Welcome to the era of emotionally intelligent AI with HelpingAI2-6B! This remarkable large language model is designed to facilitate conversations that are not only coherent but also rich in empathy and emotional understanding. In this blog post, we will walk you through how to utilize this technology effectively and troubleshoot common issues you might encounter along the way.

Overview

HelpingAI2-6B brings a unique twist to conversations, allowing it to:

• Engage in meaningful, open-ended dialogues.
• Recognize and validate user emotions.
• Provide supportive and empathetic responses.
• Avoid harmful or insensitive speech.

Methodology

HelpingAI2-6B employs a blend of advanced training methods to ensure high emotional intelligence:

• Supervised Learning: Utilizes large dialogue datasets with emotional labeling.
• Reinforcement Learning: Implements a reward model favoring supportive responses.
• Constitution Training: Embeds stable and ethical objectives guiding conversational behavior.
• Knowledge Augmentation: Incorporates psychological resources to enhance understanding.

Understanding the Code: An Analogy

When using HelpingAI2-6B, think of the model as a master chef in a high-end restaurant. To create the perfect dish (a coherent and empathetic conversation), the chef needs the right ingredients (data), a reliable recipe (the model itself), and some kitchen tools (code libraries). Here’s how the process works:

• The model is like the chef who uses a mixture of sample conversations (recipes) to understand what flavors (emotions) to combine.
• The tokenizer is the sous-chef, preparing ingredients so that the chef can focus on creating the beautiful dish without distractions.
• The chat input acts as the order ticket in a restaurant, detailing the customer’s wishes (user prompts) for the chef.
• Finally, the outputs are the finished dishes (responses) that are exquisitely plated and served to the customers (users).

Getting Started with the Model

Here is a simple implementation of the HelpingAI2-6B model using Python:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the HelpingAI2-6B model
model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI2-6B ", trust_remote_code=True)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI2-6B ", trust_remote_code=True)

# 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": "I'm excited because I just got accepted into my dream school! I wanted to share the good news with someone." }
]

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

Using the Model with GGUF

If you prefer to interface with your model through GGUF, here’s how:

# Install the required package
# %pip install -U 'webscout[local]' -q 

from webscout.Local.utils import download_model
from webscout.Local.model import Model
from webscout.Local.thread import Thread
from webscout.Local import formats
from webscout.Local.samplers import SamplerSettings

# Download the model
repo_id = "OEvortex/HelpingAI2-6B "
filename = "HelpingAI2-6B -q4_k_m.gguf"
model_path = download_model(repo_id, filename, token="")

# Load the model
model = Model(model_path, n_gpu_layers=40)

# Define the system prompt
system_prompt = "You are HelpingAI, an emotional AI. Always answer my questions in the HelpingAI style."

# Create a chat format
helpingai = formats.llama3.copy()
helpingai['system_content'] = system_prompt

# Define sampler settings
sampler = SamplerSettings(temp=0.7, top_p=0.9)

# Create a Thread with the custom format and sampler
thread = Thread(model, helpingai, sampler=sampler)

# Start interacting with the model
thread.interact(header="🌟 HelpingAI2-6B : Emotionally Intelligent Conversational AI 🚀", color=True)

Example Dialogue

Here are some sample interactions with HelpingAI2-6B:

User: I’m feeling really down today. Nothing seems to be going right.

HelpingAI: Oh no, I’m really sorry to hear that you’re feeling down. 😔 It’s totally okay to feel this way sometimes. Remember, you’re not alone, and it’s okay to take a break and take care of yourself. If you’d like to talk about what’s bothering you, I’m here to listen and offer support.

Troubleshooting Common Issues

Here are some troubleshooting ideas:

• If the model doesn’t respond, ensure that your code is correctly pointing to the model repository.
• Make sure that all required libraries are installed without errors.
• If responses seem off, adjust the temperature and top_p parameters to control randomness and creativity in the output.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.