Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
+license: apache-2.0
+datasets:
+- argilla/distilabel-intel-orca-dpo-pairs
+language:
+- bg
+- ca
+- cs
+- da
+- de
+- en
+- es
+- fr
+- hr
+- hu
+- it
+- nl
+- pl
+- pt
+- ro
+- ru
+- sl
+- sr
+- sv
+- uk
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
+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
+    
+---
 
 
-## Model Examination [optional]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/YXqUXFjX8uIJT-mdOnM1h.png)
 
-<!-- Relevant interpretability work for the model goes here -->
+```
+reference data model:
 
-[More Information Needed]
+  datasets:
+      link: https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
+      link dataset format gemma chatml: https://huggingface.co/datasets/NickyNicky/distilabel-intel-orca-dpo-pairs_gemma_chatml
 
-## Environmental Impact
+  model:
+    - google/gemma-2b-it
+      Link base:
+        https://huggingface.co/google/gemma-2b-it
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+      Link fine-tune:
+        https://huggingface.co/NickyNicky/gemma-2b-it_oasst2_chatML_Cluster_2_V1
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+  Epoch: 2.67
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+  Future experts: 4 
 
-## Technical Specifications [optional]
+  Eval model:
+    - link:
+        soon
+```
 
-### Model Architecture and Objective
 
-[More Information Needed]
 
-### Compute Infrastructure
+## Loss: train/loss	0.0664
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/u2N1WQnU4nlbyd3O0Qa4K.png)
 
-#### Hardware
+## Re/Accuracies:  train/rewards/accuracies	0.9642857313156128
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/sN7MP6AZWT3X4zvKkLrzq.png)
 
-#### Software
 
-[More Information Needed]
+```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
+```
 
-## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Version
+```py
+import torch
+torch.__version__
+#OUTPUTS: ('2.2.0+cu121' )
+```
 
-**BibTeX:**
+## How to use
+```py
 
-[More Information Needed]
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    HfArgumentParser,
+    TrainingArguments,
+    pipeline,
+    logging,
+    GenerationConfig,
+    TextIteratorStreamer,
+)
 
-**APA:**
+from transformers import StoppingCriteria, StoppingCriteriaList
 
-[More Information Needed]
+import torch
 
-## Glossary [optional]
+model_id='NickyNicky/gemma-2b-it_chatML_distilabel-intel-orca-dpo-pairs_v1'
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+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,
 
-[More Information Needed]
+                                             )
 
-## More Information [optional]
+max_length=2155
+print("max_length",max_length)
 
-[More Information Needed]
 
-## Model Card Authors [optional]
+tokenizer = AutoTokenizer.from_pretrained(model_id,
+                                          # use_fast = False,
+                                          max_length=max_length,)
 
-[More Information Needed]
 
-## Model Card Contact
+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]
 
-[More Information Needed]
+    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=0.1,
+              #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
+```