81 lines
2.5 KiB
Markdown
81 lines
2.5 KiB
Markdown
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---
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base_model: HuggingFaceTB/135M-lc-100k-rope-12B
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tags:
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- alignment-handbook
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- trl
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- sft
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- generated_from_trainer
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- trl
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- sft
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- generated_from_trainer
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datasets:
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- HuggingFaceTB/magpie-ultra-v1.0-top-300K-short-H4
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- HuggingFaceTB/OpenHermes-2.5-H4-200k
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- HuggingFaceTB/ifeval-like-data-36k-H4
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- HuggingFaceTB/everyday-conversations-llama3.1-2k
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- HuggingFaceTB/self-oss-instruct-sc2-H4
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- HuggingFaceTB/summarization-data-10k-H4
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- HuggingFaceTB/smollm-v2-summarization
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- HuggingFaceTB/smollm-v2-rewriting-50k-H4
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- HuggingFaceTB/explore-instruct-rewrite-H4
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- HuggingFaceTB/LongAlign-16k-ctx-english-H4
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model-index:
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- name: smollm2-135M-8k-lc100k-mix1-ep2
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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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# smollm2-135M-8k-lc100k-mix1-ep2
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This model is a fine-tuned version of [HuggingFaceTB/SmoLLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on [Smol-SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) (the HuggingFaceTB/magpie-ultra-v1.0-top-300K-short-H4, the HuggingFaceTB/OpenHermes-2.5-H4-200k, the HuggingFaceTB/ifeval-like-data-36k-H4, the HuggingFaceTB/everyday-conversations-llama3.1-2k, the HuggingFaceTB/self-oss-instruct-sc2-H4, the HuggingFaceTB/summarization-data-10k-H4, the HuggingFaceTB/smollm-v2-summarization, the HuggingFaceTB/smollm-v2-rewriting-50k-H4, the HuggingFaceTB/explore-instruct-rewrite-H4 and the HuggingFaceTB/LongAlign-16k-ctx-english-H4 datasets).
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It achieves the following results on the evaluation set:
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- Loss: 1.8390
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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: 0.001
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- train_batch_size: 4
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- eval_batch_size: 4
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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: 4
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- total_train_batch_size: 128
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- total_eval_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.2705 | 1.0 | 392 | 1.8649 |
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| 1.1867 | 2.0 | 784 | 1.8390 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.1.2
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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