Update README.md

README.md

@@ -7,4 +7,137 @@ language:
 - es
 library_name: adapter-transformers
 pipeline_tag: text2text-generation
----
+---
+# Model Card for opt-6.7b-lora-sag-t12000-v1200-v1
+
+## Adapter Description
+
+This adapter was created as part of the SomosNLP Hackathon 2023 with the [PEFT](https://github.com/huggingface/peft) library and allowed the base model [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) to be fine-tuned on the [SQUAD_ES](https://huggingface.co/datasets/squad_es) (v1.1.0) and [MLSUM](https://huggingface.co/datasets/mlsum) by using the method *LoRA*.
+
+- **Developed by:**
+  - 🇵🇪 Enrique Ubaldo
+  - 🇵🇪 Fernando Alva-Manchego
+  - 🇵🇪 @Levi111
+  - 🇲🇽 @IvanHU
+  - 🇨🇺 [Alberto Carmona Barthelemy](https://huggingface.co/milyiyo)
+- **Model type:** Text2Text Generation
+- **Language(s) (NLP):** Spanish
+- **License:** apache-2.0
+
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+This model is designed for use in Spanish language instruction, specifically for tasks such as generating summaries, creating questions, and generating answers based on a given context and question.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+Please note that this model inherits biases from its original base model. You can review these biases by visiting the following [link](https://huggingface.co/facebook/opt-6.7b#limitations-and-bias).
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```py
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+peft_model_id = "hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t14000-v1400-v1"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+model.config.use_cache = True
+
+generation_config = GenerationConfig(temperature=.8,
+                                     top_p=0.75,
+                                     top_k=40)
+
+def gen_summary(text):
+  input_text = f'<s>Instruction: Elabora un resume del siguiente texto.\nInput: {text}\nOutput: '
+  batch = tokenizer(input_text, return_tensors='pt')
+  with torch.cuda.amp.autocast():
+    output_tokens = model.generate(**batch, 
+                                   max_new_tokens=256, 
+                                   generation_config=generation_config)
+  output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+  return output
+
+def gen_question(text):
+  input_text = f'<s>Instruction: Dado el siguiente texto quiero que generes una pregunta cuya respuesta se encuentre en él.\nInput: {text}\nOutput: '
+  batch = tokenizer(input_text, return_tensors='pt')
+  with torch.cuda.amp.autocast():
+    output_tokens = model.generate(**batch, 
+                                   max_new_tokens=256, 
+                                   generation_config=generation_config)
+  output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+  return output
+
+def gen_qna(context, question):
+  input_text = f"""<s>Instruction: Te voy a proporcionar un texto del cual deseo que me respondas una pregunta. 
+    El texto es el siguiente: `{context}`\nInput: {question}\nOutput: """
+  batch = tokenizer(input_text, return_tensors='pt')
+  with torch.cuda.amp.autocast():
+    output_tokens = model.generate(**batch, 
+                                   max_new_tokens=256, 
+                                   generation_config=generation_config)
+  output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+  return output
+```
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+- [SQUAD_ES](https://huggingface.co/datasets/squad_es) (Subset: v1.1.0)
+- [MLSUM](https://huggingface.co/datasets/mlsum) (Subset: es)
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+We selected 4000 examples for each of the three tasks in the training dataset, and 400 examples for each task in the validation dataset. 
+This resulted in a total of 12000 examples for training and 1200 examples for validation.
+
+The Colab used for training is [here](https://colab.research.google.com/drive/1mwLUNgsSLrRbHUiXJuaU_6EQaYrYg10A?usp=sharing).
+
+#### Training Hyperparameters
+
+- **Training regime:** fp16 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Steps:**: 1240
+- **Learning rate:**: 2e-4
+- **Training loss:**: 1.013
+- **Validation loss:**: 1.53
+- **Training duration:**: 7.5 hours
+
+WandB link: [here](https://wandb.ai/milyiyo/huggingface/reports/eval-loss-train-loss-23-04-08-19-14-44---Vmlldzo0MDEwMzQ5?accessToken=o5f3pldc7np25ch8123rcjz4195vrcc9nl31r26i130jhhpvuabie3ezrw6dcs6r)
+
+
+
+<!--## Environmental Impact -->
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+<!--Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** T4 16GB (Free Colab)
+- **Hours used:** 7.5 hour
+- **Cloud Provider:** Google Cloud Platform
+- **Compute Region:** us-central1?
+- **Carbon Emitted:** Total emissions are estimated to be 0.04 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.
+-->