Instructions to use HelpingAI/HAI-SER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HelpingAI/HAI-SER with Transformers:

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

pipe = pipeline("text-generation", model="HelpingAI/HAI-SER")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HAI-SER")
model = AutoModelForCausalLM.from_pretrained("HelpingAI/HAI-SER", 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 HelpingAI/HAI-SER with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HelpingAI/HAI-SER

SGLang

How to use HelpingAI/HAI-SER 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 "HelpingAI/HAI-SER" \
    --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": "HelpingAI/HAI-SER",
		"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 "HelpingAI/HAI-SER" \
        --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": "HelpingAI/HAI-SER",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HelpingAI/HAI-SER with Docker Model Runner:
```
docker model run hf.co/HelpingAI/HAI-SER
```

HAI-SER / README.md

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	---
	license: other
	license_name: helpingai
	license_link: https://helpingai.co/license
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- HelpingAI
	- SER
	- Emotional Reasoning
	- Conversational AI
	library_name: transformers
	---

	<div align="center">
	❤️ <span style="background: linear-gradient(45deg, #FF6347, #FFD700); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">HAI-SER</span>
	</div>

	<div align="center" style="display: flex; justify-content: center; gap: 4px;">
	<a href="https://github.com/HelpingAI"><img src="https://img.shields.io/badge/GitHub-Organization-blue.svg" alt="GitHub Organization"></a>
	<a href="https://huggingface.co/HelpingAI"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Organization-yellow" alt="Hugging Face"></a>
	<a href="https://helpingai.co/license"><img src="https://img.shields.io/badge/License-HelpingAI-green.svg" alt="Model License"></a>
	<a href="https://github.com/HelpingAI/community/discussions"><img src="https://img.shields.io/badge/Join-Community%20Discussion-blue?style=for-the-badge&logo=github" alt="Join Community Discussion"></a>
	</div>

	<div align="center">
	[📜 License](https://helpingai.co/license) \| [🌐 Website](https://helpingai.co)
	</div>

	<div align="center" style="display: flex; justify-content: center; gap: 4px;">
	<img src="https://img.shields.io/badge/Model%20Type-SER-ff6347" alt="Model Type">
	<img src="https://img.shields.io/badge/Task-Emotional%20Reasoning-blue" alt="Task">
	<img src="https://img.shields.io/badge/Version-v1.0-yellow" alt="Version">
	</div>

	## 🌟 About HAI-SER

	HAI-SER is HelpingAI's revolutionary Structured Emotional Reasoning (SER) model, crafted to redefine the emotional intelligence of AI. Unlike traditional models, HAI-SER goes beyond words—it understands emotions, breaks down mental states, and offers real, empathetic insights for human-AI interaction. 🚀

	### 💡 Core Features of HAI-SER

	The Structured Emotional Reasoning (SER) framework is built upon these key pillars:

	- Emotional Vibe Check – Reads emotional energy from conversations 🎭
	- Mind-State Analysis – Understands thoughts, moods, and mental shifts 🧠
	- Root Cause Deep-Dive – Identifies why emotions arise 🔍
	- Impact Check – Evaluates how emotions affect real-life actions 💥
	- Safety Check – Prioritizes user well-being 🚨
	- Healing Game Plan – Offers structured support for growth & recovery 💪
	- Growth Potential – Helps users evolve emotionally 📈
	- How to Approach – Guides users in communication & self-awareness 🤝

	## 🚀 Implementation

	### Load HAI-SER with Hugging Face Transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	# Load HAI-SER
	model = AutoModelForCausalLM.from_pretrained("HelpingAI/HAI-SER")
	tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HAI-SER")

	# Example usage
	chat = [
	{"role": "system", "content": "You are an emotionally intelligent AI assistant who always thinks step by step before responding."},
	{"role": "user", "content": "I feel really stressed out about my exams."}
	]

	inputs = tokenizer.apply_chat_template(
	chat,
	add_generation_prompt=True,
	return_tensors="pt"
	)

	outputs = model.generate(
	inputs,
	max_new_tokens=128,
	temperature=0.7,
	top_p=0.9,
	)

	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## ⚙️ Training Details

	### 🏋️ Training Data
	* Trained on a curated dataset emphasizing emotional intelligence, human psychology, and nuanced conversation.
	* Includes dialogues from mental health scenarios, coaching sessions, and empathetic responses.

	### 📌 Capabilities
	* Understands and analyzes emotions with high accuracy.
	* Provides tailored emotional insights instead of generic responses.
	* Capable of deep reasoning for emotional problem-solving.

	## ⚠️ Limitations

	* Still evolving – may not always capture deep emotions perfectly.
	* Not a replacement for professional therapy – designed to support, not diagnose.
	* Best used with human moderation in sensitive situations.

	## 📚 Citation

	```bibtex
	@misc{haiser2025,
	author = {HelpingAI Team},
	title = {HAI-SER: Structured Emotional Reasoning for Empathetic AI},
	year = {2025},
	publisher = {HelpingAI},
	journal = {HuggingFace},
	howpublished = {\url{https://huggingface.co/HelpingAI/HAI-SER}}
	}
	```

	---
	Created with ❤️ by HelpingAI

	[🌐 Website](https://helpingai.co) • [📜 License](https://helpingai.co/license) • [🤗 HuggingFace](https://huggingface.co/HelpingAI) • [💬 Discord](https://discord.gg/YweJwNqrnH)