---
base_model: HuggingFaceTB/135M-lc-100k-rope-12B
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceTB/magpie-ultra-v1.0-top-300K-short-H4
- HuggingFaceTB/OpenHermes-2.5-H4-200k
- HuggingFaceTB/ifeval-like-data-36k-H4
- HuggingFaceTB/everyday-conversations-llama3.1-2k
- HuggingFaceTB/self-oss-instruct-sc2-H4
- HuggingFaceTB/summarization-data-10k-H4
- HuggingFaceTB/smollm-v2-summarization
- HuggingFaceTB/smollm-v2-rewriting-50k-H4
- HuggingFaceTB/explore-instruct-rewrite-H4
- HuggingFaceTB/LongAlign-16k-ctx-english-H4
model-index:
- name: smollm2-135M-8k-lc100k-mix1-ep2
  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. -->

# smollm2-135M-8k-lc100k-mix1-ep2

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).
It achieves the following results on the evaluation set:
- Loss: 1.8390

## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2705        | 1.0   | 392  | 1.8649          |
| 1.1867        | 2.0   | 784  | 1.8390          |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1