Instructions to use Amama02/pinterest-personality-keywords-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Amama02/pinterest-personality-keywords-v3 with Transformers:

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

pipe = pipeline("text-generation", model="Amama02/pinterest-personality-keywords-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Amama02/pinterest-personality-keywords-v3")
model = AutoModelForSeq2SeqLM.from_pretrained("Amama02/pinterest-personality-keywords-v3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Amama02/pinterest-personality-keywords-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Amama02/pinterest-personality-keywords-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Amama02/pinterest-personality-keywords-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Amama02/pinterest-personality-keywords-v3

SGLang

How to use Amama02/pinterest-personality-keywords-v3 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 "Amama02/pinterest-personality-keywords-v3" \
    --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": "Amama02/pinterest-personality-keywords-v3",
		"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 "Amama02/pinterest-personality-keywords-v3" \
        --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": "Amama02/pinterest-personality-keywords-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Amama02/pinterest-personality-keywords-v3 with Docker Model Runner:
```
docker model run hf.co/Amama02/pinterest-personality-keywords-v3
```

Amama02 commited on Aug 7, 2025

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-## Uses
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 ---
+language: en
+license: apache-2.0
+base_model: google/flan-t5-base
+tags:
+- text2text-generation
+- pinterest
+- keywords
+- personality
+- fine-tuned
+- lora
+- flan-t5
 library_name: transformers
+pipeline_tag: text2text-generation
+widget:
+- text: "Generate Pinterest keywords for Cleopatra - Culture: Egyptian | Role: Royalty | Period: Ancient Egypt - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords"
+  example_title: "Cleopatra Keywords"
+- text: "Generate Pinterest keywords for Leonardo da Vinci - Culture: Italian | Role: Polymath | Period: Renaissance - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords"
+  example_title: "Leonardo da Vinci Keywords"
 ---
+# Pinterest Personality Keywords Generator
+🎨 **Fine-tuned FLAN-T5 model for generating Pinterest-optimized keywords for historical and fictional personalities.**
+This model was fine-tuned using LoRA (Low-Rank Adaptation) to generate visually appealing, searchable Pinterest keywords based on personality information.
+## 🚀 Quick Start
+### Using Transformers Pipeline
+```python
+from transformers import pipeline
+# Load the model
+generator = pipeline("text2text-generation", model="Amama02/pinterest-personality-keywords-v3")
+# Generate keywords
+input_text = "Generate Pinterest keywords for Marie Curie - Culture: Polish-French | Role: Scientist | Period: Early 20th Century - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords"
+result = generator(
+    input_text,
+    max_length=300,
+    num_beams=8,
+    temperature=0.9,
+    do_sample=True,
+    top_p=0.95,
+    repetition_penalty=2.0,
+    length_penalty=1.2,
+    no_repeat_ngram_size=2
+)
+print(result[0]['generated_text'])
+```
+### Using Direct Model Loading
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Amama02/pinterest-personality-keywords-v3")
+model = AutoModelForSeq2SeqLM.from_pretrained("Amama02/pinterest-personality-keywords-v3")
+# Prepare input
+input_text = "Generate Pinterest keywords for Frida Kahlo - Culture: Mexican | Role: Artist | Period: 20th Century - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords"
+# Tokenize and generate
+inputs = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True)
+outputs = model.generate(
+    **inputs,
+    max_length=300,
+    num_beams=8,
+    temperature=0.9,
+    do_sample=True,
+    top_p=0.95,
+    repetition_penalty=2.0,
+    length_penalty=1.2,
+    early_stopping=True,
+    no_repeat_ngram_size=2
+)
+keywords = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(keywords)
+```
+## 📝 Input Format
+The model expects input in this specific format:
+```
+Generate Pinterest keywords for [PERSONALITY_NAME] - Culture: [CULTURE] | Role: [ROLE] | Period: [TIME_PERIOD] | Bio: [BIOGRAPHY] - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords
+```
+### Required Fields:
+- **PERSONALITY_NAME**: Name of the person
+- **Culture**: Cultural background or nationality
+- **Role**: Profession, title, or main role
+- **Period**: Historical time period
+- **Bio**: (Optional) Brief biography
+## 🎯 Example Outputs
+| Input | Generated Keywords |
+|-------|-------------------|
+| **Cleopatra** (Egyptian Royalty, Ancient Egypt) | "Egyptian queen aesthetic, ancient Egypt fashion, Cleopatra makeup, pharaoh style, golden jewelry, Egyptian mythology, ancient beauty, royal Egyptian, hieroglyphics, Egyptian art" |
+| **Leonardo da Vinci** (Italian Polymath, Renaissance) | "Renaissance art, Italian genius, classical paintings, Renaissance fashion, vintage sketches, Italian Renaissance, Renaissance architecture, classical art history" |
+| **Marie Curie** (Polish-French Scientist, Early 20th Century) | "vintage science, female scientist aesthetic, laboratory vintage, early 1900s fashion, women in science, vintage academic, scientific discovery, vintage portraits" |
+## ⚙️ Generation Parameters
+The model is optimized with these generation settings:
+- **max_length**: 300
+- **num_beams**: 8
+- **temperature**: 0.9
+- **top_p**: 0.95
+- **repetition_penalty**: 2.0
+- **length_penalty**: 1.2
+- **no_repeat_ngram_size**: 2
+## 🔧 Technical Details
+- **Base Model**: google/flan-t5-base
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **LoRA Rank**: 16
+- **Target Modules**: ["q", "v", "k", "o", "wi", "wo"]
+- **Training Data**: Historical and fictional personalities dataset
+- **Task**: Seq2Seq text generation
+## 📊 Model Performance
+The model has been optimized for:
+- ✅ **Visual Keywords**: Generates terms that work well for image searches
+- ✅ **Pinterest Optimization**: Keywords tailored for Pinterest's search algorithm
+- ✅ **Cultural Sensitivity**: Respects cultural context and historical accuracy
+- ✅ **Diversity**: Produces varied and creative keyword combinations
+## 🚫 Limitations
+- Specifically designed for Pinterest keyword generation
+- May not perform well on other text generation tasks
+- Limited to personalities with sufficient historical/cultural context
+- Requires specific input format for optimal results