Instructions to use OEvortex/EMO-phi-128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OEvortex/EMO-phi-128k with Transformers:

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

pipe = pipeline("text-generation", model="OEvortex/EMO-phi-128k", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OEvortex/EMO-phi-128k", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("OEvortex/EMO-phi-128k", trust_remote_code=True)
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-phi-128k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OEvortex/EMO-phi-128k"
# 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-phi-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OEvortex/EMO-phi-128k

SGLang

How to use OEvortex/EMO-phi-128k 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-phi-128k" \
    --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-phi-128k",
		"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-phi-128k" \
        --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-phi-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OEvortex/EMO-phi-128k with Docker Model Runner:
```
docker model run hf.co/OEvortex/EMO-phi-128k
```

EMO-phi-128k

EMO-phi-128k is an emotional intelligence conversational AI model fine-tuned from Microsoft's Phi-3-mini-128k-instruct model. It is designed to engage in open-ended dialogue while exhibiting emotional understanding and emotional intelligence capabilities.

Model Details

Developer: OEvortex
Model Type: Transformer-based language model
Language: English
License: MIT
Base Model: microsoft/Phi-3-mini-128k-instruct

Model Description

EMO-phi-128k is a 128k parameter conversational AI model that has been fine-tuned to incorporate emotional intelligence and emotional understanding capabilities. It aims to engage in emotionally aware and contextual dialogue by recognizing and responding appropriately to the emotional tones and sentiments expressed by the user.

While inheriting the strong language understanding and generation capabilities of its base model, EMO-phi-128k has been specifically optimized for emotional intelligence tasks through additional fine-tuning on emotional dialogue datasets.

Intended Uses

Emotional Support / Conversational Companion
Customer Service Chatbots (with emotional intelligence)
Creative Writing Assistance (with emotional awareness)
Psychological/Therapeutic Applications

Limitations and Risks

As an AI system, EMO-phi-128k may exhibit biases present in its training data. Its true emotional intelligence capabilities are not fully known or verified. The model should be used with caution, especially in sensitive or high-stakes applications involving mental health, therapy, or counseling. Proper human oversight is recommended.

Additionally, like all language models, EMO-phi-128k is susceptible to generating harmful, biased, or explicit content if prompted in an unsafe manner. Safety considerations should be taken into account when deploying or interacting with the model.

How to Use

You can load and use the EMO-phi-128k model with the Transformers library in Python:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

torch.random.manual_seed(0)

model = AutoModelForCausalLM.from_pretrained(
    "OEvortex/EMO-phi-128k", 
    device_map="cuda", 
    torch_dtype="auto", 
    trust_remote_code=True, 
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")

messages = [
    {"role": "system", "content": "You are a helpful Emotional intelligence named as EMO-phi, remember to always answer users question in EMO style."},
    {"role": "user", "content": "My best friend recently lost their parent to cancer after a long battle. They are understandably devastated and struggling with grief."},
]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.6,
    "do_sample": True,
}

output = pipe(messages, **generation_args)
print(output[0]['generated_text'])