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--- |
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license: afl-3.0 |
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language: |
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- yo |
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datasets: |
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- afriqa |
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- xlsum |
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- menyo20k_mt |
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- alpaca-gpt4 |
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--- |
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# Model Description |
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**mistral_7b_yo_instruct** is a **text generation** model in Yorùbá. |
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## Intended uses & limitations |
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#### How to use |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_path = "seyabde/mistral_7b_yo_instruct" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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device_map="auto", |
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torch_dtype='auto' |
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).eval() |
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# Prompt content: "Pẹlẹ o. Bawo ni o se wa?" ("Hello. How are you?") |
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messages = [ |
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{"role": "user", "content": "Pẹlẹ o. Bawo ni o se wa?"} |
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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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output_ids = model.generate(input_ids.to('cuda')) |
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response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) |
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# Model response: |
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print(response) |
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``` |
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#### Example outputs |
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``` |
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Ilana (Instruction): '...' |
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mistral_7b_yo_instruct: '...' |
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``` |
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#### Eval results |
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Coming soon |
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#### Limitations and bias |
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This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. |
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#### Training data |
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This model is fine-tuned on 60k+ instruction-following demonstrations built from an aggregation of datasets ([AfriQA](https://huggingface.co/datasets/masakhane/afriqa), [XLSum](https://huggingface.co/datasets/csebuetnlp/xlsum), [MENYO-20k](https://huggingface.co/datasets/menyo20k_mt)), and translations of [Alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4)). |
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### Use and safety |
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We emphasize that mistral_7b_yo_instruct is intended only for research purposes and is not ready to be deployed for general use, namely because we have not designed adequate safety measures. |