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End of training

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  1. README.md +5 -6
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@@ -1,7 +1,7 @@
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  ---
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  base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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  library_name: transformers
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- model_name: mistral_arq2pt_v2
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  tags:
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  - generated_from_trainer
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  - unsloth
@@ -10,7 +10,7 @@ tags:
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  licence: license
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  ---
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- # Model Card for mistral_arq2pt_v2
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  This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3-bnb-4bit).
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  It has been trained using [TRL](https://github.com/huggingface/trl).
@@ -21,24 +21,23 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
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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="SEMEVAL-11/mistral_arq2pt_v2", 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: 2.5.1
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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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  ---
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  base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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  library_name: transformers
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+ model_name: mistral_arq2pt_v1
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  tags:
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  - generated_from_trainer
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  - unsloth
 
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  licence: license
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  ---
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+ # Model Card for mistral_arq2pt_v1
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  This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3-bnb-4bit).
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  It has been trained using [TRL](https://github.com/huggingface/trl).
 
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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="SEMEVAL-11/mistral_arq2pt_v1", 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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  This model was trained with SFT.
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  ### Framework versions
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+ - TRL: 0.12.1
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  - Transformers: 4.47.1
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  - Pytorch: 2.5.1
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+ - Datasets: 3.1.0
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  - Tokenizers: 0.21.0
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  ## Citations