---
language: 
- multilingual

license: apache-2.0
---

# Model Card for Sindibad-7B


#  Table of Contents

0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)


# TL;DR

# Model Details

## Model Description


- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0

# Usage

Find below some example scripts on how to use the model in `transformers` (Make sure to have the latest transformers, or the one built from source):

## Using the Pytorch model

### Running the model on a CPU

<details>
<summary> Click to expand </summary>

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b")

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```

</details>

### Running the model on a GPU

<details>
<summary> Click to expand </summary>

```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto")

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```

</details>

### Running the model on a GPU using different precisions

#### FP16

<details>
<summary> Click to expand </summary>

```python
# pip install accelerate
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto", torch_dtype=torch.float16)

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```

</details>

#### INT8

<details>
<summary> Click to expand </summary>

```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto", load_in_8bit=True)

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```

</details>


# Training Details

## Training Data

Jingwei

## Training Procedure

Maksim

# Evaluation

## Results 

Ilyas