transformers safetensors llama text-generation generated_from_trainer conversational en dataset:Scottie201/wandb base_model:HuggingFaceTB/SmolLM2-1.7B-Instruct base_model:finetune:HuggingFaceTB/SmolLM2-1.7B-Instruct autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
datasets:
- Scottie201/wandb
language:
- en
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: fine-tuned-custom-model
  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. -->

# fine-tuned-custom-model

This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) on the Scottie201 dataset.  
It achieves the following results on the evaluation set:
- Loss: 2.3012
- Evaluation Runtime: 125.50 seconds
- Evaluation Samples per Second: 0.096
- Evaluation Steps per Second: 0.048

## Model description

This is a fine-tuned version of the HuggingFaceTB/SmolLM2-1.7B-Instruct model, trained to handle custom tasks related to text generation. The model can handle a wide range of text completion, summarization, and question-answering tasks.

## Intended uses & limitations

The model can be used for tasks like:
- Text generation
- Question answering
- Summarization
- General text-based tasks

### Limitations:
- May not work well for domain-specific tasks that were not part of the training data.
- Performance on unseen data may vary depending on the domain of the query.

## Training and evaluation data

The model was trained and evaluated on the Scottie201 dataset, which includes various text-based tasks. The evaluation was carried out after 2 epochs of training.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- **learning_rate**: 1e-05
- **train_batch_size**: 2
- **eval_batch_size**: 2
- **seed**: 42
- **optimizer**: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08
- **lr_scheduler_type**: linear
- **num_epochs**: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4066        | 1.0   | 25   | 2.3138          |
| 2.3976        | 2.0   | 50   | 2.3012          |

### Evaluation results

| Evaluation Metric           | Value     |
|-----------------------------|-----------|
| **Loss**                    | 2.3012    |
| **Runtime**                 | 125.50 sec|
| **Samples per Second**      | 0.096     |
| **Steps per Second**        | 0.048     |

### Framework versions

- **Transformers**: 4.48.3
- **PyTorch**: 2.6.0+cu124
- **Datasets**: 3.2.0
- **Tokenizers**: 0.21.0

---

This model card now includes detailed information about the evaluation procedure, training results, and evaluation results such as loss, runtime, and throughput. The evaluation metric table provides clarity on how the model performed during evaluation.

Let me know if you'd like any more adjustments or additional details!