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--- |
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license: cc-by-nc-4.0 |
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datasets: |
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- bertin-project/alpaca-spanish |
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language: |
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- es |
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inference: false |
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--- |
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# Model Card for Model ID |
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This model is the Llama-2-13b-hf fine-tuned with an adapter on the Spanish Alpaca dataset. |
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## Model Details |
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### Model Description |
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This is a Spanish chat model fine-tuned on a Spanish instruction dataset. |
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The model expect a prompt containing the instruction, with an option to add an input (see examples below). |
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- **Developed by:** 4i Intelligent Insights |
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- **Model type:** Chat model |
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- **Language(s) (NLP):** Spanish |
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- **License:** cc-by-nc-4.0 (inhereted from the alpaca-spanish dataset), |
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- **Finetuned from model :** Llama 2 13B ([license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)) |
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## Uses |
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The model is intended to be used directly without the need of further fine-tuning. |
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## Bias, Risks, and Limitations |
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This model inherits the bias, risks, and limitations of its base model, Llama 2, and of the dataset used for fine-tuning. |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model for inference. The adapter was directly merged into the original Llama 2 model. |
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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. |
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```py |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TrainingArguments, GenerationConfig |
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import torch |
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model_name = "4i-ai/Llama-2-13b-alpaca-es" |
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#Tokenizer |
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) |
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def create_and_prepare_model(): |
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compute_dtype = getattr(torch, "float16") |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=compute_dtype, |
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bnb_4bit_use_double_quant=True, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, quantization_config=bnb_config, device_map={"": 0} |
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) |
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return model |
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model = create_and_prepare_model() |
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def generate(instruction, input=None): |
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#Format the prompt to look like the training data |
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if input is not None: |
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prompt = "### Instruction:\n"+instruction+"\n\n### Input:\n"+input+"\n\n### Response:\n" |
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else : |
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prompt = "### Instruction:\n"+instruction+"\n\n### Response:\n" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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input_ids = inputs["input_ids"].cuda() |
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generation_output = model.generate( |
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input_ids=input_ids, |
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repetition_penalty=1.5, |
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generation_config=GenerationConfig(temperature=0.1, top_p=0.75, top_k=40, num_beams=20), #hyperparameters for generation |
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return_dict_in_generate=True, |
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output_scores=True, |
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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 |
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) |
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for seq in generation_output.sequences: |
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output = tokenizer.decode(seq, skip_special_tokens=True) |
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print(output.split("### Response:")[1].strip()) |
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generate("Háblame de la superconductividad.") |
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print("-----------") |
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generate("Encuentra la capital de España.") |
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print("-----------") |
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generate("Encuentra la capital de Portugal.") |
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print("-----------") |
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generate("Organiza los números dados en orden ascendente.", "2, 3, 0, 8, 4, 10") |
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print("-----------") |
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generate("Compila una lista de 5 estados de EE. UU. ubicados en el Oeste.") |
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print("-----------") |
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generate("Compila una lista de 2 estados de EE. UU. ubicados en el Oeste.") |
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print("-----------") |
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generate("Compila una lista de 10 estados de EE. UU. ubicados en el Este.") |
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print("-----------") |
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generate("¿Cuál es el color de una fresa?") |
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print("-----------") |
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generate("¿Cuál es el color de la siguiente fruta?", "fresa") |
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print("-----------") |
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``` |
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Expected output: |
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``` |
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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 |
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La capital de España es Madrid. |
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La capital de Portugal es Lisboa. |
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0, 2, 3, 4, 8, 10 |
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1. California |
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2. Oregón |
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3. Washington |
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4. Nevada |
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5. Arizona |
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California y Washington. |
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1. Maine |
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2. Nuevo Hampshire |
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3. Vermont |
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4. Massachusetts |
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5. Rhode Island |
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6. Connecticut |
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7. Nueva York |
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8. Nueva Jersey |
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9. Pensilvania |
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10. Delaware |
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El color de una fresa es rojo brillante. |
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El color de la fresa es rojo. |
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``` |
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## Contact Us |
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[4i.ai](https://4i.ai/) provides natural language processing solutions with dialog, vision and voice capabilities to deliver real-life multimodal human-machine conversations. |
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Please contact us at [email protected] |
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