---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- generated_from_trainer
model-index:
- name: prm
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
base_model: Qwen/Qwen2.5-7B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Jennny/direct_label_rolls
conversation: qwen-7b-chat
type: sharegpt
split: "train"
train_on_split: "train"
warmup_ratio: 0.05
val_set_size: 0.0
output_dir: ./prm
wandb_project: preference-models
# wandb_entity: domain-generalization
wandb_watch:
wandb_name: "qwen-7b-bs32_lr2e-6_prm"
wandb_log_model:
train_on_inputs: false
save_safetensors: true
#noisy_embedding_alpha: 10.0 # default for sharegpt type
dataset_prepared_path: ~/data/preference-models/last_run_prepared
dataset_processes: 48
#torch_compile: true
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
trust_remote_code: True
adapter:
lora_model_dir:
#lora_r: 32
#lora_alpha: 16
#lora_dropout: 0.05
#lora_target_linear: true
#lora_fan_in_fan_out:
gradient_checkpointing: True
#warmup_ratio: 0.1
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
#max_steps: 10
#optimizer: adamw_torch_fused
optimizer: paged_adamw_32bit
#lr_scheduler: constant_with_warmup
lr_scheduler: cosine
learning_rate: 2.0e-6
weight_decay: 0.0
max_grad_norm: 1.0
group_by_length: false
bf16: auto
fp16: false
tf32: true
early_stopping_patience:
local_rank:
logging_steps: 2
xformers_attention:
flash_attention: true
eval_steps:
eval_table_size:
eval_table_max_new_tokens:
#save_steps: 100
save_strategy: "epoch"
save_total_limit: 4
#save_safetensors: false
debug:
ddp: #true
deepspeed: #deepspeed/zero1.json # multi-gpu only
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
# prm
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0487
## 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: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 3
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0290 | 1 | 3.8909 |
| 3.8462 | 0.0580 | 2 | 3.1606 |
| 3.8462 | 0.0870 | 3 | 1.4003 |
| 2.3026 | 0.1159 | 4 | 0.5247 |
| 2.3026 | 0.1449 | 5 | 0.2535 |
| 0.3725 | 0.1739 | 6 | 0.1224 |
| 0.3725 | 0.2029 | 7 | 0.0711 |
| 0.1704 | 0.2319 | 8 | 0.0705 |
| 0.1704 | 0.2609 | 9 | 0.0842 |
| 0.0719 | 0.2899 | 10 | 0.0684 |
| 0.0719 | 0.3188 | 11 | 0.0837 |
| 0.0719 | 0.3478 | 12 | 0.0794 |
| 0.0719 | 0.3768 | 13 | 0.0679 |
| 0.0729 | 0.4058 | 14 | 0.0607 |
| 0.0729 | 0.4348 | 15 | 0.0682 |
| 0.0639 | 0.4638 | 16 | 0.0660 |
| 0.0639 | 0.4928 | 17 | 0.0607 |
| 0.0659 | 0.5217 | 18 | 0.0609 |
| 0.0659 | 0.5507 | 19 | 0.0599 |
| 0.0584 | 0.5797 | 20 | 0.0595 |
| 0.0584 | 0.6087 | 21 | 0.0579 |
| 0.059 | 0.6377 | 22 | 0.0572 |
| 0.059 | 0.6667 | 23 | 0.0579 |
| 0.1069 | 0.6957 | 24 | 0.0617 |
| 0.1069 | 0.7246 | 25 | 0.0601 |
| 0.0585 | 0.7536 | 26 | 0.0563 |
| 0.0585 | 0.7826 | 27 | 0.0598 |
| 0.097 | 0.8116 | 28 | 0.0590 |
| 0.097 | 0.8406 | 29 | 0.0548 |
| 0.059 | 0.8696 | 30 | 0.0559 |
| 0.059 | 0.8986 | 31 | 0.0570 |
| 0.0695 | 0.9275 | 32 | 0.0548 |
| 0.0695 | 0.9565 | 33 | 0.0554 |
| 0.0533 | 0.9855 | 34 | 0.0564 |
| 0.0533 | 1.0145 | 35 | 0.0541 |
| 0.0544 | 1.0145 | 36 | 0.0548 |
| 0.0544 | 1.0435 | 37 | 0.0555 |
| 0.0555 | 1.0725 | 38 | 0.0531 |
| 0.0555 | 1.1014 | 39 | 0.0532 |
| 0.0524 | 1.1304 | 40 | 0.0536 |
| 0.0524 | 1.1594 | 41 | 0.0519 |
| 0.0641 | 1.1884 | 42 | 0.0520 |
| 0.0641 | 1.2174 | 43 | 0.0522 |
| 0.0494 | 1.2464 | 44 | 0.0514 |
| 0.0494 | 1.2754 | 45 | 0.0511 |
| 0.0502 | 1.3043 | 46 | 0.0514 |
| 0.0502 | 1.3333 | 47 | 0.0511 |
| 0.0482 | 1.3623 | 48 | 0.0505 |
| 0.0482 | 1.3913 | 49 | 0.0511 |
| 0.0472 | 1.4203 | 50 | 0.0509 |
| 0.0472 | 1.4493 | 51 | 0.0498 |
| 0.0478 | 1.4783 | 52 | 0.0498 |
| 0.0478 | 1.5072 | 53 | 0.0502 |
| 0.055 | 1.5362 | 54 | 0.0499 |
| 0.055 | 1.5652 | 55 | 0.0493 |
| 0.0459 | 1.5942 | 56 | 0.0493 |
| 0.0459 | 1.6232 | 57 | 0.0497 |
| 0.0492 | 1.6522 | 58 | 0.0497 |
| 0.0492 | 1.6812 | 59 | 0.0494 |
| 0.0504 | 1.7101 | 60 | 0.0490 |
| 0.0504 | 1.7391 | 61 | 0.0488 |
| 0.0564 | 1.7681 | 62 | 0.0488 |
| 0.0564 | 1.7971 | 63 | 0.0488 |
| 0.0503 | 1.8261 | 64 | 0.0488 |
| 0.0503 | 1.8551 | 65 | 0.0487 |
| 0.0495 | 1.8841 | 66 | 0.0487 |
| 0.0495 | 1.9130 | 67 | 0.0487 |
| 0.0446 | 1.9420 | 68 | 0.0487 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1