mrSoul7766
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README.md
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library_name: transformers
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# Model Card for Model ID
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- **Model type:** google/gemma-2b
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- **Finetuned from model [optional]:** google/gemma-2b-it
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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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### 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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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
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###
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<!-- This section
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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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## 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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### 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:**
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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
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<!-- This should link to a Dataset Card if possible. -->
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[
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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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### Results
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[More Information Needed]
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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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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:**
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- **Hours used:**
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- **Cloud Provider:**
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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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#### Software
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**BibTeX:**
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[More Information Needed]
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**APA:**
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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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metrics:
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- bleu : 0.67
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- chrf : 0.73
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---
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# Model Card for Model ID
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- **Model type:** google/gemma-2b
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- **Finetuned from model [optional]:** google/gemma-2b-it
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## Uses
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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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Use this model to generate Python code."
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```python
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "mrSoul7766/gemma-2b-it-python-code-gen-adapter"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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text = """<start_of_turn>Convert JSON data to a CSV file<end_of_turn>
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<start_of_turn>model"""
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#device = "cuda:0"
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=200)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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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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This model is trained on very basic Python code, so it might not be able to handle complex code.
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## How to Get Started with the Model
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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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**Fine-tuning Data:** [flytech/python-codes-25k](https://huggingface.co/datasets/flytech/python-codes-25k/viewer/default/train?p=2&row=294)
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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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#### Training Hyperparameters
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- **Training regime:** fp16 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- **learning_rate:** 2e-4
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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 & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca?row=44)
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#### Metrics
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- **chrf:** 0.73
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- **codeblue:** 0.67
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- **codeblue_ngram:** 0.53
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### Results
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[More Information Needed]
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```python
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import json
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import pandas as pd
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# Load the JSON data
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with open('data.json', 'r') as f:
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data = json.load(f)
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# Create the DataFrame
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df = pd.DataFrame(data)
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```
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#### Summary
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## Environmental Impact
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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:** H100
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- **Hours used:** 30 minutes
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- **Cloud Provider:** Google-cloud
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## Technical Specifications [optional]
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### Model Architecture and Objective
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#### Hardware
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- **Hardware Type:** H100
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- **Hours used:** 30 minutes
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- **Cloud Provider:** Google-cloud
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#### Software
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- bitsandbytes==0.42.0
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- peft==0.8.2
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- trl==0.7.10
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- accelerate==0.27.1
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- datasets==2.17.0
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- transformers==4.38.0
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