File size: 2,017 Bytes
7c7aef2 7bac254 7c7aef2 adf20b0 7c7aef2 adf20b0 7c7aef2 adf20b0 7c7aef2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: dmariko/SmolLM-1.7B-Instruct_qlora_nf4_merged
datasets:
- generator
model-index:
- name: SmolLM_1_7B_Instruct_qlora_nf4-plaba
results: []
license: cc-by-nc-4.0
language:
- en
---
<!-- 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. -->
# SmolLM_1_7B_Instruct_qlora_nf4-plaba
This model is a fine-tuned version of [dmariko/SmolLM-1.7B-Instruct_qlora_nf4_merged](https://huggingface.co/dmariko/SmolLM-1.7B-Instruct_qlora_nf4_merged) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6513
## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.8 | 1 | 1.7222 |
| No log | 1.6 | 2 | 1.7181 |
| No log | 2.4 | 3 | 1.6971 |
| No log | 4.0 | 5 | 1.6586 |
| No log | 4.8 | 6 | 1.6597 |
| No log | 5.6 | 7 | 1.6572 |
| No log | 6.4 | 8 | 1.6539 |
| 1.6809 | 8.0 | 10 | 1.6513 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1 |