Instructions to use Tohrumi/MistralAI_iwslt15_10000_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Tohrumi/MistralAI_iwslt15_10000_2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Tohrumi/MistralAI_iwslt15_10000_2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Tohrumi/MistralAI_iwslt15_10000_2 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 Tohrumi/MistralAI_iwslt15_10000_2 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 Tohrumi/MistralAI_iwslt15_10000_2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Tohrumi/MistralAI_iwslt15_10000_2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Tohrumi/MistralAI_iwslt15_10000_2", max_seq_length=2048, )
MistralAI_iwslt15_10000_2
This model is a fine-tuned version of unsloth/mistral-7b-bnb-4bit on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0438
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: 8
- eval_batch_size: 8
- seed: 4269
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.1684 | 0.32 | 100 | 1.0926 |
| 1.0883 | 0.64 | 200 | 1.0701 |
| 1.0672 | 0.96 | 300 | 1.0498 |
| 0.9315 | 1.28 | 400 | 1.0547 |
| 0.8973 | 1.6 | 500 | 1.0495 |
| 0.8831 | 1.92 | 600 | 1.0438 |
Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for Tohrumi/MistralAI_iwslt15_10000_2
Base model
unsloth/mistral-7b-bnb-4bit