QuantFactory / aya-23-8B-GGUF

Aya-23-8B-GGUF

• This is quantized version of CohereForAI/aya-23-8B created using llama.cpp

Model Description

Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained Command family of models with the recently released Aya Collection . The result is a powerful multilingual large language model serving 23 languages.

This model card corresponds to the 8-billion version of the Aya 23 model. We also released a 35-billion version which you can find here .

We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese

Developed by: Cohere For AI and Cohere

• Point of Contact: Cohere For AI: cohere.for.ai
• License: CC-BY-NC , requires also adhering to C4AI's Acceptable Use Policy
• Model: aya-23-8B
• Model Size: 8 billion parameters

Try Aya 23

You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space here .

Usage

Please install transformers from the source repository that includes the necessary changes for this model

# pip install transformers==4.41.1
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "CohereForAI/aya-23-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>

gen_tokens = model.generate(
    input_ids, 
    max_new_tokens=100, 
    do_sample=True, 
    temperature=0.3,
    )

gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)

Example Notebook

This notebook showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with QLoRA .

Model Details

Input : Models input text only.

Output : Models generate text only.

Model Architecture : Aya-23-8B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions.

Languages covered : The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese

Context length : 8192

Evaluation

Please refer to the Aya 23 technical report for further details about the base model, data, instruction tuning, and evaluation.

Terms of Use

We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to C4AI's Acceptable Use Policy .