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--- |
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tags: |
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- text-generation-inference |
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- text-generation |
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- Sentiment Analysis |
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license: apache-2.0 |
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library_name: transformers |
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widget: |
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- messages: |
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- role: user |
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content: What is your name? |
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language: |
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- en |
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- ro |
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--- |
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<div align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/65ec00afa735404e87e1359e/u5qyAgn_2-Bh46nzOFlcI.png"> |
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<h2>Accessible and portable generative AI solutions for developers and businesses.</h2> |
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</div> |
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***Description*** |
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"Bella-2-8b" by Cognitivess is a text generation model tailored for empathic AI interactions, supporting both English and Romanian languages. |
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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. |
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It employs a floating-point 16 (BF16) tensor type for operations, facilitating speech-to-speech applications. |
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Licensed under Cognitivess AI, Bella-2-8b is available on the Hugging Face platform for wide accessibility. |
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### Intended use |
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Bella-2-8B 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. |
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**Model Developer:** Cognitivess AI |
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**Model Dates:** Bella-2-8b was trained between May 2024 and June 2024. |
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**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. |
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### Model Architecture: |
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Bella-2-8B model architecture is Transformer-based and trained with a sequence length of 8192 tokens. |
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**Architecture Type:** Transformer (auto-regressive language model) |
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Try this model on [bella.cognitivess.com](https://bella.cognitivess.com/) now. |
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# Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model_path = "CognitivessAI/bella-2-8b" |
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# Load the tokenizer and model, converting model to half precision |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained(model_path).half().eval() |
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# Move the model to CUDA if available |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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# Prompt content: "hi" |
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messages = [ |
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{"role": "user", "content": "Who are you?"} |
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] |
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input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') |
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# Move input_ids to the same device as the model |
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input_ids = input_ids.to(device) |
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# Adjust the generate method to set max_new_tokens |
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output_ids = model.generate(input_ids, max_new_tokens=50) |
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response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) |
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# Model response: "I'm Bella, an AI model developed by Cognitivess." |
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print(response) |
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``` |
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**Contact:** |
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<a href="mailto:hello@cognitivess.com">hello@cognitivess.com</a> |
