Downloads · 30 days
7
16% of all-time downloads
TinyLlama-1.1B-Chat-v1.0-sft-chat_threads
mjschock/TinyLlama-1.1B-Chat-v1.0-sft-chat_threads
TinyLlama-1.1B-Chat-v1.0-sft-chat_threads is a machine learning model from mjschock. 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 peft.
should probably proofread and complete it, then remove this comment. --
All-time downloads
44
Public
Repo size
2.9 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors289 MB · 99%
At a glance
- Library
- peft
- Access
- Public
- Created
- Nov 9, 2024
- Updated
- Nov 10, 2024
- SHA
- 7fd37375
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. -->
TinyLlama-1.1B-Chat-v1.0-sft-chat_threads
This model is a fine-tuned version of mjschock/TinyLlama-1.1B-Chat-v1.0 on the mjschock/chat_threads dataset. It achieves the following results on the evaluation set:
- Loss: 0.5586
- Bleu: 0.7572
- Precisions: 0.7641
- Brevity Penalty: 0.9983
- Length Ratio: 0.9986
- Translation Length: 582.3552
- Reference Length: 582.9104
- Meteor: 0.7364
- Rouge1: 0.7900
- Rouge2: 0.5570
- Rougel: 0.7250
- Rougelsum: 0.7838
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Meteor | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 0 | 0 | 0.8976 | 0.6391 | 0.6567 | 0.9934 | 0.9936 | 579.7720 | 582.9104 | 0.6775 | 0.6912 | 0.3881 | 0.5809 | 0.6813 |
| 0.7612 | 0.9630 | 13 | 0.7168 | 0.6941 | 0.7056 | 0.9969 | 0.9973 | 581.2681 | 582.9104 | 0.7030 | 0.7375 | 0.4604 | 0.6572 | 0.7281 |
| 0.6321 | 2.0 | 27 | 0.5992 | 0.7420 | 0.7498 | 0.9981 | 0.9981 | 582.0161 | 582.9104 | 0.7312 | 0.7780 | 0.5342 | 0.7069 | 0.7720 |
| 0.5738 | 2.8889 | 39 | 0.5586 | 0.7572 | 0.7641 | 0.9983 | 0.9986 | 582.3552 | 582.9104 | 0.7364 | 0.7900 | 0.5570 | 0.7250 | 0.7838 |
Framework versions
- PEFT 0.13.2
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.19.1
- Library
- peft
- Created
- Nov 9, 2024
- Updated
- Nov 10, 2024