metadata
datasets:
- DIBT/10k_prompts_ranked
- NickyNicky/10k_prompts_ranked_all_chatml_json_gemma
- NickyNicky/10k_prompts_ranked_all
model:
- NickyNicky/gemma-2b-it_oasst2_chatML_Cluster_2_V1
language:
- en
library_name: transformers
widget:
- text: >
<bos><start_of_turn>system
You are a prompt evaluator response format json.
ngrams_length: "8" | cluster_length: "15".
lista de codigos linguisticos disponibles: ["en", "en"].<end_of_turn>
<start_of_turn>user
### |detect_prompt|:
What were the main contributions of Eratosthenes to the development of
mathematics in ancient Greece?<end_of_turn>
<start_of_turn>model\n
license: apache-2.0
reference data model:
datasets:
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
future experts: test
Eval model:
- link:
soon
train/loss 0.5407
!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
!pip install flash-attn==2.5.5 --no-build-isolation
Version
import torch
torch.__version__
#OUTPUTS: ('2.2.0+cu121' )
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_chatML_Cluster2_aya_multilingual'
model_id= "NickyNicky/gemma-2b-it_oasst2_chatML_Cluster2_aya_multilingual_10k_prompts_ranked_all_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=2100
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])
prompt="""What were the main contributions of Eratosthenes to the development of mathematics in ancient Greece?"""
#EXAMPLE #1
input_text = f'''<bos><start_of_turn>system
You are a prompt evaluator response format json.
ngrams_length: "8" | cluster_length: "15".
lista de codigos linguisticos disponibles: ["en", "en"].<end_of_turn>
<start_of_turn>user
### |detect_prompt|:
{prompt}<end_of_turn>
<start_of_turn>model
'''
### OUTPUT EXAMPLE
'''
{
"ngrams_length": "8",
"ngrams": ["main", "contribution", "eratosthenes", "development", "mathematic", "ancient", "greece", "ancient greece"],
"cluster_length": "15",
"cluster": ["quantum", "magnetic", "star", "metal", "planet", "gravity", "force", "universe", "distance", "compound", "gravitational", "quantum computing", "solar", "sun", "earth"],
"cluster_desc": ["Astrophysics", "Quantum Computing"],
"avg_rating": "5.0",
"kind": "synthetic"
}<end_of_turn><eos>
'''
inputs = tokenizer.encode(input_text,
return_tensors="pt",
add_special_tokens=False).to("cuda:0")
max_new_tokens=700
generation_config = GenerationConfig(
max_new_tokens=max_new_tokens,
temperature=0.32,
#top_p=0.9,
top_k=45,
repetition_penalty=1., #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
code
https://colab.research.google.com/drive/1z26uLnTZWZ994G_dgyghNzh4hF2eEA6Z?usp=sharing
generated dataset model NickyNicky/prompts_ranked_808.
https://huggingface.co/datasets/NickyNicky/prompts_ranked_808