---
library_name: transformers
tags:
- generated_from_trainer
- trl
- sft
base_model: HuggingFaceTB/SmolLM-1.7B-Instruct
datasets: gabrielmbmb/ifeval-trl
model_name: SmolLM-1.7B-Instruct-IFEval
licence: license
model-index:
- name: SmolLM-1.7B-Instruct-IFEval
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 23.06
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gabrielmbmb/SmolLM-1.7B-Instruct-IFEval
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 4.5
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gabrielmbmb/SmolLM-1.7B-Instruct-IFEval
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 0.0
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gabrielmbmb/SmolLM-1.7B-Instruct-IFEval
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 0.45
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gabrielmbmb/SmolLM-1.7B-Instruct-IFEval
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 1.6
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gabrielmbmb/SmolLM-1.7B-Instruct-IFEval
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 1.73
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gabrielmbmb/SmolLM-1.7B-Instruct-IFEval
      name: Open LLM Leaderboard
---

# Model Card for SmolLM-1.7B-Instruct-IFEval

This model is a fine-tuned version of [HuggingFaceTB/SmolLM-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-1.7B-Instruct) on the [gabrielmbmb/ifeval-trl](https://huggingface.co/datasets/gabrielmbmb/ifeval-trl) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import pipeline

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?"
generator = pipeline("text-generation", model="gabrielmbmb/SmolLM-1.7B-Instruct-IFEval", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gabrielmbmb/huggingface/runs/1fz7wyq7)

This model was trained with SFT.

### Framework versions

- TRL: 0.12.0.dev0
- Transformers: 4.45.1
- Pytorch: 2.4.1
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	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},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_gabrielmbmb__SmolLM-1.7B-Instruct-IFEval)

|      Metric       |Value|
|-------------------|----:|
|Avg.               | 5.22|
|IFEval (0-Shot)    |23.06|
|BBH (3-Shot)       | 4.50|
|MATH Lvl 5 (4-Shot)| 0.00|
|GPQA (0-shot)      | 0.45|
|MuSR (0-shot)      | 1.60|
|MMLU-PRO (5-shot)  | 1.73|