66 lines
1.8 KiB
Markdown
66 lines
1.8 KiB
Markdown
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---
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library_name: transformers
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license: other
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base_model: Qwen/Qwen2.5-Coder-14B-Instruct
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: Qwen2.5-Coder-14B-Instruct-num11
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Qwen2.5-Coder-14B-Instruct-num11
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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.
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## RoPE config fix
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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.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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- total_eval_batch_size: 64
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 0.1
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- num_epochs: 1.0
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### Training results
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### Framework versions
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- Transformers 5.0.0
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- Pytorch 2.6.0+cu124
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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