Instructions to use maraoz/mistral_instruct_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use maraoz/mistral_instruct_generation 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, "maraoz/mistral_instruct_generation") - Notebooks
- Google Colab
- Kaggle
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Download README.md from maraoz/mistral_instruct_generation: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
-
https://huggingface.co/maraoz/mistral_instruct_generation/resolve/main/README.md
- Command line
-
hf download hf://maraoz/mistral_instruct_generation/README.md
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curl -L -o README.md https://huggingface.co/maraoz/mistral_instruct_generation/resolve/main/README.md
1.67 kB
metadata
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: mistral_instruct_generation
results: []
mistral_instruct_generation
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4259
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7768 | 0.02 | 20 | 1.5506 |
| 1.5974 | 0.04 | 40 | 1.4599 |
| 1.5168 | 0.06 | 60 | 1.4403 |
| 1.5212 | 0.08 | 80 | 1.4321 |
| 1.3018 | 0.1 | 100 | 1.4259 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0