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---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- generated_from_trainer
datasets:
- aaditya/mimicraw_clinicaltrial_train
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.6.0`
```yaml
base_model: Qwen/Qwen2.5-7B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: aaditya/mimicraw_clinicaltrial_train
type: alpaca
val_set_size: 0.05
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
adapter: qlora
lora_r: 256
lora_alpha: 512
lora_dropout: 0.05
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- down_proj
- up_proj
wandb_project: qwen_mimicrawclinicaltrail
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 6
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 2e-6
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
save_total_limit: 4
```
</details><br>
# out
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the aaditya/mimicraw_clinicaltrial_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6060
## 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: 2e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8273 | 0.0008 | 1 | 0.8615 |
| 0.6312 | 0.3335 | 400 | 0.6677 |
| 0.6221 | 0.6671 | 800 | 0.6416 |
| 0.1335 | 1.0 | 1200 | 0.6267 |
| 0.6062 | 1.3327 | 1600 | 0.6176 |
| 0.5861 | 1.6662 | 2000 | 0.6119 |
| 0.6194 | 1.9998 | 2400 | 0.6084 |
| 0.5953 | 2.3319 | 2800 | 0.6068 |
| 0.6394 | 2.6654 | 3200 | 0.6060 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |