|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- OpenAssistant/oasst2 |
|
language: |
|
- bg |
|
- ca |
|
- cs |
|
- da |
|
- de |
|
- en |
|
- es |
|
- fr |
|
- hr |
|
- hu |
|
- it |
|
- nl |
|
- pl |
|
- pt |
|
- ro |
|
- ru |
|
- sl |
|
- sr |
|
- sv |
|
- uk |
|
|
|
library_name: transformers |
|
|
|
widget: |
|
- text: | |
|
<bos><start_of_turn>system |
|
You are a helpful AI assistant.<end_of_turn> |
|
<start_of_turn>user |
|
What is the meaning of life in the current time?<end_of_turn> |
|
<start_of_turn>model |
|
|
|
--- |
|
|
|
|
|
 |
|
|
|
``` |
|
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 |
|
|
|
Epoch: 7 |
|
|
|
future experts: Cluster_2 |
|
|
|
Eval model: |
|
- link: |
|
soon |
|
|
|
``` |
|
|
|
|
|
## |
|
|
|
|
|
```Python |
|
!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 |
|
```py |
|
import torch |
|
torch.__version__ |
|
#OUTPUTS: ('2.2.0+cu121' ) |
|
``` |
|
|
|
## How to use |
|
```py |
|
|
|
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_chatML_Cluster_2_V1' |
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id, |
|
device_map="auto", |
|
trust_remote_code=True, |
|
torch_dtype=torch.bfloat16, |
|
attn_implementation="flash_attention_2", |
|
# load_in_4bit=True, |
|
# low_cpu_mem_usage= True, |
|
|
|
) |
|
|
|
max_length=2055 |
|
print("max_length",max_length) |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id, |
|
# use_fast = False, |
|
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 |
|
# Codifica cada token de parada y guarda sus IDs en una lista |
|
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): |
|
# Verifica si los últimos tokens generados coinciden con alguno de los conjuntos de tokens de parada |
|
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 |
|
|
|
# Uso del criterio de parada personalizado |
|
stop_tokens = ["<end_of_turn>"] # Lista de tokens de parada |
|
|
|
# Inicializa tu criterio de parada con el tokenizer y la lista de tokens de parada |
|
stopping_criteria = ListOfTokensStoppingCriteria(tokenizer, stop_tokens) |
|
|
|
# Añade tu criterio de parada a una StoppingCriteriaList |
|
stopping_criteria_list = StoppingCriteriaList([stopping_criteria]) |
|
|
|
|
|
|
|
#EXAMPLE #1 |
|
txt="""<bos><start_of_turn>system |
|
You are a helpful AI assistant.<end_of_turn> |
|
<start_of_turn>user |
|
Me dices los diferentes tipos de reciclaje que suelen existir en las ciudades europeas<end_of_turn> |
|
<start_of_turn>model |
|
""" |
|
|
|
#EXAMPLE #2 |
|
txt="""<bos><start_of_turn>system |
|
You are a helpful AI assistant.<end_of_turn> |
|
<start_of_turn>user |
|
What is the meaning of life in the current time?<end_of_turn> |
|
<start_of_turn>model |
|
""" |
|
|
|
|
|
inputs = tokenizer.encode(txt, |
|
return_tensors="pt", |
|
add_special_tokens=False).to("cuda:0") |
|
max_new_tokens=1000 |
|
generation_config = GenerationConfig( |
|
max_new_tokens=max_new_tokens, |
|
temperature=0.55, |
|
#top_p=0.9, |
|
#top_k=len_tokens, |
|
repetition_penalty=1.1, |
|
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) #True |
|
``` |
|
|
