---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-Coder-14B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Qwen2.5-Coder-14B-Instruct-num11
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Qwen2.5-Coder-14B-Instruct-num11

This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) on the fim_midtrain_v1, the fim_midtrain_v2, the fim_midtrain_v3_multi_pairs, the fim_midtrain_v3_multi_pairs_0317, the fim_midtrain_v3_multi_triples and the fim_midtrain_v3_multi_triples_0317 datasets.

## RoPE config fix

This upload keeps the original weights and adds top-level `"rope_theta": 1000000.0` to `config.json` so Qwen2 loaders in current Transformers/vLLM versions do not fall back to the default `10000.0` RoPE base.

## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 64
- 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: 0.1
- num_epochs: 1.0

### Training results



### Framework versions

- Transformers 5.0.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.22.2