reference data model:
datasets:
- lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
link: https://huggingface.co/datasets/NickyNicky/oasst2_clusters
model:
- google/gemma-2b-it
Link:
https://huggingface.co/google/gemma-2b-it
base fine tune: NickyNicky/gemma-2b-it_oasst2_chatML_Cluster_2_V1
Epoch: 2.5
future experts: 5
Eval model:
- link:
soon
train/loss 0.2377
!python -m pip install --upgrade pip
!pip install "torch>=2.1.1" -U
!pip install torchaudio==2.2.0
!pip install -q datasets trl peft bitsandbytes sentencepiece wandb
!pip install -q accelerate safetensors deepspeed
!pip install -q scipy ninja -U
!pip install -q -U transformers==4.38.0
Version
import torch
torch.__version__
How to use
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
GenerationConfig,
TextIteratorStreamer,
)
from transformers import StoppingCriteria, StoppingCriteriaList
import torch
model_id='NickyNicky/gemma-2b-it_oasst2_Cluster_2_aya_dataset_multilingual_chatml_response_json_V1'
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
max_length=1055
print("max_length",max_length)
tokenizer = AutoTokenizer.from_pretrained(model_id,
max_length=max_length,)
class ListOfTokensStoppingCriteria(StoppingCriteria):
"""
Clase para definir un criterio de parada basado en una lista de tokens específicos.
"""
def __init__(self, tokenizer, stop_tokens):
self.tokenizer = tokenizer
self.stop_token_ids_list = [tokenizer.encode(stop_token, add_special_tokens=False) for stop_token in stop_tokens]
def __call__(self, input_ids, scores, **kwargs):
for stop_token_ids in self.stop_token_ids_list:
len_stop_tokens = len(stop_token_ids)
if len(input_ids[0]) >= len_stop_tokens:
if input_ids[0, -len_stop_tokens:].tolist() == stop_token_ids:
return True
return False
stop_tokens = ["<end_of_turn>"]
stopping_criteria = ListOfTokensStoppingCriteria(tokenizer, stop_tokens)
stopping_criteria_list = StoppingCriteriaList([stopping_criteria])
input_text = """James Buchanan es el único presidente que nunca se casó.”"""
input_language_code="es"
targets_traslate= "en"
txt=f"""<bos><start_of_turn>system
You are a helpful AI assistant.
solo responde en formato json.
lista de codigos linguisticos disponibles: ["es", "en", "fr", "de"].<end_of_turn>
<start_of_turn>user
{{
"input": "{input_language_code}",
"targets": "{targets_traslate}",
"inputs_{input_language_code}": "{input_text}",
}}<end_of_turn>
<start_of_turn>model
"""
inputs = tokenizer.encode(txt,
return_tensors="pt",
add_special_tokens=False).to("cuda:0")
max_new_tokens=200
generation_config = GenerationConfig(
max_new_tokens=max_new_tokens,
temperature=0.32,
top_k=45,
repetition_penalty=1.,
do_sample=True,
)
outputs = model.generate(generation_config=generation_config,
input_ids=inputs,
stopping_criteria=stopping_criteria_list,)
tokenizer.decode(outputs[0], skip_special_tokens=False)