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---
license: apache-2.0
datasets:
- NickyNicky/aya_dataset_multilingual_chatml_gemma_response_json
model:
- NickyNicky/gemma-2b-it_oasst2_chatML_Cluster_2_V1
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.
solo responde en formato json.
lista de codigos linguisticos disponibles: ["es", "en", "fr", "de"].<end_of_turn>
<start_of_turn>user
{
"input": "fr",
"targets": "es",
"inputs_fr": "Quels président des États-Unis ne s’est jamais marié ?",
}<end_of_turn>
<start_of_turn>model\n
---
```
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
!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_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",
# load_in_4bit=True,
# low_cpu_mem_usage= True,
)
max_length=1055
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
input_text = """James Buchanan es el único presidente que nunca se casó.”"""
input_language_code="es"
#The 'targets' -key- with its respective value is for a response according to the language.
targets_traslate= "en" # English response regarding language code -> "es", "en", "fr", "de"
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
"""
### OUTPUT EXAMPLE
###'''
###<start_of_turn>model
###{
### "targets": "en",
### "targets_es": ""
###}<end_of_turn>
###'''
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_p=0.9,
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) #True
```
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