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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: openlm-research/open_llama_3b_v2
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: openlm-research/open_llama_3b_v2
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
push_dataset_to_hub:
datasets:
- path: "./raft_oracle_context_alpaca.json"
type: alpaca
dataset_prepared_path: ./dataset-pre
val_set_size: 0.02
adapter: lora
lora_model_dir:
sequence_len: 1024
sample_packing: true
lora_r: 8
lora_alpha: 16
lora_dropout: 0.0
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/lora-out
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: false
fp16: true
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
gptq_groupsize:
s2_attention:
gptq_model_v1:
warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
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 [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4426
## 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
- distributed_type: multi-GPU
- num_devices: 7
- total_train_batch_size: 14
- total_eval_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.9628 | 0.0022 | 1 | 1.9150 |
| 0.5816 | 0.2505 | 115 | 0.6157 |
| 0.3604 | 0.5011 | 230 | 0.4307 |
| 0.2598 | 0.7516 | 345 | 0.3558 |
| 0.2227 | 1.0022 | 460 | 0.3434 |
| 0.1381 | 1.2266 | 575 | 0.3376 |
| 0.0718 | 1.4771 | 690 | 0.3372 |
| 0.0684 | 1.7277 | 805 | 0.3608 |
| 0.0817 | 1.9782 | 920 | 0.3663 |
| 0.0315 | 2.2004 | 1035 | 0.3888 |
| 0.0331 | 2.4510 | 1150 | 0.4003 |
| 0.0222 | 2.7015 | 1265 | 0.4145 |
| 0.0222 | 2.9521 | 1380 | 0.4216 |
| 0.0166 | 3.1743 | 1495 | 0.4330 |
| 0.017 | 3.4248 | 1610 | 0.4391 |
| 0.0142 | 3.6754 | 1725 | 0.4426 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1