Instructions to use OEvortex/EMO-2B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OEvortex/EMO-2B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="OEvortex/EMO-2B-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OEvortex/EMO-2B-v2")
model = AutoModelForCausalLM.from_pretrained("OEvortex/EMO-2B-v2")
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 OEvortex/EMO-2B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OEvortex/EMO-2B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OEvortex/EMO-2B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OEvortex/EMO-2B-v2

SGLang

How to use OEvortex/EMO-2B-v2 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 "OEvortex/EMO-2B-v2" \
    --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": "OEvortex/EMO-2B-v2",
		"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 "OEvortex/EMO-2B-v2" \
        --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": "OEvortex/EMO-2B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OEvortex/EMO-2B-v2 with Docker Model Runner:
```
docker model run hf.co/OEvortex/EMO-2B-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EMO-2B: Emotionally Intelligent Conversational AI

Overview

EMO-2B-v2 is a state-of-the-art conversational AI model with 2.5 billion parameters, designed to engage in emotionally resonant dialogue. Building upon the success of EMO-1.5B, this model has been further fine-tuned on an extensive corpus of emotional narratives, enabling it to perceive and respond to the emotional undertones of user inputs with exceptional empathy and emotional intelligence.

Key Features

Advanced Emotional Intelligence: With its increased capacity, EMO-2B demonstrates an even deeper understanding and generation of emotional language, allowing for more nuanced and contextually appropriate emotional responses.
Enhanced Contextual Awareness: The model considers an even broader context within conversations, accounting for subtle emotional cues and providing emotionally resonant responses tailored to the specific situation.
Empathetic and Supportive Dialogue: EMO-2B excels at active listening, validating emotions, offering compassionate advice, and providing emotional support, making it an ideal companion for users seeking empathy and understanding.
Dynamic Persona Adaptation: The model can dynamically adapt its persona, communication style, and emotional responses to match the user's emotional state, ensuring a highly personalized and tailored conversational experience.

Use Cases

EMO-2B is well-suited for a variety of applications where emotional intelligence and empathetic communication are crucial, such as:

Mental health support chatbots
Emotional support companions
Personalized coaching and motivation
Narrative storytelling and interactive fiction
Customer service and support (for emotionally sensitive contexts)

Limitations and Ethical Considerations

While EMO-2B is designed to provide emotionally intelligent and empathetic responses, it is important to note that it is an AI system and cannot replicate the depth and nuance of human emotional intelligence. Users should be aware that the model's responses, while emotionally supportive, should not be considered a substitute for professional mental health support or counseling.

Additionally, as with any language model, EMO-2B may reflect biases present in its training data. Users should exercise caution and critical thinking when interacting with the model, and report any concerning or inappropriate responses.

Downloads last month: 13

Safetensors

Model size

3B params

Tensor type

F16

Model tree for OEvortex/EMO-2B-v2

Quantizations

1 model

Dataset used to train OEvortex/EMO-2B-v2

Collection including OEvortex/EMO-2B-v2

Emotional Intelligence

Collection

Models are listed based on EQ • 17 items • Updated Oct 6, 2024 • 9