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anyiwang/mistral_7b_anyi

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README.md CHANGED
@@ -1,57 +1,58 @@
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  ---
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- license: apache-2.0
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- library_name: peft
 
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  tags:
 
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  - trl
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  - sft
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- - generated_from_trainer
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- base_model: mistralai/Mistral-7B-Instruct-v0.2
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- model-index:
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- - name: results
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- results: []
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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- # results
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- This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
 
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- ## Model description
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- More information needed
 
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- ## Intended uses & limitations
 
 
 
 
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- More information needed
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-
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- ## Training and evaluation data
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- More information needed
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- ## Training procedure
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- ### Training hyperparameters
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- The following hyperparameters were used during training:
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- - learning_rate: 0.0002
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- - train_batch_size: 8
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- - eval_batch_size: 8
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- - seed: 42
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- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: constant
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- - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 10
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- ### Training results
 
 
 
 
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- ### Framework versions
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- - PEFT 0.7.1
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- - Transformers 4.36.2
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- - Pytorch 2.1.2
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- - Datasets 2.16.1
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- - Tokenizers 0.15.0
 
 
 
 
 
 
 
 
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  ---
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+ base_model: mistralai/Mistral-7B-Instruct-v0.2
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+ library_name: transformers
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+ model_name: results
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  tags:
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+ - generated_from_trainer
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  - trl
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  - sft
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+ licence: license
 
 
 
 
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  ---
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+ # Model Card for results
 
 
 
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+ This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
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+ It has been trained using [TRL](https://github.com/huggingface/trl).
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+ ## Quick start
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+ ```python
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+ from transformers import pipeline
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+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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+ generator = pipeline("text-generation", model="anyiwang/results", device="cuda")
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+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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+ print(output["generated_text"])
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+ ```
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+ ## Training procedure
 
 
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+
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+ This model was trained with SFT.
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+ ### Framework versions
 
 
 
 
 
 
 
 
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+ - TRL: 0.13.0
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+ - Transformers: 4.47.1
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+ - Pytorch: 2.1.0+cu118
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+ - Datasets: 3.2.0
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+ - Tokenizers: 0.21.0
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+ ## Citations
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+ Cite TRL as:
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+
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+ ```bibtex
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+ @misc{vonwerra2022trl,
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+ title = {{TRL: Transformer Reinforcement Learning}},
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+ author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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+ year = 2020,
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+ journal = {GitHub repository},
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+ publisher = {GitHub},
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+ howpublished = {\url{https://github.com/huggingface/trl}}
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+ }
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+ ```
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