Instructions to use kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification", 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("kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification

SGLang

How to use kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification 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 "kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification" \
    --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": "kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification",
		"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 "kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification" \
        --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": "kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification with Docker Model Runner:
```
docker model run hf.co/kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification
```

Phi-3.5-mini-instruct-Ecommerce-Text-Classification

This model is a fine-tuned version of microsoft/Phi-3.5-mini-instruct on an saurabhshahane/ecommerce-text-classification dataset.

Tutorial

Customize Phi-3.5-mini-instruct model to predict various Ecommerce Categories from the text.

Use with Transformers

from transformers import AutoTokenizer,AutoModelForCausalLM,pipeline
import torch

model_id = "kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
        model_id,
        return_dict=True,
        low_cpu_mem_usage=True,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True,
)

text = "Inalsa Dazzle Glass Top, 3 Burner Gas Stove with Rust Proof Powder Coated Body, Black Toughened Glass Top, 2 Medium and 1 Small High Efficiency Brass Burners, Aluminum Mixing Tubes, Powder Coated Body, Inbuilt Stainless Steel Drip Trays, 360 degree Swivel Nozzle,Bigger Legs to Facilitate Cleaning Under Cooktop"
prompt = f"""Classify the E-commerce text into Electronics, Household, Books and Clothing.
text: {text}
label: """.strip()

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipe(prompt, max_new_tokens=4, do_sample=True, temperature=0.1)

print(outputs[0]["generated_text"].split("label: ")[-1].strip())

# Household

Results

Accuracy: 0.860
Accuracy for label Electronics: 0.825
Accuracy for label Household: 0.926
Accuracy for label Books: 0.683
Accuracy for label Clothing: 0.947

Classification Report:

              precision    recall  f1-score   support

 Electronics       0.97      0.82      0.89        40
   Household       0.88      0.93      0.90        81
       Books       0.90      0.68      0.78        41
    Clothing       0.88      0.95      0.91        38

   micro avg       0.90      0.86      0.88       200
   macro avg       0.91      0.85      0.87       200
weighted avg       0.90      0.86      0.88       200

Confusion Matrix:

[[33  6  1  0]
 [ 1 75  2  3]
 [ 0  3 28  2]
 [ 0  1  0 36]]

Downloads last month: 5

Safetensors

Model size

4B params

Tensor type

F16

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