Instructions to use DrishtiSharma/llama2-7b-int4-dolly-15k-english-unsloth-w-packing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DrishtiSharma/llama2-7b-int4-dolly-15k-english-unsloth-w-packing with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-2-7b") model = PeftModel.from_pretrained(base_model, "DrishtiSharma/llama2-7b-int4-dolly-15k-english-unsloth-w-packing") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use DrishtiSharma/llama2-7b-int4-dolly-15k-english-unsloth-w-packing 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 DrishtiSharma/llama2-7b-int4-dolly-15k-english-unsloth-w-packing 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 DrishtiSharma/llama2-7b-int4-dolly-15k-english-unsloth-w-packing to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DrishtiSharma/llama2-7b-int4-dolly-15k-english-unsloth-w-packing to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="DrishtiSharma/llama2-7b-int4-dolly-15k-english-unsloth-w-packing", max_seq_length=2048, )
llama2-7b-int4-dolly-15k-english-unsloth-w-packing
This model is a fine-tuned version of unsloth/llama-2-7b on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 1.2198
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: 6
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2675 | 0.64 | 100 | 1.2318 |
| 1.1937 | 1.27 | 200 | 1.2221 |
| 1.1728 | 1.91 | 300 | 1.2178 |
| 1.1459 | 2.55 | 400 | 1.2198 |
Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
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Base model
unsloth/llama-2-7b