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Description

"Bella-1-8b" by Cognitivess is a text generation model tailored for empathic AI interactions, supporting both English and Romanian languages. The model, built on the transformers architecture, features 8.03 billion parameters , well-suited for a variety of text generation tasks, including question answering, summarization, reasoning, dialogue, sentiment analysis. It employs a floating-point 32 (F32) tensor type for operations, facilitating speech-to-speech applications. Licensed under Cognitivess AI, Bella-1-8b is available on the Hugging Face platform for wide accessibility.

Under the Cognitivess Open Model License, Cognitivess AI confirms:

Models are commercially usable.
You are free to create and distribute Derivative Models.
Cognitivess does not claim ownership to any outputs generated using the Models or Derivative Models.

Intended use

Bella-1-8B is a multilingual chat model designed to support a variety of languages including English, Romanian, Spanish, French, German, and many more, intended for diverse language applications.

Model Developer: Cognitivess AI

Model Dates: Bella-1-8b was trained between September 2023 and Jun 2024.

Data Freshness: The pretraining data has a cutoff of June 2024. Training will continue beyond the current data cutoff date to incorporate new data as it becomes available.

Model Architecture:

Bella-1-8B model architecture is Transformer-based and trained with a sequence length of 8192 tokens.

Architecture Type: Transformer (auto-regressive language model)

Try this model on bella.cognitivess.com now.

Usage


from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "cognitivess/bella-1-8b"

# Load the tokenizer and model, converting model to half precision
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path).half().eval()

# Move the model to CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "Who are you?"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')

# Move input_ids to the same device as the model
input_ids = input_ids.to(device)

# Adjust the generate method to set max_new_tokens
output_ids = model.generate(input_ids, max_new_tokens=50)

response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "I'm Bella, an AI model developed by Cognitivess."
print(response)

Evaluation results The model bella-1-8b achieves the following results:

Safety bella-1-8b can make mistakes. Check important info.

Contact: hello@cognitivess.com