Text Generation
Transformers
Safetensors
English
llama
HelpingAI
Emotionally-Intelligent
EQ-focused
Conversational
SLM
conversational
text-generation-inference
Instructions to use HelpingAI/HelpingAI-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HelpingAI/HelpingAI-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HelpingAI/HelpingAI-3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HelpingAI-3") model = AutoModelForCausalLM.from_pretrained("HelpingAI/HelpingAI-3") 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
- vLLM
How to use HelpingAI/HelpingAI-3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HelpingAI/HelpingAI-3" # 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/HelpingAI-3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/HelpingAI/HelpingAI-3
- SGLang
How to use HelpingAI/HelpingAI-3 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/HelpingAI-3" \ --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/HelpingAI-3", "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/HelpingAI-3" \ --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/HelpingAI-3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use HelpingAI/HelpingAI-3 with Docker Model Runner:
docker model run hf.co/HelpingAI/HelpingAI-3
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license: other
license_name: helpingai
license_link: https://helpingai.co/license
pipeline_tag: text-generation
language:
- en
tags:
- HelpingAI
- Emotionally-Intelligent
- EQ-focused
- Conversational
- SLM
library_name: transformers
---
# HelpingAI3
## Model Description
**HelpingAI3** is an advanced language model developed to excel in emotionally intelligent conversations. Building upon the foundations of HelpingAI2.5, this model offers enhanced emotional understanding and contextual awareness.
## Model Details
- **Developed by**: HelpingAI
- **Model type**: Decoder-only large language model
- **Language**: English
- **License**: [HelpingAI License](https://helpingai.co/license)
## Training Data
HelpingAI3 was trained on a diverse dataset comprising:
- **Emotional Dialogues**: 15 million rows to enhance conversational intelligence.
- **Therapeutic Exchanges**: 3 million rows aimed at providing advanced emotional support.
- **Cultural Conversations**: 250,000 rows to improve global awareness.
- **Crisis Response Scenarios**: 1 million rows to better handle emergency situations.
## Training Procedure
The model underwent the following training processes:
- **Base Model**: Initiated from HelpingAI2.5.
- **Emotional Intelligence Training**: Employed Reinforcement Learning for Emotion Understanding (RLEU) and context-aware conversational fine-tuning.
- **Optimization**: Utilized mixed-precision training and advanced token efficiency techniques.
## Intended Use
HelpingAI3 is designed for:
- **AI Companionship & Emotional Support**: Offering empathetic interactions.
- **Therapy & Wellbeing Guidance**: Assisting in mental health support.
- **Personalized Learning**: Tailoring educational content to individual needs.
- **Professional AI Assistance**: Enhancing productivity in professional settings.
## Limitations
While HelpingAI3 strives for high emotional intelligence, users should be aware of potential limitations:
- **Biases**: The model may inadvertently reflect biases present in the training data.
- **Understanding Complex Emotions**: There might be challenges in accurately interpreting nuanced human emotions.
- **Not a Substitute for Professional Help**: For serious emotional or psychological issues, consulting a qualified professional is recommended.
## How to Use
### Using Transformers
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the HelpingAI3 model
model = AutoModelForCausalLM.from_pretrained("HelpingAI/HelpingAI-3")
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HelpingAI-3")
# 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": "Introduce yourself."}
]
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))
```
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