Update README.md

README.md

@@ -23,84 +23,31 @@ this model uses Task classification and the conversation is between USER and Ans
 # NOTE ⚠️
 
 
-THE JAX/FLAX version of model is available both for training and usage
 
+THE JAX/FLAX version of model is available both for training and usage And This model support context length of 3300
 
 
-# Using Model in Huggingface Transformers
+this model support run with OST_UI so heres how to run it with just one command 
 
-## Examples 🚀 
-
-```text
-</s><|prompter|> TEXT </s><|ai|>
+```shell
+git clone https://github.com/erfanzar/OST-OpenSourceTransformers
+cd OST-OpenSourceTransformers/
+python3 OST_UI/app.py --model_id='erfanzar/chatLGeM' --
 ```
 
-For Byte by Byte Generation, You can use this code
-```python
-# It's recommended to use PipeLine 
-# Make Sure that you have sentence piece,bits and bytes and accelerate installed
-from transformers import LlamaTokenizer, LlamaForCausalLM, pipeline, GenerationConfig
-import torch
-from IPython.display import clear_output
-import textwrap
-from typing import List, Optional
-import re
-import base64
-
-tokenizer = LlamaTokenizer.from_pretrained("erfanzar/LGeM-7B-MT")
-model = LlamaForCausalLM.from_pretrained(
-    'erfanzar/LGeM-7B-MT',
-    load_in_8bit=True, 
-    device_map='auto',
-    torch_dtype=torch.float16
-)
-
-def generator(input_text,pipe_line,max_number=256,do_print=False ,args_a=False):
-  verify_text = lambda txt : '\n'.join([textwrap.fill(txt, width=140) for txt in txt.split('\n')])
-  
-  orginal_text = input_text
-  if not input_text.startswith(f'<|prompter|>') and args_a:
-    input_text = f'<\s><|prompter|> {input_text}<\s><|ai|>'
-  for i in range(max_number):
-    exac = input_text
-    with torch.no_grad():
-      output = pipe_line(input_text)
-    input_text = output[0]['generated_text']
-    if do_print:
-      clear_output(wait=True)
-      print(verify_text(input_text))
-    
-    if input_text.endswith('<\s>') and i>6 or exac == input_text or input_text.endswith('<|prompter|>') and i>6:
-      break
-    yield verify_text(input_text)
+## Examples 🚀 
 
+```text
+</s><|prompter|> TEXT </s><|assistant|>
 ```
 
-
-And Use just like 
-
-
-
-```python
-
-pipe_line = pipeline(
-    "text-generation",
-    model=model, 
-    tokenizer=tokenizer, 
-    temperature=0.8,
-    top_p=0.95,
-    max_new_tokens=4,
-    output_scores=True
-
-)
-```
  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=256,temperature :float= 1,eos_token_id:int=2):
+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):
@@ -160,8 +107,8 @@ from modules import FlaxLGeMForCausalLM
 
 - and Training code is available at jax_train.py (check source)
 - training parameters 
-- - learning rate 5e-5
-- - Optimizer LION
+- - learning rate 2e-5
+- - Optimizer AdamW
 - - batch 32
 - - TPU POD
 - - Train Time 50 hours