Instructions to use DariaaaS/mistral_instruct_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DariaaaS/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, "DariaaaS/mistral_instruct_generation") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from DariaaaS/mistral_instruct_generation: direct link, hf CLI and curl.
- Browser
- Download file 1.62 kB
-
https://huggingface.co/DariaaaS/mistral_instruct_generation/resolve/main/README.md
- Command line
-
hf download hf://DariaaaS/mistral_instruct_generation/README.md
-
curl -L -o README.md https://huggingface.co/DariaaaS/mistral_instruct_generation/resolve/main/README.md
1.62 kB
metadata
base_model: mistralai/Mistral-7B-Instruct-v0.1
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
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 the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.6932
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.6515 | 0.0024 | 20 | 2.7542 |
| 2.6869 | 0.0048 | 40 | 2.7300 |
| 2.6922 | 0.0073 | 60 | 2.7081 |
| 2.7005 | 0.0097 | 80 | 2.6988 |
| 2.7012 | 0.0121 | 100 | 2.6932 |
Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1