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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
-
-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
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **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]
-
-## Technical Specifications [optional]
+---
+license: apache-2.0
+datasets:
+- NickyNicky/oasst2_clusters
+- OpenAssistant/oasst2
+model:
+- google/gemma-2b-it
+language:
+- bg
+- ca
+- cs
+- da
+- de
+- en
+- es
+- fr
+- hr
+- hu
+- it
+- nl
+- pl
+- pt
+- ro
+- ru
+- sl
+- sr
+- sv
+- uk
 
-### Model Architecture and Objective
+library_name: transformers
 
-[More Information Needed]
+widget:
+- text: |
+    <bos><start_of_turn>system
+    You are a helpful AI assistant.<end_of_turn>
+    <start_of_turn>user
+    escribe una historia de 100 palabras<end_of_turn>
+    <start_of_turn>model\n    
+---
 
-### Compute Infrastructure
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/YXqUXFjX8uIJT-mdOnM1h.png)
 
-#### Hardware
+```
+reference data model:
 
-[More Information Needed]
+  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
 
-#### Software
+  model:
+    - google/gemma-2b-it
+      Link:
+        https://huggingface.co/google/gemma-2b-it
 
-[More Information Needed]
+    base fine tune: google/gemma-2b-it
 
-## Citation [optional]
+  Epoch: 3
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+  future experts: 8
 
-**BibTeX:**
+  Eval model:
+    - link:
+        soon
 
-[More Information Needed]
+```
 
-**APA:**
 
-[More Information Needed]
+## train/loss 0.95
 
-## Glossary [optional]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/T_Din2d6NjAt75ImpSOrs.png)
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-[More Information Needed]
+## 
 
-## More Information [optional]
 
-[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
+!pip install flash-attn==2.5.5 --no-build-isolation
+```
 
-## Model Card Authors [optional]
 
-[More Information Needed]
+## Version
+```py
+import torch
+torch.__version__
+#OUTPUTS: ('2.2.0+cu121' )
+```
 
-## Model Card Contact
+## How to use
+```py
 
-[More Information Needed]
+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_all_chatML_Unsloth_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=2048
+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 = f"""<bos><start_of_turn>system
+You are a helpful AI assistant.<end_of_turn>
+<start_of_turn>user
+**News:**
+he Texas Blockchain Council (TBC) and Bitcoin mining firm Riot Platforms have won a favorable ruling from a United States District Judge in a lawsuit against several United States energy officials.
+On February 22, Cointelegraph reported that the TBC and Riot alleged the U.S. Department of Energy, Energy Information Administration (EIA), Office of Management and Budget (OMB) and their leadership sought an “invasive” data collection from cryptocurrency miners.
+According to a February 23 filing in the United States District Court for the Western District of Texas, the TBC and Riot convinced the judge that irreversible harm would happen without a temporary restraining order (TRO) against further data collection.
+As a result, the court enforced a TRO which prohibits the EIA from requiring crypto miners to respond to the survey, as well as prohibiting the EIA from sharing any data that has already been received from the survey.
+“The Court finds that Plaintiffs have shown through a verified complaint and supporting evidence that immediate and irreparable injury, loss, or damage will result if a TRO is not issued.”
+
+
+Instruccion:
+- responde en español.
+- has un análisis sobre el contexto de la noticia y buscar información relevante para poder responder satisfactoriamente.
+- has 5 preguntas importantes y sus respuestas.
+
+en español responde solo en json:
+```json
+{
+  "analisis_noticia": "",
+  "preguntas_respuestas": [
+    {
+      "pregunta": "",
+      "respuesta": ""
+    }
+  ]
+}```<end_of_turn>
+<start_of_turn>model
+"""
+
+inputs = tokenizer.encode(txt,
+                          return_tensors="pt",
+                          add_special_tokens=False).to("cuda:0")
+max_new_tokens=1500
+generation_config = GenerationConfig(
+              max_new_tokens=max_new_tokens,
+              temperature=0.15,
+              # top_p=0.55,
+              top_k=50,
+              repetition_penalty=1.1,
+              do_sample=True,
+          )
+outputs = base_model.generate(generation_config=generation_config,
+                         input_ids=inputs,
+                         stopping_criteria=stopping_criteria_list,)
+print(tokenizer.decode(outputs[0], skip_special_tokens=False) )
+```
+
+```
+'''
+### OUTPUT EXAMPLE
+<start_of_turn>model
+{
+  "analisis_noticia": "Texas Blockchain Council and Bitcoin mining firm Riot Platforms have won a favorable ruling from a United States District Judge in a lawsuit against several United States energy officials.",
+  "preguntas_respuestas": [
+    {
+      "pregunta": "¿Cuál es el objetivo principal del Texas Blockchain Council?",
+      "respuesta": "El objetivo principal del Texas Blockchain Council es promover el uso de las tecnologías blockchain en Texas y en todo el mundo."
+    },
+    {
+      "pregunta": "¿Qué tipo de tecnología blockchain se utiliza más comúnmente en Texas?",
+      "respuesta": "La tecnología blockchain utilizada más comúnmente en Texas es la criptomoneda Bitcoin."
+    },
+    {
+      "pregunta": "¿Cómo se utilizan las criptomonedas en el ámbito empresarial y gubernamental en Texas?",
+      "respuesta": "Las criptomonedas son utilizadas por empresas y gobiernos gubernamentales en Texas para mejorar la eficiencia y seguridad en el proceso de pago."
+    },
+    {
+      "pregunta": "¿Qué medidas están siendo tomadas para proteger los derechos de propiedad intelectual y la privacidad de los ciudadanos en Texas?",
+      "respuesta": "Texas está trabajando junto con otras entidades gubernamentales y organizaciones empresariales para desarrollar leyes que protegen los derechos de propiedad intelectual y la privacidad de los ciudadanos."
+    },
+    {
+      "pregunta": "¿Cómo se espera que las nuevas tecnologías de blockchain impacten el futuro económico y social de Texas?",
+      "respuesta": "Se espera que estas nuevas tecnologías de blockchain tengan un impacto positivo en el futuro económico y social de Texas al permitir una mayor transparencia, eficiencia y seguridad en el sistema financiero."
+    }
+  ]
+}<end_of_turn>
+'''
+```