Instructions to use mwrites/mistral_instruct_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mwrites/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, "mwrites/mistral_instruct_generation") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from mwrites/mistral_instruct_generation: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
-
https://huggingface.co/mwrites/mistral_instruct_generation/resolve/main/README.md
- Command line
-
hf download hf://mwrites/mistral_instruct_generation/README.md
-
curl -L -o README.md https://huggingface.co/mwrites/mistral_instruct_generation/resolve/main/README.md
1.67 kB
metadata
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
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 the generator dataset. It achieves the following results on the evaluation set:
- Loss: 1.3366
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
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.5239 | 0.83 | 20 | 1.3754 |
| 1.4225 | 1.67 | 40 | 1.3335 |
| 1.3316 | 2.5 | 60 | 1.3258 |
| 1.3123 | 3.33 | 80 | 1.3280 |
| 1.3232 | 4.17 | 100 | 1.3366 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0