README.md

---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
model-index:
- name: Fox-1-1.6B
  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: 27.66
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tensoropera/Fox-1-1.6B
      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: 7.4
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tensoropera/Fox-1-1.6B
      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: 1.28
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tensoropera/Fox-1-1.6B
      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: 1.79
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tensoropera/Fox-1-1.6B
      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: 3.87
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tensoropera/Fox-1-1.6B
      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: 4.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tensoropera/Fox-1-1.6B
      name: Open LLM Leaderboard
---

## Model Card for Fox-1-1.6B

> [!IMPORTANT]  
> This model is a base pretrained model which requires further finetuning for most use cases.
> For a more interactive experience, we
> recommend [tensoropera/Fox-1-1.6B-Instruct-v0.1](https://huggingface.co/tensoropera/Fox-1-1.6B-Instruct-v0.1), the
> instruction-tuned version of Fox-1.

Fox-1 is a decoder-only transformer-based small language model (SLM) with 1.6B total parameters developed
by [TensorOpera AI](https://tensoropera.ai/). The model was trained with a 3-stage data curriculum on 3 trillion
tokens of text and code data in 8K sequence length. Fox-1 uses Grouped Query Attention (GQA) with 4 key-value heads and
16 attention heads for faster inference.

For the full details of this model please read [Fox-1 technical report](https://arxiv.org/abs/2411.05281)
and [release blog post](https://blog.tensoropera.ai/tensoropera-unveils-fox-foundation-model-a-pioneering-open-source-slm-leading-the-way-against-tech-giants).

## Benchmarks

We evaluated Fox-1 on ARC Challenge (25-shot), HellaSwag (10-shot), TruthfulQA (0-shot), MMLU (5-shot),
Winogrande (5-shot), and GSM8k (5-shot). We follow the Open LLM Leaderboard's evaluation setup and report the average
score of the 6 benchmarks. The model was evaluated on a machine with 8*H100 GPUs.

|               | Fox-1-1.6B | Qwen-1.5-1.8B | Gemma-2B | StableLM-2-1.6B | OpenELM-1.1B |
|---------------|------------|---------------|----------|-----------------|--------------|
| GSM8k         | 36.39%     | 34.04%        | 17.06%   | 17.74%          | 2.27%        |
| MMLU          | 43.05%     | 47.15%        | 41.71%   | 39.16%          | 27.28%       |
| ARC Challenge | 41.21%     | 37.20%        | 49.23%   | 44.11%          | 36.26%       |
| HellaSwag     | 62.82%     | 61.55%        | 71.60%   | 70.46%          | 65.23%       |
| TruthfulQA    | 38.66%     | 39.37%        | 33.05%   | 38.77%          | 36.98%       |
| Winogrande    | 60.62%     | 65.51%        | 65.51%   | 65.27%          | 61.64%       |
| Average       | 47.13%     | 46.81%        | 46.36%   | 45.92%          | 38.28%       |

# [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_tensoropera__Fox-1-1.6B)

|      Metric       |Value|
|-------------------|----:|
|Avg.               | 7.69|
|IFEval (0-Shot)    |27.66|
|BBH (3-Shot)       | 7.40|
|MATH Lvl 5 (4-Shot)| 1.28|
|GPQA (0-shot)      | 1.79|
|MuSR (0-shot)      | 3.87|
|MMLU-PRO (5-shot)  | 4.13|