Instructions to use SamChen888/Mistral-PT-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SamChen888/Mistral-PT-Finetuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.3-bnb-4bit") model = PeftModel.from_pretrained(base_model, "SamChen888/Mistral-PT-Finetuned") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use SamChen888/Mistral-PT-Finetuned with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SamChen888/Mistral-PT-Finetuned to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SamChen888/Mistral-PT-Finetuned to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SamChen888/Mistral-PT-Finetuned to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="SamChen888/Mistral-PT-Finetuned", max_seq_length=2048, )
metadata
library_name: peft
license: other
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- llama-factory
- lora
- unsloth
- generated_from_trainer
model-index:
- name: train_finetuned_en
results: []
train_finetuned_en
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on the train_novo 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 3.0
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3