peft safetensors qwen2 axolotl generated_from_trainer dataset:medalpaca/medical_meadow_medqa base_model:Qwen/Qwen2.5-7B-Instruct base_model:adapter:Qwen/Qwen2.5-7B-Instruct license:apache-2.0 region:us
---
base_model: Qwen/Qwen2.5-7B-Instruct
datasets:
- medalpaca/medical_meadow_medqa
library_name: peft
license: apache-2.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: lora-qwen-25-7b-instruct
  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
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: 
load_in_4bit:
strict: false

datasets:
  - path: medalpaca/medical_meadow_medqa
    type: alpaca
dataset_prepared_path:
val_set_size: 0.1
output_dir: ./lora-qwen25

sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true


adapter: lora
lora_r: 256
lora_alpha: 128
lora_dropout: 0.05
#lora_target_modules:
#  - q_proj
#  - v_proj
#  - k_proj
#  - o_proj
#  - gate_proj
#  - down_proj
#  - up_proj
lora_target_linear: true

wandb_project: lora-qwen-25-7b-instruct
wandb_entity: 
wandb_watch:
wandb_name: 
wandb_log_model: 

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00001

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
  
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps:
eval_steps: 
save_steps:

evals_per_epoch: 16
saves_per_epoch: 2

debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay:
fsdp:
fsdp_config:
special_tokens:

hub_model_id: neginashz/lora-qwen-25-7b-instruct
hub_strategy: 
early_stopping_patience:

resume_from_checkpoint:
auto_resume_from_checkpoints: true



```

</details><br>

# lora-qwen-25-7b-instruct

This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the medalpaca/medical_meadow_medqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1181

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Use 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: 7
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.774         | 0.0741 | 6    | 2.5571          |
| 1.4649        | 0.1481 | 12   | 1.3144          |
| 0.649         | 0.2222 | 18   | 0.4603          |
| 0.1557        | 0.2963 | 24   | 0.1620          |
| 0.1792        | 0.3704 | 30   | 0.1539          |
| 0.1432        | 0.4444 | 36   | 0.1422          |
| 0.1393        | 0.5185 | 42   | 0.1385          |
| 0.1137        | 0.5926 | 48   | 0.1340          |
| 0.1246        | 0.6667 | 54   | 0.1317          |
| 0.1235        | 0.7407 | 60   | 0.1313          |
| 0.123         | 0.8148 | 66   | 0.1293          |
| 0.1413        | 0.8889 | 72   | 0.1277          |
| 0.1338        | 0.9630 | 78   | 0.1268          |
| 0.1093        | 1.0247 | 84   | 0.1263          |
| 0.1442        | 1.0988 | 90   | 0.1265          |
| 0.1127        | 1.1728 | 96   | 0.1244          |
| 0.137         | 1.2469 | 102  | 0.1231          |
| 0.1098        | 1.3210 | 108  | 0.1224          |
| 0.1276        | 1.3951 | 114  | 0.1223          |
| 0.102         | 1.4691 | 120  | 0.1215          |
| 0.1208        | 1.5432 | 126  | 0.1217          |
| 0.1143        | 1.6173 | 132  | 0.1211          |
| 0.1315        | 1.6914 | 138  | 0.1204          |
| 0.1166        | 1.7654 | 144  | 0.1200          |
| 0.1055        | 1.8395 | 150  | 0.1200          |
| 0.1235        | 1.9136 | 156  | 0.1194          |
| 0.12          | 1.9877 | 162  | 0.1193          |
| 0.0982        | 2.0494 | 168  | 0.1193          |
| 0.1129        | 2.1235 | 174  | 0.1188          |
| 0.1094        | 2.1975 | 180  | 0.1190          |
| 0.1216        | 2.2716 | 186  | 0.1191          |
| 0.1387        | 2.3457 | 192  | 0.1187          |
| 0.1001        | 2.4198 | 198  | 0.1184          |
| 0.1031        | 2.4938 | 204  | 0.1185          |
| 0.0818        | 2.5679 | 210  | 0.1183          |
| 0.126         | 2.6420 | 216  | 0.1185          |
| 0.124         | 2.7160 | 222  | 0.1183          |
| 0.1193        | 2.7901 | 228  | 0.1184          |
| 0.1082        | 2.8642 | 234  | 0.1183          |
| 0.1181        | 2.9383 | 240  | 0.1181          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0