Instructions to use isaaclee/mistral_train_run3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use isaaclee/mistral_train_run3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "isaaclee/mistral_train_run3") - Notebooks
- Google Colab
- Kaggle
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Download README.md from isaaclee/mistral_train_run3: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
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https://huggingface.co/isaaclee/mistral_train_run3/resolve/main/README.md
- Command line
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hf download hf://isaaclee/mistral_train_run3/README.md
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curl -L -o README.md https://huggingface.co/isaaclee/mistral_train_run3/resolve/main/README.md
1.25 kB
metadata
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: mistral_train_run3
results: []
mistral_train_run3
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
Training results
Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1