Instructions to use Angelectronic/llama3-chat_10000_500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Angelectronic/llama3-chat_10000_500 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Angelectronic/llama3-chat_10000_500") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use Angelectronic/llama3-chat_10000_500 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/llama3-chat_10000_500 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/llama3-chat_10000_500 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Angelectronic/llama3-chat_10000_500 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Angelectronic/llama3-chat_10000_500", max_seq_length=2048, )
llama3-chat_10000_500
This model is a fine-tuned version of unsloth/llama-3-8b-Instruct-bnb-4bit on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8491
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: 4
- total_train_batch_size: 32
- 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 |
|---|---|---|---|
| 1.765 | 0.33 | 104 | 1.4841 |
| 1.5146 | 0.67 | 208 | 1.4604 |
| 1.4912 | 1.0 | 312 | 1.4545 |
| 1.3584 | 1.33 | 416 | 1.4698 |
| 1.358 | 1.66 | 520 | 1.4671 |
| 1.3483 | 2.0 | 624 | 1.4637 |
| 1.1105 | 2.33 | 728 | 1.5471 |
| 1.101 | 2.66 | 832 | 1.5512 |
| 1.1007 | 3.0 | 936 | 1.5522 |
| 0.8526 | 3.33 | 1040 | 1.7081 |
| 0.8445 | 3.66 | 1144 | 1.7156 |
| 0.8463 | 3.99 | 1248 | 1.7115 |
| 0.6865 | 4.33 | 1352 | 1.8423 |
| 0.6811 | 4.66 | 1456 | 1.8458 |
| 0.6859 | 4.99 | 1560 | 1.8491 |
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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Base model
unsloth/llama-3-8b-Instruct-bnb-4bit