Update README.md

README.md

@@ -8,12 +8,12 @@ tags:
 - large language model
 - h2o-llmstudio
 inference: false
-thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
+thumbnail: https://static.wixstatic.com/media/bdee4e_d0af74523fa64a998d4cfb894e8cd3bb~mv2.png/v1/crop/x_40,y_663,w_1954,h_663/fill/w_342,h_116,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/PAIX%20Logo%20(2).png
 ---
 # Model Card
 ## Summary
 
-This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
+This model was trained using By PAIX.Cloud
 - Base model: [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b)
 
 
@@ -62,7 +62,6 @@ print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text
 <|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
 ```
 
-Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
 
 ```python
 import torch
@@ -168,7 +167,6 @@ RWForCausalLM(
 
 ## Model Configuration
 
-This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
 
 
 ## Model Validation