

this model uses Task classification and the conversation is between USER and Answer or AI 

# NOTE ⚠️



THE JAX/FLAX version of model is available both for training and usage And This model support context length of 3300


this model support run with OST_UI so heres how to run it with just one command 

```shell
git clone https://github.com/erfanzar/OST-OpenSourceTransformers
cd OST-OpenSourceTransformers/
python3 OST_UI/app.py --model_id='erfanzar/chatLGeM' --num_gpus <NUMBER OF GPUS TO USE>
```

## Examples 🚀 

```text
</s><|prompter|> TEXT </s><|assistant|>
```

 or Just Simply Open [GOOGLE COLAB 🚀🚀](https://colab.research.google.com/drive/1nWS_FhWIDH3-g56F3FbWCIYi0ngVdWHx?usp=sharing)

### Generate Method to get res Text by Text

```python

def generate(model_,input_ids_,tokeinzer_,max_length:int=3300,temperature :float= 0.2,eos_token_id:int=2):
  with torch.no_grad():
    before_start = len(input_ids_[0])+1
    for _ in range(max_length):
      out = model_(
          input_ids=input_ids_,
          return_dict=True,
      )
      opa = torch.nn.functional.softmax(out.logits[:,-1,:]/temperature)
      Camila = torch.multinomial(opa,1)
      input_ids_ = torch.cat([input_ids_,Camila],-1)
      clear_output(wait=True)
      print(f"\r{tokeinzer_.decode(input_ids_[0],skip_special_tokens=True)[before_start:]}",end='')
      if Camila[0].item() == eos_token_id:
        break
      yield tokeinzer_.decode(Camila[0],skip_special_tokens=True)
  return f"{tokeinzer_.decode(input_ids_[0],skip_special_tokens=True)[before_start:]}"
```


### Result 

```python
import socket
import time

def check_internet_connection():
    try:
        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        s.connect(("www.google.com", 80))
        print("Internet connection is active.")
    except:
        print("Internet connection is not active.")

if __name__ == "__main__":

  check_internet_connection()
```


# Using Model in OST


### LGeM 🚀

- what is LGeM, LGeM is a CausalLM Model that is trained on self instruct data (Alpaca data) and for initialization of the first train of the main model (weights are available) I used pre weights from Alpaca LoRA (open source) 

- it's Decoder Only
- built-in Pytorch and Jax
- you can simply import models like (In EasyDeL or OST Library) 

```python
# Pytorch
from modules import LGeMForCausalLM
# Jax
from modules import FlaxLGeMForCausalLM
```

- and Training code is available at jax_train.py (check source)
- training parameters 
- - learning rate 2e-5
- - Optimizer AdamW
- - batch 32
- - TPU POD
- - Train Time 50 hours
- - budget 500 $
``` shell
python3 LGeM-train.py
```
{"language": ["en", "es", "ar", "fr", "fa"], "license": "apache-2.0", "tags": ["code"], "datasets": ["OpenAssistant/oasst1", "EleutherAI/pile"], "metrics": ["accuracy", "bleu"], "pipeline_tag": "text-generation"}
transformers pytorch llama text-generation code en es ar fr fa dataset:OpenAssistant/oasst1 dataset:EleutherAI/pile license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #llama #text-generation #code #en #es #ar #fr #fa #dataset-OpenAssistant/oasst1 #dataset-EleutherAI/pile #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 



this model uses Task classification and the conversation is between USER and Answer or AI 

# NOTE ️



THE JAX/FLAX version of model is available both for training and usage And This model support context length of 3300


this model support run with OST_UI so heres how to run it with just one command 



## Examples  



 or Just Simply Open GOOGLE COLAB 

### Generate Method to get res Text by Text




### Result 




# Using Model in OST


### LGeM 

- what is LGeM, LGeM is a CausalLM Model that is trained on self instruct data (Alpaca data) and for initialization of the first train of the main model (weights are available) I used pre weights from Alpaca LoRA (open source) 

- it's Decoder Only
- built-in Pytorch and Jax
- you can simply import models like (In EasyDeL or OST Library) 



- and Training code is available at jax_train.py (check source)
- training parameters 
- - learning rate 2e-5
- - Optimizer AdamW
- - batch 32
- - TPU POD
- - Train Time 50 hours
- - budget 500 $

# NOTE ️



THE JAX/FLAX version of model is available both for training and usage And This model support context length of 3300


this model support run with OST_UI so heres how to run it with just one command ## Examples  



 or Just Simply Open GOOGLE COLAB ### Generate Method to get res Text by Text ### Result # Using Model in OST ### LGeM 

- what is LGeM, LGeM is a CausalLM Model that is trained on self instruct data (Alpaca data) and for initialization of the first train of the main model (weights are available) I used pre weights from Alpaca LoRA (open source) 

- it's Decoder Only
- built-in Pytorch and Jax
- you can simply import models like (In EasyDeL or OST Library) 



- and Training code is available at jax_train.py (check source)
- training parameters 
- - learning rate 2e-5
- - Optimizer AdamW
- - batch 32
- - TPU POD
- - Train Time 50 hours
- - budget 500 $
TAGS
#transformers #pytorch #llama #text-generation #code #en #es #ar #fr #fa #dataset-OpenAssistant/oasst1 #dataset-EleutherAI/pile #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # NOTE ️



THE JAX/FLAX version of model is available both for training and usage And This model support context length of 3300


this model support run with OST_UI so heres how to run it with just one command ## Examples  



 or Just Simply Open GOOGLE COLAB ### Generate Method to get res Text by Text ### Result # Using Model in OST ### LGeM 

- what is LGeM, LGeM is a CausalLM Model that is trained on self instruct data (Alpaca data) and for initialization of the first train of the main model (weights are available) I used pre weights from Alpaca LoRA (open source) 

- it's Decoder Only
- built-in Pytorch and Jax
- you can simply import models like (In EasyDeL or OST Library) 



- and Training code is available at jax_train.py (check source)
- training parameters 
- - learning rate 2e-5
- - Optimizer AdamW
- - batch 32
- - TPU POD
- - Train Time 50 hours
- - budget 500 $
TAGS
#transformers #pytorch #llama #text-generation #code #en #es #ar #fr #fa #dataset-OpenAssistant/oasst1 #dataset-EleutherAI/pile #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# NOTE ️



THE JAX/FLAX version of model is available both for training and usage And This model support context length of 3300


this model support run with OST_UI so heres how to run it with just one command## Examples  



 or Just Simply Open GOOGLE COLAB### Generate Method to get res Text by Text### Result# Using Model in OST### LGeM 

- what is LGeM, LGeM is a CausalLM Model that is trained on self instruct data (Alpaca data) and for initialization of the first train of the main model (weights are available) I used pre weights from Alpaca LoRA (open source) 

- it's Decoder Only
- built-in Pytorch and Jax
- you can simply import models like (In EasyDeL or OST Library) 



- and Training code is available at jax_train.py (check source)
- training parameters 
- - learning rate 2e-5
- - Optimizer AdamW
- - batch 32
- - TPU POD
- - Train Time 50 hours
- - budget 500 $