End of training
Browse files- README.md +59 -0
- adapter_model.safetensors +1 -1
- all_results.json +8 -0
- config.json +57 -0
- train_results.json +8 -0
- trainer_state.json +1162 -0
README.md
ADDED
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| 1 |
+
---
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| 2 |
+
datasets: open-r1/OpenR1-Math-220k
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| 3 |
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library_name: transformers
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| 4 |
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model_name: Qwen2.5-1.5B-Open-R1-Distill-ScaleTuning-lightkernel
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| 5 |
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tags:
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| 6 |
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- generated_from_trainer
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| 7 |
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- open-r1
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| 8 |
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- trl
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| 9 |
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- sft
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| 10 |
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licence: license
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| 11 |
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---
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| 12 |
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| 13 |
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# Model Card for Qwen2.5-1.5B-Open-R1-Distill-ScaleTuning-lightkernel
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| 14 |
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This model is a fine-tuned version of [None](https://huggingface.co/None) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset.
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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| 19 |
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| 20 |
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```python
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| 21 |
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from transformers import pipeline
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| 22 |
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| 23 |
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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| 24 |
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generator = pipeline("text-generation", model="chentianqi/Qwen2.5-1.5B-Open-R1-Distill-ScaleTuning-lightkernel", device="cuda")
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| 25 |
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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| 26 |
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print(output["generated_text"])
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```
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## Training procedure
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This model was trained with SFT.
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### Framework versions
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| 37 |
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- TRL: 0.16.0.dev0
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| 39 |
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- Transformers: 4.49.0
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| 40 |
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- Pytorch: 2.5.1+cu124
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- Datasets: 3.4.1
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- Tokenizers: 0.21.1
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| 43 |
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## Citations
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| 47 |
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| 48 |
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Cite TRL as:
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| 49 |
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| 50 |
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```bibtex
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| 51 |
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@misc{vonwerra2022trl,
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| 52 |
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title = {{TRL: Transformer Reinforcement Learning}},
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| 53 |
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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| 54 |
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year = 2020,
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| 55 |
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journal = {GitHub repository},
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| 56 |
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publisher = {GitHub},
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| 57 |
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howpublished = {\url{https://github.com/huggingface/trl}}
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| 58 |
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}
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```
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adapter_model.safetensors
CHANGED
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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| 3 |
size 20497672
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| 1 |
version https://git-lfs.github.com/spec/v1
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oid sha256:910c251d3f52345339ea72eacbe2ea8eb8354abb65c1cf3e96332d0f06135a91
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size 20497672
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all_results.json
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{
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"total_flos": 1.3060333048430592e+16,
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"train_loss": 0.6017354608175174,
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| 4 |
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"train_runtime": 40505.6177,
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| 5 |
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"train_samples": 93733,
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| 6 |
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"train_samples_per_second": 2.54,
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| 7 |
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"train_steps_per_second": 0.02
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}
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config.json
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{
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| 2 |
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"_attn_implementation_autoset": true,
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| 3 |
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"_name_or_path": "/home/chentianqi/model/Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int4",
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| 4 |
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"architectures": [
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| 5 |
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"Qwen2ForCausalLM"
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],
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| 7 |
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"attention_dropout": 0.0,
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| 8 |
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"bos_token_id": 151643,
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| 9 |
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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| 12 |
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"initializer_range": 0.02,
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| 13 |
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"intermediate_size": 8960,
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| 14 |
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"max_position_embeddings": 32768,
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| 15 |
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"max_window_layers": 21,
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| 16 |
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"model_type": "qwen2",
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| 17 |
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"num_attention_heads": 12,
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| 18 |
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"quantization_config": {
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"backend": "auto",
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"batch_size": 1,
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"bits": 4,
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| 24 |
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"block_name_to_quantize": null,
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| 25 |
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"cache_block_outputs": true,
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| 26 |
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"checkpoint_format": "gptq",
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| 27 |
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"damp_percent": 0.01,
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"dataset": null,
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| 29 |
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"desc_act": false,
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"exllama_config": {
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| 31 |
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"version": 1
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},
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"group_size": 128,
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| 34 |
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"max_input_length": null,
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"meta": null,
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| 36 |
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"model_seqlen": null,
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| 37 |
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"module_name_preceding_first_block": null,
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| 38 |
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"modules_in_block_to_quantize": null,
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| 39 |
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"pad_token_id": null,
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"quant_method": "gptq",
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| 41 |
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"sym": true,
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| 42 |
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"tokenizer": null,
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"true_sequential": true,
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"use_cuda_fp16": false,
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| 45 |
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"use_exllama": true
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},
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| 47 |
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"rms_norm_eps": 1e-06,
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| 48 |
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"rope_scaling": null,
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| 49 |
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"rope_theta": 1000000.0,
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| 50 |
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"sliding_window": 32768,
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| 51 |
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"tie_word_embeddings": true,
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| 52 |
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"torch_dtype": "bfloat16",
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| 53 |
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"transformers_version": "4.49.0",
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| 54 |
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"use_cache": true,
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| 55 |
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"use_sliding_window": false,
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| 56 |
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"vocab_size": 151936
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}
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train_results.json
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{
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"total_flos": 1.3060333048430592e+16,
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| 3 |
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"train_loss": 0.6017354608175174,
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| 4 |
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"train_runtime": 40505.6177,
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| 5 |
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"train_samples": 93733,
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| 6 |
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"train_samples_per_second": 2.54,
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| 7 |
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"train_steps_per_second": 0.02
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}
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trainer_state.json
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