Update README.md

README.md

@@ -1,199 +1,118 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- turkish
+- general tasks
+- RAG
+- DPO
+license: apache-2.0
+language:
+- tr
+- en
+pipeline_tag: text2text-generation
+base_model: mistralai/Mistral-7B-v0.1
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
+# Model Card for Cymist2-v0.2-DPO
 ### 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]
+Cymist2-v0.2 is a cutting-edge language model developed by the Cypien AI Team, optimized for text-generation tasks. The model leverages the transformers library and is available under the Apache-2.0 license.
 
-### Model Sources [optional]
+- **Developed by:** Cypien AI Team
+- **Model type:** Language Model for Text-Generation
+- **Language(s) (NLP):** Turkish, English
+- **License:** Apache-2.0
+- **Finetuned from model**: mistralai/Mistral-7B-v0.1
 
-<!-- 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]
+This model is designed for direct use in general applications requiring Turkish language understanding, RAG and text-generation capabilities. It can be integrated into chatbots, virtual assistants, and other AI systems where understanding and generating human-like responses are essential.
 
-### Downstream Use [optional]
+### Out-of-Scope Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+The model is not intended for use in critical systems where incorrect answers could lead to harm or in contexts that require domain-specific knowledge beyond the scope of general text-generation.
 
-[More Information Needed]
+## Bias, Risks, and Limitations
 
-### Out-of-Scope Use
+The model, like all AI models, may inherit biases from its training data. Users should be aware of these potential biases and consider them when integrating the model into applications.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-## Bias, Risks, and Limitations
+model_name = "cypienai/cymist-2-v02-DPO-main"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token_id = tokenizer.eos_token_id
+```
+## Use Flash-Attention 2 to further speed-up generation
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+First make sure to install flash-attn. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with:
 
-[More Information Needed]
+```python
+model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.bfloat16,
+        attn_implementation="flash_attention_2"
+        )
+```
 
-### Recommendations
+# Example usage
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Here's the prompt template for this model:
 
-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
+```python
+question="Yenilenebilir gıdalar nelerdir ?"
+prompt= f"[INST] {question} [/INST]"
 
-Use the code below to get started with the model.
+with torch.inference_mode():
+  input_ids = tokenizer(prompt, return_tensors="pt").to(device)
+  output = model.generate(**input_ids, max_new_tokens=8096)
+  decoded_output = tokenizer.decode(output[0], skip_special_tokens=False)
+  print(decoded_output)
+``` 
 
-[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]
+The model was trained on a diverse set of Turkish & English language sources, encompassing a wide range of topics to ensure comprehensive language understanding.
 
 ### 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. -->
+#### Preprocessing
 
-[More Information Needed]
+The training data underwent standard NLP preprocessing steps, including tokenization, normalization, and possibly data augmentation to enhance the model's robustness.
 
-#### 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]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
+The training of Cymist2-v0.1-SFT was conducted with a focus on minimizing carbon emissions. Detailed carbon emission statistics will be provided based on the Machine Learning Impact calculator, considering hardware type, usage hours, cloud provider, compute region, and total emissions.
 
-#### Software
+0.93 kg of CO2eq
 
-[More Information Needed]
-
-## 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. -->
-
-**BibTeX:**
+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).
 
-[More Information Needed]
+## Technical Specifications
 
-**APA:**
+More detailed technical specifications, including model architecture, compute infrastructure, hardware, and software, will be provided to offer insights into the model's operational context.
 
-[More Information Needed]
+## Citation
 
-## Glossary [optional]
+When citing this model in your research, please refer to this model card for information about the model's development and capabilities.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Glossary
 
-[More Information Needed]
+A glossary section can be added to define specific terms and calculations related to the model, ensuring clarity for all potential users.
 
 ## More Information [optional]
 
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+For more information or inquiries about the model, please contact the Cypien AI Team.
 
 ## Model Card Contact
 
-[More Information Needed]
+[email protected]
+
+CypienAI team