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1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4# Load the HelpingAI2.5-10B model
5model = AutoModelForCausalLM.from_pretrained("HelpingAI/HelpingAI2.5-10B")
6# Load the tokenizer
7tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HelpingAI2.5-10B")
8
9# Define the chat input
10chat = [
11 { "role": "system", "content": "You are HelpingAI, an emotional AI. Always answer my questions in the HelpingAI style." },
12 { "role": "user", "content": "GIVE ME YOUR INTRO" }
13]
14
15inputs = tokenizer.apply_chat_template(
16response = outputs[0][inputs.shape[-1]:]
17print(tokenizer.decode(response, skip_special_tokens=True))1from webscout.Local import *
2model_path = download_model("HelpingAI/HelpingAI2.5-10B", "q4_k_m.gguf", token=None)
3model = Model(model_path, n_gpu_layers=0, context_length=4096)
4
5thread = Thread(model, format=helpingai2)
6# print(thread.send("hi")) #send a single msg to ai
7
8thread.interact() # interact with the model in terminal| 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 |
1@misc{helpingai2024,
2 author = {Abhay Koul},
3 title = {HelpingAI2.5-10B: Emotionally Intelligent Language Model},
4 year = {2024},
5 publisher = {Huggingface},
6 journal = {GitHub repository},
7 howpublished = {\url{https://huggingface.co/HelpingAI/HelpingAI2.5-10B}}
8}