---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: MetaMath-Qwen2.5-0.5b-PRM
  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: `0.4.1`
```yaml
base_model: Qwen/Qwen2.5-0.5B
bf16: auto
dataset_prepared_path: /training/data/prepared
datasets:
- conversation: llama3
  path: RLHFlow/Mistral-PRM-Data
  split: train
  train_on_split: train
  type: sharegpt
flash_attention: true
fp16: false
gradient_accumulation_steps: 4
gradient_checkpointing: true
hub_model_id: rawsh/MetaMath-Qwen2.5-0.5b-PRM
hub_strategy: every_save
learning_rate: 2.0e-06
load_in_4bit: false
load_in_8bit: false
logging_steps: 2
lr_scheduler: cosine
max_grad_norm: 1.0
micro_batch_size: 1
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: paged_adamw_32bit
output_dir: /training/prm
pad_to_sequence_len: true
push_to_hub: true
sample_packing: true
save_safetensors: true
save_strategy: epoch
save_total_limit: 4
sequence_len: 8192
special_tokens:
  pad_token: <|endoftext|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_name: qwen2.5-0.5b-bs32_lr2e-6_prm
wandb_project: preference-models
warmup_ratio: 0.05
weight_decay: 0.0

```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dankgpt/preference-models/runs/eqqhapl0)
# MetaMath-Qwen2.5-0.5b-PRM

This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on the None dataset.

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 214
- num_epochs: 1

### Training results



### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1