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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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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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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 without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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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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- ### 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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- <!-- 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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- #### 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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- #### 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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- #### 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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- #### 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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- **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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ---
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  library_name: transformers
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+ extra_gated_heading: Access Gemma on Hugging Face
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+ extra_gated_prompt: >-
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+ To access Gemma on Hugging Face, you’re required to review and agree to
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+ Google’s usage license. To do this, please ensure you’re logged-in to Hugging
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+ Face and click below. Requests are processed immediately.
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+ extra_gated_button_content: Acknowledge license
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+ license: other
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+ license_name: gemma-terms-of-use
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+ license_link: https://ai.google.dev/gemma/terms
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+ base_model:
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+ - google/gemma-7b
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+ datasets:
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+ - vicgalle/alpaca-gpt4
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  ---
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+ ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/uwPjZeV-JQwKWrI7nHg4w.webp)
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+
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+ # Gemmalpaca-7B
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+ This is gemma-7b model supervised fine-tuned on the [vicgalle/alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4) dataset. It outperforms gemma-7b-it, Google's chat version, on Nous' benchmark suite.
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+
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+ It's mostly a test to see how fine-tuning works with Gemma models on a well-known dataset.
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+
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+ ## 🔍 Applications
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+ This model has a context length of 8k. I recommend using it with the Alpaca chat template and NOT the Gemma Instruct template (works perfectly with LM Studio). You also want to add `</s>` as a stop token.
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+
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+ ## 🏆 Evaluation
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+ ### Nous
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+ Gemmalpaca-7B outperforms gemma-7b and gemma-7b-it on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
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+ | Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
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+ |---|---:|---:|---:|---:|---:|
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+ | [**mlabonne/Gemmalpaca-7B**](https://huggingface.co/mlabonne/Gemmalpaca-7B) [📄](https://gist.github.com/mlabonne/61622c46e53914a16e11be89d078f66c) | **34.45** | **21.6** | **40.87** | **44.85** | **30.49** |
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+ | [google/gemma-2b](https://huggingface.co/google/gemma-2b) [📄](https://gist.github.com/mlabonne/7df1f238c515a5f63a750c8792cef59e) | 34.26 | 22.7 | 43.35 | 39.96 | 31.03 |
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+ | [google/gemma-7b](https://huggingface.co/google/gemma-7b) [📄](https://gist.github.com/mlabonne/5f9855d341c3b11f775348ecb4fd8cf1) | 33.56 | 20.64 | 38.49 | 46.61 | 28.51 |
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+ | [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) [📄](https://gist.github.com/mlabonne/0fb752dc3c5b578fff87a73c56a16d7a) | 33.53 | 21.33 | 40.84 | 41.7 | 30.25 |
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+
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+ ## 🧩 Configuration
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+ It was trained using [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) with the following configuration.
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+ ```yaml
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+ base_model: alpindale/gemma-7b
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+ model_type: AutoModelForCausalLM
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+ tokenizer_config: philschmid/gemma-tokenizer-chatml
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+ tokenizer_type: AutoTokenizer
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+ tokenizer_use_fast: true
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+
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+ load_in_8bit: false
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+ load_in_4bit: true
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+ strict: false
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+
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+ datasets:
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+ - path: vicgalle/alpaca-gpt4
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+ type: alpaca
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+
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+ dataset_prepared_path:
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+ val_set_size: 0.01
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+ output_dir: ./out
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+
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+ sequence_len: 2048
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+ sample_packing: true
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+ pad_to_sequence_len: true
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+
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+ adapter: qlora
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+ lora_model_dir:
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+ lora_r: 32
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+ lora_alpha: 64
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+ lora_dropout: 0.05
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+ lora_target_linear: true
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+
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+ wandb_project: axolotl
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+ wandb_entity:
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+ wandb_watch:
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+ wandb_name:
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+ wandb_log_model:
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+
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+ gradient_accumulation_steps: 2
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+ micro_batch_size: 4
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+ num_epochs: 3
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+ optimizer: adamw_bnb_8bit
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+ lr_scheduler: cosine
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+ learning_rate: 0.0002
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+ train_on_inputs: false
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+ group_by_length: false
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+ bf16: auto
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+ fp16:
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+ tf32: false
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+
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+ gradient_checkpointing: true
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+ early_stopping_patience:
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+ resume_from_checkpoint:
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+ local_rank:
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+ logging_steps: 1
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+ xformers_attention:
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+ flash_attention: true
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+
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+ warmup_steps: 10
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+ evals_per_epoch: 10
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+ eval_table_size:
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+ eval_table_max_new_tokens: 128
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+ saves_per_epoch: 1
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+ debug:
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+ deepspeed:
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+ weight_decay: 0.1
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+ fsdp:
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+ fsdp_config:
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+ special_tokens:
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+ ```
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+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)