|
--- |
|
license: llama3 |
|
library_name: peft |
|
tags: |
|
- axolotl |
|
- generated_from_trainer |
|
base_model: meta-llama/Meta-Llama-3-8B |
|
model-index: |
|
- name: query-gen |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
|
<details><summary>See axolotl config</summary> |
|
|
|
axolotl version: `0.4.0` |
|
```yaml |
|
base_model: meta-llama/Meta-Llama-3-8B |
|
model_type: LlamaForCausalLM |
|
tokenizer_type: AutoTokenizer |
|
|
|
load_in_8bit: false |
|
load_in_4bit: true |
|
strict: false |
|
|
|
hub_model_id: davanstrien/query-gen |
|
|
|
datasets: |
|
- path: davanstrien/query-gen |
|
type: alpaca |
|
dataset_prepared_path: last_run_prepared |
|
val_set_size: 0.05 |
|
output_dir: ./lora-out |
|
|
|
sequence_len: 1024 |
|
sample_packing: true |
|
pad_to_sequence_len: true |
|
|
|
adapter: lora |
|
lora_model_dir: |
|
lora_r: 32 |
|
lora_alpha: 16 |
|
lora_dropout: 0.05 |
|
lora_target_linear: true |
|
lora_fan_in_fan_out: |
|
|
|
wandb_project: axolotl |
|
wandb_entity: |
|
wandb_watch: |
|
wandb_name: query |
|
wandb_log_model: |
|
|
|
gradient_accumulation_steps: 4 |
|
micro_batch_size: 10 |
|
num_epochs: 4 |
|
optimizer: adamw_bnb_8bit |
|
lr_scheduler: cosine |
|
learning_rate: 0.0002 |
|
|
|
train_on_inputs: false |
|
group_by_length: false |
|
bf16: auto |
|
fp16: |
|
tf32: false |
|
|
|
gradient_checkpointing: true |
|
early_stopping_patience: |
|
resume_from_checkpoint: |
|
local_rank: |
|
logging_steps: 1 |
|
xformers_attention: |
|
flash_attention: true |
|
s2_attention: |
|
|
|
warmup_steps: 10 |
|
evals_per_epoch: 4 |
|
eval_table_size: |
|
eval_max_new_tokens: 128 |
|
saves_per_epoch: 1 |
|
debug: |
|
deepspeed: |
|
weight_decay: 0.0 |
|
fsdp: |
|
fsdp_config: |
|
special_tokens: |
|
pad_token: <|end_of_text|> |
|
|
|
``` |
|
|
|
</details><br> |
|
|
|
# query-gen |
|
|
|
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.2679 |
|
|
|
## 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: 10 |
|
- eval_batch_size: 10 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 4 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 160 |
|
- total_eval_batch_size: 40 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 10 |
|
- num_epochs: 4 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:----:|:---------------:| |
|
| 2.8337 | 0.0071 | 1 | 2.8390 | |
|
| 1.414 | 0.2540 | 36 | 1.4018 | |
|
| 1.3212 | 0.5079 | 72 | 1.3332 | |
|
| 1.304 | 0.7619 | 108 | 1.3042 | |
|
| 1.2874 | 1.0159 | 144 | 1.2900 | |
|
| 1.229 | 1.2522 | 180 | 1.2835 | |
|
| 1.2247 | 1.5062 | 216 | 1.2779 | |
|
| 1.2362 | 1.7601 | 252 | 1.2708 | |
|
| 1.2364 | 2.0141 | 288 | 1.2663 | |
|
| 1.1734 | 2.2504 | 324 | 1.2691 | |
|
| 1.1781 | 2.5044 | 360 | 1.2683 | |
|
| 1.1995 | 2.7584 | 396 | 1.2658 | |
|
| 1.1861 | 3.0123 | 432 | 1.2626 | |
|
| 1.1332 | 3.2487 | 468 | 1.2680 | |
|
| 1.1438 | 3.5026 | 504 | 1.2680 | |
|
| 1.1553 | 3.7566 | 540 | 1.2679 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.10.0 |
|
- Transformers 4.40.2 |
|
- Pytorch 2.1.2+cu118 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
