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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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### Model Architecture and Objective
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[More Information Needed]
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[
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#### Software
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## Citation [optional]
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---
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license: apache-2.0
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datasets:
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- argilla/distilabel-intel-orca-dpo-pairs
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language:
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- bg
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- ca
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- cs
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- da
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- de
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- en
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- es
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- fr
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- hr
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- hu
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- it
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- nl
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- pl
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- pt
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- ro
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- ru
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- sl
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- sr
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- sv
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- uk
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library_name: transformers
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widget:
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- text: |
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<bos><start_of_turn>system
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You are a helpful AI assistant.<end_of_turn>
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<start_of_turn>user
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What is the meaning of life in the current time?<end_of_turn>
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<start_of_turn>model
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/YXqUXFjX8uIJT-mdOnM1h.png)
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```
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reference data model:
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datasets:
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link: https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
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link dataset format gemma chatml: https://huggingface.co/datasets/NickyNicky/distilabel-intel-orca-dpo-pairs_gemma_chatml
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model:
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- google/gemma-2b-it
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Link base:
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https://huggingface.co/google/gemma-2b-it
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Link fine-tune:
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https://huggingface.co/NickyNicky/gemma-2b-it_oasst2_chatML_Cluster_2_V1
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Epoch: 2.67
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Future experts: 4
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Eval model:
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- link:
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soon
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```
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## Loss: train/loss 0.0664
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/u2N1WQnU4nlbyd3O0Qa4K.png)
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## Re/Accuracies: train/rewards/accuracies 0.9642857313156128
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/sN7MP6AZWT3X4zvKkLrzq.png)
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```Python
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!python -m pip install --upgrade pip
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!pip install "torch>=2.1.1" -U
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# !pip install torchaudio==2.2.0
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!pip install -q datasets trl peft bitsandbytes sentencepiece wandb
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!pip install -q accelerate safetensors deepspeed
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!pip install -q scipy ninja -U
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!pip install -q -U transformers==4.38.0
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```
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## Version
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```py
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import torch
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torch.__version__
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#OUTPUTS: ('2.2.0+cu121' )
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```
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## How to use
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```py
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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BitsAndBytesConfig,
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HfArgumentParser,
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TrainingArguments,
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pipeline,
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logging,
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GenerationConfig,
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TextIteratorStreamer,
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)
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from transformers import StoppingCriteria, StoppingCriteriaList
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import torch
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model_id='NickyNicky/gemma-2b-it_chatML_distilabel-intel-orca-dpo-pairs_v1'
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model = AutoModelForCausalLM.from_pretrained(model_id,
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device_map="auto",
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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# load_in_4bit=True,
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# low_cpu_mem_usage= True,
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)
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max_length=2155
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print("max_length",max_length)
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tokenizer = AutoTokenizer.from_pretrained(model_id,
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# use_fast = False,
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max_length=max_length,)
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class ListOfTokensStoppingCriteria(StoppingCriteria):
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"""
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Clase para definir un criterio de parada basado en una lista de tokens específicos.
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"""
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def __init__(self, tokenizer, stop_tokens):
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self.tokenizer = tokenizer
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# Codifica cada token de parada y guarda sus IDs en una lista
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self.stop_token_ids_list = [tokenizer.encode(stop_token, add_special_tokens=False) for stop_token in stop_tokens]
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def __call__(self, input_ids, scores, **kwargs):
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# Verifica si los últimos tokens generados coinciden con alguno de los conjuntos de tokens de parada
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for stop_token_ids in self.stop_token_ids_list:
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len_stop_tokens = len(stop_token_ids)
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if len(input_ids[0]) >= len_stop_tokens:
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if input_ids[0, -len_stop_tokens:].tolist() == stop_token_ids:
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return True
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return False
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# Uso del criterio de parada personalizado
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stop_tokens = ["<end_of_turn>"] # Lista de tokens de parada
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# Inicializa tu criterio de parada con el tokenizer y la lista de tokens de parada
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stopping_criteria = ListOfTokensStoppingCriteria(tokenizer, stop_tokens)
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# Añade tu criterio de parada a una StoppingCriteriaList
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stopping_criteria_list = StoppingCriteriaList([stopping_criteria])
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#EXAMPLE #1
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txt="""<bos><start_of_turn>system
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You are a helpful AI assistant.<end_of_turn>
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<start_of_turn>user
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Me dices los diferentes tipos de reciclaje que suelen existir en las ciudades europeas<end_of_turn>
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<start_of_turn>model
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"""
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#EXAMPLE #2
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txt="""<bos><start_of_turn>system
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You are a helpful AI assistant.<end_of_turn>
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<start_of_turn>user
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What is the meaning of life in the current time?<end_of_turn>
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<start_of_turn>model
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"""
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inputs = tokenizer.encode(txt,
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return_tensors="pt",
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add_special_tokens=False).to("cuda:0")
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max_new_tokens=1000
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generation_config = GenerationConfig(
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max_new_tokens=max_new_tokens,
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temperature=0.1,
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#top_p=0.9,
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#top_k=len_tokens,
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repetition_penalty=1.1,
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do_sample=True,
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)
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outputs = model.generate(generation_config=generation_config,
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input_ids=inputs,
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stopping_criteria=stopping_criteria_list,)
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tokenizer.decode(outputs[0], skip_special_tokens=False) #True
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```
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