How to use from
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 Abhaykoul/HelpingAI2-4x6B 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 Abhaykoul/HelpingAI2-4x6B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abhaykoul/HelpingAI2-4x6B to start chatting
Quick Links

HelpingAI2-4x6B : Emotionally Intelligent Conversational AI

Usage Code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the HelpingAI2-4x6B  model
model = AutoModelForCausalLM.from_pretrained("Abhaykoul/HelpingAI2-4x6B ", trust_remote_code=True)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("Abhaykoul/HelpingAI2-4x6B ", 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))
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Datasets used to train Abhaykoul/HelpingAI2-4x6B