metadata
language:
- en
- fa
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## [Model description](#model-description)|[Example output](#example-output)|[Banchmark results](#banchmark-results)|[How to use](#how-to-use)|[Training and finetuning](#training-and-finetuning) |Model description
Example output:
Example 1:
- Input: "سلام، خوبی؟"
- Output: "سلام، خوشحالم که با شما صحبت می کنم. چطور می توانم به شما کمک کنم؟"
Example 2: - Input: "سلام، خوبی؟" - Output: "سلام، خوشحالم که با شما صحبت می کنم. چطور می توانم به شما کمک کنم؟"
Banchmark results
| model | dataset | max_token | prompt | score |
|---|---|---|---|---|
| base-model-7b | ARC-easy-dev | 2 | en-1 | 0.41929 |
| base-model-7b | ARC-easy-dev | 80 | en-2 | 0.39122 |
| base-model-7b | ARC-easy-dev | 300 | en-1 | 0.34448 |
| model | dataset | max_token | prompt | score |
| --------------- | ------------------ | ----------- | -------- | --------- |
| fa-model-7b | ARC-easy-dev | 80 | en-1 | 0.37894 |
| fa-model-7b | ARC-easy-dev | 80 | en-2 | 0.33333 |
| fa-model-7b | ARC-easy-dev | 80 | fa-2 | 0.28771 |
| fa-model-7b | ARC-easy-dev | 300 | fa-1 | 0.25752 |
| fa-model-7b | ARC-easy-dev | 2 | fa-1 | 0.24035 |
| model | dataset | max_token | prompt | score |
|---|---|---|---|---|
| base-model-7b | ARC-challenge-dev | 80 | en-2 | 0.37123 |
| base-model-7b | ARC-challenge-dev | 2 | en-2 | 0.36789 |
| base-model-7b | ARC-challenge-dev | 2 | en-1 | 0.35451 |
| base-model-7b | ARC-challenge-dev | 80 | en-1 | 0.33779 |
| model | dataset | max_token | prompt | score |
| --------------- | -------------------- | ----------- | -------- | --------- |
| fa-model-7b | ARC-challenge-dev | 2 | en-1 | 0.39298 |
| fa-model-7b | ARC-challenge-dev | 80 | en-1 | 0.38421 |
| fa-model-7b | ARC-challenge-dev | 2 | en-2 | 0.31929 |
| fa-model-7b | ARC-challenge-dev | 80 | en-2 | 0.31754 |
How to use
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("aidal/Persian-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("aidal/Persian-Mistral-7B")
input_text = "پایتخت ایران کجاست؟"
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))