Update README.md

README.md

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 ---
 license: cc-by-nc-4.0
+datasets:
+- bertin-project/alpaca-spanish
+language:
+- es
+inference: false
 ---
+
+
+# Model Card for Model ID
+
+This model is the Llama-2-13b-hf fine-tuned with an adapter on the Spanish Alpaca dataset.
+
+## Model Details
+
+### Model Description
+
+This is a Spanish chat model fine-tuned on a Spanish instruction dataset. 
+
+The model expect a prompt containing the instruction, with an option to add an input (see examples below). 
+
+
+
+- **Developed by:** 4i Intelligent Insights
+- **Model type:** Chat model
+- **Language(s) (NLP):** Spanish
+- **License:** cc-by-nc-4.0 (inhereted from the alpaca-spanish dataset), 
+- **Finetuned from model  :** Llama 2 13B  ([license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/))
+
+
+## Uses
+
+The model is intended to be used directly without the need of further fine-tuning.
+
+
+## Bias, Risks, and Limitations
+
+This model inherits the bias, risks, and limitations of its base model, Llama 2, and of the dataset used for fine-tuning. 
+Note that the Spanish Alpaca dataset was obtained by translating the original Alpaca dataset. It contains translation errors that may have negatively impacted the fine-tuning of the model. 
+
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model for inference. The adapter was directly merged into the original Llama 2 model. 
+
+
+The following code sample uses 4-bit quantization, you may load the model without it if you have enough VRAM. We show results for hyperparameters that we found work well for this set of prompts.
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TrainingArguments, GenerationConfig
+import torch
+model_name = "4i-ai/Llama-2-13b-alpaca-es"
+
+
+#Tokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+def create_and_prepare_model():
+        compute_dtype = getattr(torch, "float16")
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=compute_dtype,
+            bnb_4bit_use_double_quant=True,
+        )
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name, quantization_config=bnb_config, device_map={"": 0}
+        )
+        return model
+model = create_and_prepare_model()
+
+def generate(instruction, input=None):
+    #Format the prompt to look like the training data
+    if input is not None:
+        prompt = "### Instruction:\n"+instruction+"\n\n### Input:\n"+input+"\n\n### Response:\n"
+    else :
+        prompt = "### Instruction:\n"+instruction+"\n\n### Response:\n"
+
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].cuda()
+
+    generation_output = model.generate(
+            input_ids=input_ids,
+            repetition_penalty=1.5,
+            generation_config=GenerationConfig(temperature=0.1, top_p=0.75, top_k=40, num_beams=20), #hyperparameters for generation
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=150, #maximum tokens generated, increase if you want longer asnwer (up to 2048 - the length of the prompt), generation "looks" slower for longer response
+
+    )
+    for seq in generation_output.sequences:
+        output = tokenizer.decode(seq, skip_special_tokens=True)
+        print(output.split("### Response:")[1].strip())
+        
+generate("Háblame de la superconductividad.")
+print("-----------")
+generate("Encuentra la capital de España.")
+print("-----------")
+generate("Encuentra la capital de Portugal.")
+print("-----------")
+generate("Organiza los números dados en orden ascendente.", "2, 3, 0, 8, 4, 10")
+print("-----------")
+generate("Compila una lista de 5 estados de EE. UU. ubicados en el Oeste.")
+print("-----------")
+generate("Compila una lista de 2 estados de EE. UU. ubicados en el Oeste.")
+print("-----------")
+generate("Compila una lista de 10 estados de EE. UU. ubicados en el Este.")
+print("-----------")
+generate("¿Cuál es la color de una fresa?")
+print("-----------")
+generate("¿Cuál es la color de la siguiente fruta?", "fresa")
+print("-----------")
+
+```
+
+Expected output:
+
+```
+La superconductividad es un fenómeno físico en el que los materiales pueden conducir corrientes eléctricas a bajas temperaturas sin pérdida de energía debido a la resistencia. Los materiales superconductores son capaces de conducir corrientes eléctricas a temperaturas mucho más bajas que los materiales normales. Esto se debe a que los electrones en los materiales superconductores se comportan de manera cooperativa, lo que les permite conducir corrientes eléctricas sin pérdida de energía. Los materiales superconductores tienen muchas aplicaciones
+-----------
+La capital de España es Madrid.
+-----------
+La capital de Portugal es Lisboa.
+-----------
+0, 2, 3, 4, 8, 10
+-----------
+1. California
+2. Oregón
+3. Washington
+4. Nevada
+5. Arizona
+-----------
+California y Washington.
+-----------
+1. Maine
+2. Nuevo Hampshire
+3. Vermont
+4. Massachusetts
+5. Rhode Island
+6. Connecticut
+7. Nueva York
+8. Nueva Jersey
+9. Pensilvania
+10. Delaware
+-----------
+La color de una fresa es rosa.
+-----------
+Roja
+-----------
+```
+
+
+
+
+
+## Contact Us
+[4i.ai](https://4i.ai/) provides natural language processing solutions with dialog, vision and voice capabilities to deliver real-life multimodal human-machine conversations. 
+Please contact us at info@4i.ai