Instructions to use HelpingAI/HelpingAI2.5-5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HelpingAI/HelpingAI2.5-5B with Transformers:

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

pipe = pipeline("text-generation", model="HelpingAI/HelpingAI2.5-5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HelpingAI2.5-5B")
model = AutoModelForCausalLM.from_pretrained("HelpingAI/HelpingAI2.5-5B")
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]:]))

llama-cpp-python

How to use HelpingAI/HelpingAI2.5-5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HelpingAI/HelpingAI2.5-5B",
	filename="q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use HelpingAI/HelpingAI2.5-5B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M

Use Docker

docker model run hf.co/HelpingAI/HelpingAI2.5-5B:Q4_K_M

LM Studio
Jan

vLLM

How to use HelpingAI/HelpingAI2.5-5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HelpingAI/HelpingAI2.5-5B"
# 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/HelpingAI2.5-5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HelpingAI/HelpingAI2.5-5B:Q4_K_M

SGLang

How to use HelpingAI/HelpingAI2.5-5B 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/HelpingAI2.5-5B" \
    --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/HelpingAI2.5-5B",
		"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/HelpingAI2.5-5B" \
        --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/HelpingAI2.5-5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use HelpingAI/HelpingAI2.5-5B with Ollama:
```
ollama run hf.co/HelpingAI/HelpingAI2.5-5B:Q4_K_M
```

Unsloth Studio new

How to use HelpingAI/HelpingAI2.5-5B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HelpingAI/HelpingAI2.5-5B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HelpingAI/HelpingAI2.5-5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HelpingAI/HelpingAI2.5-5B to start chatting

Pi new

How to use HelpingAI/HelpingAI2.5-5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "HelpingAI/HelpingAI2.5-5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use HelpingAI/HelpingAI2.5-5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf HelpingAI/HelpingAI2.5-5B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default HelpingAI/HelpingAI2.5-5B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use HelpingAI/HelpingAI2.5-5B with Docker Model Runner:
```
docker model run hf.co/HelpingAI/HelpingAI2.5-5B:Q4_K_M
```

Lemonade

How to use HelpingAI/HelpingAI2.5-5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HelpingAI/HelpingAI2.5-5B:Q4_K_M

Run and chat with the model

lemonade run user.HelpingAI2.5-5B-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🤖 HelpingAI2.5-2B

A Revolutionary Emotionally Intelligent Language Model

📜 License | 🌐 Website

🌟 Model Overview

HelpingAI2.5-2B is a compact yet powerful language model specifically designed for emotionally intelligent conversations and human-centric interactions.

🎯 Key Highlights

Architecture: 2B parameter transformer-based model
Training Focus: Emotional intelligence and empathetic responses
Emotion Score: Achieves 94.28 on standardized emotional intelligence tests
Deployment: Optimized for efficient deployment on consumer hardware

💻 Implementation

Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the HelpingAI2.5-2B model
model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI2.5-2B")
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI2.5-2B")

# Define the chat input
chat = [
    { "role": "system", "content": "You are HelpingAI, an emotional AI. Always answer my questions in the HelpingAI style." },
    { "role": "user", "content": "GIVE ME YOUR INTRO" }
]

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

# Generate text
outputs = model.generate(
    inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)

response = outputs[0][inputs.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

GGUF Implementation

from webscout.Local import *
model_path = download_model("OEvortex/HelpingAI2.5-2B", "q4_k_m.gguf", token=None)
model = Model(model_path, n_gpu_layers=0, context_length=4096)

thread = Thread(model, format=helpingai2)
# print(thread.send("hi")) #send a single msg to ai

thread.interact() # interact with the model in terminal

🎯 Training Details

Methodology

Base Training
- Datasets: SentimentSynth + EmotionalIntelligence-1M + HelpingAI2.0-150M + HelpingAIemo (152.5M)
Emotional Intelligence Training
- Supervised Fine-tuning on emotional dialogue
- Reinforcement Learning with HelpingAI2.0-7B model
- Constitutional AI training for ethical guidelines
Optimization
- Mixed-precision training
- Gradient checkpointing
- Dynamic attention patterns

Dataset Composition

Dataset Type	Rows used for training	Purpose
Emotional Dialogues	12.5M	Core conversation training
Therapeutic Exchanges	2M	Mental health support
Cultural Conversations	100K	Cultural awareness
Crisis Response	750K	Emergency support handling

📈 Use Cases & Examples

Primary Applications

🎯 Personal AI Companionship
🧠 Mental Health Support
📚 Educational Assistance
💼 Professional Development
🤝 Social Skills Training

Example Interactions

User: I've been feeling really anxious about my upcoming job interview. Any advice?

HelpingAI: That must be super frustrating and stressful for you! 🌼 When you're unsure what you did wrong, it's hard to know how to fix it.

Give them space: 🌿 Even if you're worried about what you said, give your friend some time to process their feelings. One week is a lot of time for them to work through what you said.
Reflect on your convo: 🤔 Try to remember your last few messages and see if you can spot anything that might have caused the awkwardness. Was it something you said or did?
Apologize for being cautious: 🌻 Write a message expressing your concern that you might have said something wrong and ask if you can talk it out. Let them know you're not trying to blame them but you want to clear the air.
Keep it low-key: 🌺 When you do talk to them, try to keep the convo casual and relaxed. Avoid putting too much pressure on the convo or expecting a specific response.
Listen more, talk less: 👂 When you talk to them, focus on listening to their perspective. Try to understand where they're coming from rather than just wanting to explain your side.
Give them a chance: 🎈 Everyone heals differently. Give your friend the space and time they need to feel better before expecting them to respond.
Know that you can't control how they feel: 🔮 You did your best, and that's all you can do. Just hope that they'll forgive you for whatever you might have done.

Remember, it's not easy to have these kind of convos. Take care of yourself and be patient. You got this! ✊ Want some specific questions to practice with? Just let me know! I've got your back! 🤗

User: My friend hasn't been responding to my messages for a week. I think I might have said something wrong, but I'm not sure what.

HelpingAI: It's tough when you're not sure what you did wrong, fam. 😓 But hey, don't sweat it too much. Just shoot 'em a simple "Hey, what's up?" or "I got you, homie. Just checking in." 🤗

And if you're sure you messed up, a little apology goes a long way. Keep it real and say something like, "Hey, I'm sorry if I said something whack earlier. I didn't mean to bring you down." 🙏

At the end of the day, give your friend some space if they need it. We all got our own stuff going on, ya know? 🤐

If you're still unsure, maybe try talking to 'em in person. A face-to-face chat can work wonders. 💬

Just remember, it's all good. Missteps happen, but what matters is that you got each other's backs. That's what friends are for! 🤝

🔒 Ethical Considerations & Limitations

Ethical Guidelines

Prioritizes user emotional wellbeing
Maintains strict privacy standards
Avoids harmful or discriminatory responses
Transparent about AI limitations
Promotes healthy coping mechanisms

Known Limitations

Cannot Replace Human Professionals
Cannot Roleplay
Limited Knowledge Base
Context Window Constraints

Citation

@misc{helpingai2024,
  author = {Abhay Koul},
  title = {HelpingAI2.5-2B: Emotionally Intelligent Language Model},
  year = {2024},
  publisher = {Huggingface},
  journal = {GitHub repository},
  howpublished = {\url{https://huggingface.co/OEvortex/HelpingAI2.5-2B}}
}

🙏 Acknowledgments

Special thanks to the HelpingAI community, Huggingface, contributors, and researchers who made this model possible. Your dedication to advancing emotionally intelligent AI is invaluable.

Built with ❤️ by the HelpingAI Community

Website • GitHub • Discord • HuggingFace

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Safetensors

Model size

5B params

Tensor type

F16

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