metadata

license: afl-3.0
language:
  - yo
datasets:
  - afriqa
  - xlsum
  - menyo20k_mt
  - alpaca-gpt4

Model Description

mistral_7b_yo_instruct is a text generation model in Yorùbá.

Intended uses & limitations

How to use


from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "seyabde/mistral_7b_yo_instruct"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)

Example outputs

Ilana (Instruction): '...'

mistral_7b_yo_instruct: '...'

Eval results

Coming soon

Limitations and bias

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.

Training data

This model is fine-tuned on 60k+ instruction-following demonstrations built from an aggregation of datasets (AfriQA, XLSum, MENYO-20k), and translations of Alpaca-gpt4).

Use and safety

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.

BibTeX entry and citation info

@article{
  title={},
  author={},
  journal={},
  year={},
  volume={}
}