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, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("kingabzpro/Phi-3.5-mini-instruct-Ecommerce-Text-Classification", trust_remote_code=True)
model = AutoModelForMultimodalLM.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 Settings

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
```

kingabzpro commited on Sep 24, 2024

Commit

49e97ab

verified ·

1 Parent(s): eb1a73d

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -2

README.md CHANGED Viewed

@@ -68,8 +68,10 @@ 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
@@ -80,9 +82,11 @@ Accuracy for label Clothing: 0.947
    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]

 Accuracy for label Household: 0.926
 Accuracy for label Books: 0.683
 Accuracy for label Clothing: 0.947
+```
 **Classification Report:**
+```bash
               precision    recall  f1-score   support
  Electronics       0.97      0.82      0.89        40
    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:**
+```bash
 [[33  6  1  0]
  [ 1 75  2  3]
  [ 0  3 28  2]