Instructions to use nithyanantha/YOUR_OUTPUT_DIR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nithyanantha/YOUR_OUTPUT_DIR with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mathstral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "nithyanantha/YOUR_OUTPUT_DIR") - Notebooks
- Google Colab
- Kaggle
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Download README.md from nithyanantha/YOUR_OUTPUT_DIR: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/nithyanantha/YOUR_OUTPUT_DIR/resolve/main/README.md
- Command line
-
hf download hf://nithyanantha/YOUR_OUTPUT_DIR/README.md
-
curl -L -o README.md https://huggingface.co/nithyanantha/YOUR_OUTPUT_DIR/resolve/main/README.md
1.22 kB
metadata
base_model: mistralai/Mathstral-7B-v0.1
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: YOUR_OUTPUT_DIR
results: []
YOUR_OUTPUT_DIR
This model is a fine-tuned version of mistralai/Mathstral-7B-v0.1 on an unknown 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 5
- training_steps: 500
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1