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library_name: transformers
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# Model Card for Model ID
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<!-- Provide a longer summary of what this model is. -->
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This is
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- **Developed by:**
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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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 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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Use the code below 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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### 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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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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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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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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license: apache-2.0
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language:
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# Model Card for Model ID
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<!-- Provide a longer summary of what this model is. -->
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This model is Persian Q/A fine-tuned on Google's Gemma open-source model. Users can ask general question from it. It can be used for chatbot applications and fine-tuning for
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other datasets.
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- **Developed by:** Ali Bidaran
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- **Language(s) (NLP):** Farsi
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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## Uses
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This model can be used for developing chatbot applications, Q/A, instruction engineering and fine-tuning with other persian datasets.
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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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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer
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model_id = "alibidaran/Gemma2_Farsi"
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.environ['HF_TOKEN'])
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model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0}, token=os.environ['HF_TOKEN'])
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prompt = "چند روش برای کاهش چربی بدن ارائه نمایید؟"
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text = f"<s> ###Human: {prompt} ###Asistant: "
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inputs=tokenizer(text,return_tensors='pt').to('cuda')
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with torch.no_grad():
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outputs=model.generate(**inputs,max_new_tokens=400,do_sample=True,top_p=0.99,top_k=10,temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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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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