
<div style="text-align:center;width:250px;height:250px;">
    <img src="https://huggingface.co/mrm8488/pomeranian/resolve/main/pomeranian-removebg-preview.png" alt="pomeranian logo"">
</div>

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# PomeranIAn

## Adapter Description
This adapter was created with the [PEFT](https://github.com/huggingface/peft) library and allowed the base model **Falcon-7b** to be fine-tuned on the **Spanish instructions dataset** by using the method **QLoRA**.

## Model description

[Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)

## Intended uses & limitations

TBA

## Training and evaluation data

TBA

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6302        | 1.54  | 10   | 1.6542          |
| 1.5742        | 3.08  | 20   | 1.6157          |
| 1.3779        | 4.62  | 30   | 1.5896          |
| 1.2988        | 6.15  | 40   | 1.5753          |


### How to use
```py
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer, GenerationConfig

peft_model_id = "mrm8488/pomeranian"

config = PeftConfig.from_pretrained(peft_model_id)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

tokenizer = AutoTokenizer.from_pretrained(peft_model_id)

model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,
                                             return_dict=True,
                                             quantization_config=bnb_config, 
                                             trust_remote_code=True, 
                                             device_map={"":0})


prompt_input = "A continuación, se muestra una instrucción que describe una tarea, junto con una entrada que proporciona más contexto. Escribe una respuesta que complete adecuadamente la solicitud.\n\n### Instrucción:\n{instruction}\n\n### Entrada:\n{input}\n\n### Respuesta:\n"
prompt_no_input = "A continuación, se muestra una instrucción que describe una tarea. Escribe una respuesta que complete adecuadamente la solicitud.\n\n### Instrucción:\n{instruction}\n\n### Respuesta:\n"

def create_prompt(instruction, input=None):
  if input:
    return  prompt_input.format(instruction=instruction, input=input)
  else:
    return prompt_no_input.format(instruction=instruction)

def generate(
        instruction,
        input=None,
        max_new_tokens=128,
        temperature=0.1,
        top_p=0.75,
        top_k=40,
        num_beams=4,
        **kwargs,
):
    prompt = create_prompt(instruction, input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].to("cuda")
    attention_mask = inputs["attention_mask"].to("cuda")
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        **kwargs,
    )
    with torch.no_grad():
        generation_output = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            generation_config=generation_config,
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=max_new_tokens
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    return output.split("### Respuesta:")[1]

instruction = "Dime algo sobre los halcones"

print("Instrucción:", instruction)
print("Respuesta:", generate(instruction))
```


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["es"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "thumbnail": "https://huggingface.co/mrm8488/pomeranian/resolve/main/pomeranian-removebg-preview.png", "model-index": [{"name": "PomeranIAn", "results": []}]}
tensorboard generated_from_trainer es doi:10.57967/hf/0742 license:apache-2.0 region:us
TAGS
#tensorboard #generated_from_trainer #es #doi-10.57967/hf/0742 #license-apache-2.0 #region-us 


![](URL alt=)

PomeranIAn
==========


Adapter Description
-------------------


This adapter was created with the PEFT library and allowed the base model Falcon-7b to be fine-tuned on the Spanish instructions dataset by using the method QLoRA.


Model description
-----------------


Falcon 7B


Intended uses & limitations
---------------------------


TBA


Training and evaluation data
----------------------------


TBA


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 8


### Training results



### How to use


### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 8 ### Training results ### How to use ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#tensorboard #generated_from_trainer #es #doi-10.57967/hf/0742 #license-apache-2.0 #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 8 ### Training results ### How to use ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3