Instructions to use Angelectronic/mistral-inst_10000_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Angelectronic/mistral-inst_10000_200 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.2-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Angelectronic/mistral-inst_10000_200") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Angelectronic/mistral-inst_10000_200 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 Angelectronic/mistral-inst_10000_200 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 Angelectronic/mistral-inst_10000_200 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Angelectronic/mistral-inst_10000_200 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Angelectronic/mistral-inst_10000_200", max_seq_length=2048, )
mistral-inst_10000_200
This model is a fine-tuned version of unsloth/mistral-7b-instruct-v0.2-bnb-4bit on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3962
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: 4
- seed: 3407
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4844 | 0.31 | 48 | 1.1183 |
| 1.1246 | 0.61 | 96 | 1.0908 |
| 1.1002 | 0.92 | 144 | 1.0744 |
| 0.9854 | 1.23 | 192 | 1.0764 |
| 0.9533 | 1.54 | 240 | 1.0738 |
| 0.9392 | 1.84 | 288 | 1.0642 |
| 0.8426 | 2.15 | 336 | 1.1302 |
| 0.741 | 2.46 | 384 | 1.1290 |
| 0.7343 | 2.76 | 432 | 1.1219 |
| 0.7004 | 3.07 | 480 | 1.2799 |
| 0.5405 | 3.38 | 528 | 1.2481 |
| 0.538 | 3.69 | 576 | 1.2538 |
| 0.5379 | 3.99 | 624 | 1.2606 |
| 0.4171 | 4.3 | 672 | 1.3919 |
| 0.4156 | 4.61 | 720 | 1.3977 |
| 0.4168 | 4.92 | 768 | 1.3962 |
Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.2
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Model tree for Angelectronic/mistral-inst_10000_200
Base model
unsloth/mistral-7b-instruct-v0.2-bnb-4bit