Downloads · 30 days
0
fine-tuning-llama
Andyrasika/fine-tuning-llama
fine-tuning-llama is a machine learning model from Andyrasika. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as creativeml-openrail-m.
should probably proofread and complete it, then remove this comment. --
Access
Public
Updated Sep 25, 2023
Repo size
1.3 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt640 MB · 44%
At a glance
- Library
- transformers
- License
- creativeml-openrail-m
- Access
- Public
- Created
- Sep 25, 2023
- Updated
- Sep 25, 2023
- SHA
- 3e68c5ad
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
experiments
This model is a fine-tuned version of TinyPixel/Llama-2-7B-bf16-sharded on the dialogstudio dataset. It achieves the following results on the evaluation set:
- Loss: 1.8522
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9048 | 0.4 | 22 | 1.9220 |
| 1.824 | 0.8 | 44 | 1.8809 |
| 1.6784 | 1.2 | 66 | 1.8619 |
| 1.77 | 1.6 | 88 | 1.8537 |
| 1.6501 | 2.0 | 110 | 1.8522 |
from peft import AutoPeftModelForCausalLM
trained_model = AutoPeftModelForCausalLM.from_pretrained(
"Andyrasika/fine-tuning-llama",
low_cpu_mem_usage=True,
)
merged_model = model.merge_and_unload()
merged_model.save_pretrained("merged_model", safe_serialization=True)
tokenizer.save_pretrained("merged_model")
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Library
- transformers
- License
- creativeml-openrail-m
- Languages
- en
- Created
- Sep 25, 2023
- Updated
- Sep 25, 2023