Instructions to use HelpingAI/Helpingai3-raw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HelpingAI/Helpingai3-raw with Transformers:

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

pipe = pipeline("text-generation", model="HelpingAI/Helpingai3-raw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HelpingAI/Helpingai3-raw")
model = AutoModelForCausalLM.from_pretrained("HelpingAI/Helpingai3-raw")
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/Helpingai3-raw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HelpingAI/Helpingai3-raw"
# 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/Helpingai3-raw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HelpingAI/Helpingai3-raw

SGLang

How to use HelpingAI/Helpingai3-raw 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/Helpingai3-raw" \
    --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/Helpingai3-raw",
		"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/Helpingai3-raw" \
        --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/Helpingai3-raw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HelpingAI/Helpingai3-raw with Docker Model Runner:
```
docker model run hf.co/HelpingAI/Helpingai3-raw
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the HelpingAI3 model
model = AutoModelForCausalLM.from_pretrained("HelpingAI/HAI3-RAW")
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HAI3-RAW")

# 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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Safetensors

Model size

10B params

Tensor type

F16

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