---
language:
- en
- fa
---

<p align="center">
  <picture>
    <img alt="Hugging Face Transformers Library" src="https://i.postimg.cc/VN4F7WRC/Untitled-design-modified.png" width="1000" height="450" style="max-width: 100%;">
  </picture>
</p>

<h4 align="center">
    <p>
        <a href="https://huggingface.co./aidal/Persian-Mistral-7B#model-description">Model description</a> |
        <a href="https://huggingface.co./aidal/Persian-Mistral-7B#example-output">Example output</a> |
        <a href="https://huggingface.co./aidal/Persian-Mistral-7B#banchmark-results">Banchmark results</a> |
        <a href="https://huggingface.co./aidal/Persian-Mistral-7B#how-to-use">How to use</a> |
        <a href="https://huggingface.co./aidal/Persian-Mistral-7B#training-and-finetuning">Training and finetuning</a>
    </p>
</h4>

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# Model description

>Jamba is a state-of-the-art, hybrid SSM-Transformer LLM. It delivers throughput gains over traditional Transformer-based models, while outperforming or matching the leading models of its size class on most common benchmarks.

>Jamba is the first production-scale Mamba implementation, which opens up interesting research and application opportunities. While this initial experimentation shows encouraging gains, we expect these to be further enhanced with future optimizations and explorations.

>This model card is for the base version of Jamba. It’s a pretrained, mixture-of-experts (MoE) generative text model, with 12B active parameters and a total of 52B parameters across all experts. It supports a 256K context length, and can fit up to 140K tokens on a single 80GB GPU.
----

# 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 |

<br>
<br>
<br>

| 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

```python
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]))
```
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# Training and finetuning