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  ---
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  library_name: transformers
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- tags: []
 
 
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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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- ### Model Sources [optional]
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-
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- <!-- Provide the basic links for the model. -->
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-
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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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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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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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-
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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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  ## 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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- [More Information Needed]
 
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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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-
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- #### Speeds, Sizes, Times [optional]
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-
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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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-
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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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-
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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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- [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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  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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+ 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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+
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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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+
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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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+
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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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  <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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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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+
 
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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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