Instructions to use AdamLucek/Phi-3-mini-EmoMarketing-DELLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdamLucek/Phi-3-mini-EmoMarketing-DELLA with Transformers:

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

pipe = pipeline("text-generation", model="AdamLucek/Phi-3-mini-EmoMarketing-DELLA", 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("AdamLucek/Phi-3-mini-EmoMarketing-DELLA", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AdamLucek/Phi-3-mini-EmoMarketing-DELLA", trust_remote_code=True, device_map="auto")
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 AdamLucek/Phi-3-mini-EmoMarketing-DELLA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdamLucek/Phi-3-mini-EmoMarketing-DELLA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamLucek/Phi-3-mini-EmoMarketing-DELLA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AdamLucek/Phi-3-mini-EmoMarketing-DELLA

SGLang

How to use AdamLucek/Phi-3-mini-EmoMarketing-DELLA 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 "AdamLucek/Phi-3-mini-EmoMarketing-DELLA" \
    --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": "AdamLucek/Phi-3-mini-EmoMarketing-DELLA",
		"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 "AdamLucek/Phi-3-mini-EmoMarketing-DELLA" \
        --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": "AdamLucek/Phi-3-mini-EmoMarketing-DELLA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AdamLucek/Phi-3-mini-EmoMarketing-DELLA with Docker Model Runner:
```
docker model run hf.co/AdamLucek/Phi-3-mini-EmoMarketing-DELLA
```

Phi-3-mini-EmoMarketing-DELLA

This is a model based on microsoft/Phi-3-mini-128k-instruct created by merging two fine-tuned versions together, one checkpoint for a domain-specific marketing fine tune, and one for emotional intelligence conversational setting.

🤏 Models Merged

This is a merge of pre-trained language models created using mergekit. This model was merged using the DELLA merge method using marketeam/Phi-Marketing as a base.

The following models were included in the merge:

marketeam/Phi-Marketing <- Base
OEvortex/EMO-phi-128k

🧩 Configuration

The following YAML configuration was used to produce this model:

models:
  - model: marketeam/Phi-Marketing
    parameters:
      weight: 1.0
  - model: OEvortex/EMO-phi-128k
    parameters:
      weight: 1.0
merge_method: della
base_model: marketeam/Phi-Marketing
parameters:
  density: 0.7
  lambda: 1.1
  epsilon: 0.2

💻 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("AdamLucek/Phi-3-mini-EmoMarketing-DELLA", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "AdamLucek/Phi-3-mini-EmoMarketing-DELLA",
    device_map="cuda",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

# Prepare the input text
input_text = "What are specific actionable ways to market products to technical software engineers with an emotional angle?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

# Generate the output
outputs = model.generate(
    **input_ids,
    max_new_tokens=256,
    pad_token_id=tokenizer.eos_token_id
)

# Decode and print the generated text
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

output

Hello there! 😊 I'd be happy to help you with that. When it comes to marketing products to technical software engineers with an emotional angle, there are several specific actionable ways to approach this. Here are a few ideas:

Highlight the impact of the product on the user's personal and professional life. Emphasize how the product can solve a specific problem or improve the user's overall experience, and how it can positively impact their emotions and well-being.

Use storytelling to create an emotional connection with the audience. Share real-life stories or testimonials from users who have experienced positive emotional outcomes as a result of using the product.

Focus on the user's passions and interests. Understand what motivates and inspires technical software engineers, and tailor the marketing message to resonate with their emotional drivers.

Use visual and sensory elements to evoke emotions. Incorporate imagery, colors, and sounds that align with the emotional tone you want to convey, and create a visually appealing and emotionally