfig, StableLmConfig, Starcoder2Config, T5Config, UMT5Config, XLMConfig, XLMRobertaConfig, XLMRobertaXLConfig, XLNetConfig, XmodConfig, YosoConfig.
I am using transformers version 4.45.2.
In config.json file, I can see that there is no support for token classification.