|
--- |
|
license: llama2 |
|
library_name: peft |
|
tags: |
|
- generated_from_trainer |
|
base_model: codellama/CodeLlama-7b-hf |
|
model-index: |
|
- name: outputs/lora-out |
|
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: codellama/CodeLlama-7b-hf |
|
model_type: LlamaForCausalLM |
|
tokenizer_type: CodeLlamaTokenizer |
|
|
|
load_in_8bit: true |
|
load_in_4bit: false |
|
strict: false |
|
|
|
datasets: |
|
- path: AayushMathur/manim_python_alpaca |
|
type: alpaca |
|
dataset_prepared_path: |
|
val_set_size: 0.05 |
|
output_dir: ./outputs/lora-out |
|
|
|
sequence_len: 4096 |
|
sample_packing: false |
|
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: |
|
wandb_entity: |
|
wandb_watch: |
|
wandb_name: |
|
wandb_log_model: |
|
|
|
gradient_accumulation_steps: 4 |
|
micro_batch_size: 2 |
|
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 |
|
saves_per_epoch: 1 |
|
debug: |
|
deepspeed: |
|
weight_decay: 0.0 |
|
fsdp: |
|
fsdp_config: |
|
special_tokens: |
|
bos_token: "<s>" |
|
eos_token: "</s>" |
|
unk_token: "<unk>" |
|
|
|
``` |
|
|
|
</details><br> |
|
|
|
# outputs/lora-out |
|
|
|
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0039 |
|
|
|
## 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: 2 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 8 |
|
- 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 | |
|
|:-------------:|:------:|:----:|:---------------:| |
|
| 0.7377 | 0.0140 | 1 | 0.7414 | |
|
| 0.1444 | 0.2526 | 18 | 0.0560 | |
|
| 0.0349 | 0.5053 | 36 | 0.0280 | |
|
| 0.0429 | 0.7579 | 54 | 0.0206 | |
|
| 0.0625 | 1.0105 | 72 | 0.0251 | |
|
| 0.0496 | 1.2632 | 90 | 0.0157 | |
|
| 0.032 | 1.5158 | 108 | 0.0126 | |
|
| 0.0094 | 1.7684 | 126 | 0.0104 | |
|
| 0.0453 | 2.0211 | 144 | 0.0087 | |
|
| 0.0005 | 2.2737 | 162 | 0.0104 | |
|
| 0.0373 | 2.5263 | 180 | 0.0069 | |
|
| 0.0262 | 2.7789 | 198 | 0.0056 | |
|
| 0.0088 | 3.0316 | 216 | 0.0048 | |
|
| 0.0266 | 3.2842 | 234 | 0.0045 | |
|
| 0.013 | 3.5368 | 252 | 0.0041 | |
|
| 0.0141 | 3.7895 | 270 | 0.0039 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.10.0 |
|
- Transformers 4.40.2 |
|
- Pytorch 2.1.2+cu118 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
