Instructions to use HelpingAI/HelpingAI2-6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HelpingAI/HelpingAI2-6B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HelpingAI/HelpingAI2-6B",
	filename="helpingai-6b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use HelpingAI/HelpingAI2-6B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HelpingAI/HelpingAI2-6B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HelpingAI/HelpingAI2-6B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HelpingAI/HelpingAI2-6B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HelpingAI/HelpingAI2-6B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf HelpingAI/HelpingAI2-6B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HelpingAI/HelpingAI2-6B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf HelpingAI/HelpingAI2-6B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HelpingAI/HelpingAI2-6B:Q4_K_M

Use Docker

docker model run hf.co/HelpingAI/HelpingAI2-6B:Q4_K_M

LM Studio
Jan

vLLM

How to use HelpingAI/HelpingAI2-6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HelpingAI/HelpingAI2-6B"
# 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/HelpingAI2-6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HelpingAI/HelpingAI2-6B:Q4_K_M

Ollama
How to use HelpingAI/HelpingAI2-6B with Ollama:
```
ollama run hf.co/HelpingAI/HelpingAI2-6B:Q4_K_M
```

Unsloth Studio new

How to use HelpingAI/HelpingAI2-6B with 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 HelpingAI/HelpingAI2-6B 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 HelpingAI/HelpingAI2-6B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HelpingAI/HelpingAI2-6B to start chatting

Docker Model Runner
How to use HelpingAI/HelpingAI2-6B with Docker Model Runner:
```
docker model run hf.co/HelpingAI/HelpingAI2-6B:Q4_K_M
```

Lemonade

How to use HelpingAI/HelpingAI2-6B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HelpingAI/HelpingAI2-6B:Q4_K_M

Run and chat with the model

lemonade run user.HelpingAI2-6B-Q4_K_M

List all available models

lemonade list

Abhaykoul commited on Aug 5, 2024

Commit

b143566

verified ·

1 Parent(s): 09b146f

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -10

README.md CHANGED Viewed

@@ -17,12 +17,12 @@ datasets:
 language:
 - en
 ---
-# HelpingAI-6B: Emotionally Intelligent Conversational AI
 ![logo](https://huggingface.co/OEvortex/HelpingAI-3B/resolve/main/HelpingAI.png)
 ## Overview
-HelpingAI-6B is a state-of-the-art large language model designed to facilitate emotionally intelligent conversations. It leverages advanced natural language processing capabilities to engage users with empathy, understanding, and supportive dialogue across a variety of topics.
 - Engage in meaningful, open-ended dialogue while displaying high emotional intelligence.
 - Recognize and validate user emotions and emotional contexts.
@@ -31,14 +31,14 @@ HelpingAI-6B is a state-of-the-art large language model designed to facilitate e
 - Continuously improve emotional awareness and dialogue skills.
 ## Methodology
-HelpingAI-6B is part of the HelpingAI series and has been trained using:
 - **Supervised Learning**: Utilizing large dialogue datasets with emotional labeling to enhance empathy and emotional recognition.
 - **Reinforcement Learning**: Implementing a reward model that favors emotionally supportive responses to ensure beneficial interactions.
 - **Constitution Training**: Embedding stable and ethical objectives to guide its conversational behavior.
 - **Knowledge Augmentation**: Incorporating psychological resources on emotional intelligence to improve its understanding and response capabilities.
 ## Emotional Quotient (EQ)
-HelpingAI-6B has achieved an impressive Emotional Quotient (EQ) of 91.57, making it one of the most emotionally intelligent AI models available. This EQ score reflects its advanced ability to understand and respond to human emotions in a supportive and empathetic manner.
 ## Usage Code
@@ -46,10 +46,10 @@ HelpingAI-6B has achieved an impressive Emotional Quotient (EQ) of 91.57, making
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-# Load the HelpingAI-6B model
-model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI-6B", trust_remote_code=True)
 # Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-6B", trust_remote_code=True)
 # Define the chat input
@@ -93,8 +93,8 @@ from webscout.Local.samplers import SamplerSettings
 # Download the model
-repo_id = "OEvortex/HelpingAI-6B"
-filename = "helpingai-6b-q4_k_m.gguf"
 model_path = download_model(repo_id, filename, token="")
 # Load the model
@@ -114,7 +114,7 @@ sampler = SamplerSettings(temp=0.7, top_p=0.9)
 thread = Thread(model, helpingai, sampler=sampler)
 # Start interacting with the model
-thread.interact(header="🌟 HelpingAI-6B: Emotionally Intelligent Conversational AI 🚀", color=True)
 ```

 language:
 - en
 ---
+# HelpingAI2-6B : Emotionally Intelligent Conversational AI
 ![logo](https://huggingface.co/OEvortex/HelpingAI-3B/resolve/main/HelpingAI.png)
 ## Overview
+HelpingAI2-6B  is a state-of-the-art large language model designed to facilitate emotionally intelligent conversations. It leverages advanced natural language processing capabilities to engage users with empathy, understanding, and supportive dialogue across a variety of topics.
 - Engage in meaningful, open-ended dialogue while displaying high emotional intelligence.
 - Recognize and validate user emotions and emotional contexts.
 - Continuously improve emotional awareness and dialogue skills.
 ## Methodology
+HelpingAI2-6B  is part of the HelpingAI series and has been trained using:
 - **Supervised Learning**: Utilizing large dialogue datasets with emotional labeling to enhance empathy and emotional recognition.
 - **Reinforcement Learning**: Implementing a reward model that favors emotionally supportive responses to ensure beneficial interactions.
 - **Constitution Training**: Embedding stable and ethical objectives to guide its conversational behavior.
 - **Knowledge Augmentation**: Incorporating psychological resources on emotional intelligence to improve its understanding and response capabilities.
 ## Emotional Quotient (EQ)
+HelpingAI2-6B  has achieved an impressive Emotional Quotient (EQ) of 91.57, making it one of the most emotionally intelligent AI models available. This EQ score reflects its advanced ability to understand and respond to human emotions in a supportive and empathetic manner.
 ## Usage Code
 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
 # 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
 thread = Thread(model, helpingai, sampler=sampler)
 # Start interacting with the model
+thread.interact(header="🌟 HelpingAI2-6B : Emotionally Intelligent Conversational AI 🚀", color=True)
 ```