Instructions to use SEMEVAL-11/mistral_trackb_dadosORIGINAISINGLES_3epocas with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEMEVAL-11/mistral_trackb_dadosORIGINAISINGLES_3epocas with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SEMEVAL-11/mistral_trackb_dadosORIGINAISINGLES_3epocas", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use SEMEVAL-11/mistral_trackb_dadosORIGINAISINGLES_3epocas with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SEMEVAL-11/mistral_trackb_dadosORIGINAISINGLES_3epocas to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SEMEVAL-11/mistral_trackb_dadosORIGINAISINGLES_3epocas to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SEMEVAL-11/mistral_trackb_dadosORIGINAISINGLES_3epocas to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="SEMEVAL-11/mistral_trackb_dadosORIGINAISINGLES_3epocas", max_seq_length=2048, )
Training in progress, step 900
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README.md
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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:
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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
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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/
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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.
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- Transformers: 4.
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- Pytorch: 2.5.1
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- Datasets: 3.
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- Tokenizers: 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_trackb_dadosORIGINAIS5epocas
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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_trackb_dadosORIGINAIS5epocas
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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_trackb_dadosORIGINAIS5epocas", 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: 4.46.3
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- Pytorch: 2.5.1
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- Datasets: 3.1.0
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- Tokenizers: 0.20.3
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## Citations
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adapter_model.safetensors
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