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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
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
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