README.md

---
base_model: unsloth/gemma-3n-e4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
license: apache-2.0
language:
- en
---

This finetuned model is specialized in STEM like LCB, CodeForce, AIME24, AIME25, AMC23, MATH500.

Note:
- Currently only text is supported.
- Ollama: ollama run hf.co/unsloth/gemma-3n-E4B-it-GGUF:Q4_K_XL - auto-sets correct chat template and settings
- Set temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0
- Gemma 3n max tokens (context length): 32K. Gemma 3n chat template:


Use unsloth inference
```
!pip install --upgrade transformers

import torch
from transformers import pipeline
model_id = "EpistemeAI/Hercules-Coder-E4B-it"
pipe = pipeline(
    "text-generation", 
    model=model_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)
print(pipe("Write me a Python function to calculate the nth fibonacci number."))
```

Benchmark results (5 shot):
|    Tasks    |Version|Filter|n-shot| Metric |   |Value |
|-------------|------:|------|-----:|--------|---|-----:|
|arc_challenge|      1|none  |     5|acc     |↑  |0.5759|
|hellaswag    |      1|none  |     5|acc     |↑  |0.7651|
|winogrande   |      1|none  |     5|acc     |↑  |0.7526|

GPQA Diamond result
|        Tasks        |Version|  Filter   |n-shot| Metric |   |Value |   
|---------------------|------:|-----------|-----:|--------|---|-----:|
|gpqa_diamond_zeroshot|      1|none       |     0|acc     |↑  |0.2516|
|                     |       |none       |     0|acc_norm|↑  |0.2516|

# Uploaded finetuned  model

- **Developed by:** EpistemeAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3n-e4b-unsloth-bnb-4bit

This gemma3n model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

# Citations
```
@misc{liu2025rstarcoderscalingcompetitivecode,
      title={rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset}, 
      author={Yifei Liu and Li Lyna Zhang and Yi Zhu and Bingcheng Dong and Xudong Zhou and Ning Shang and Fan Yang and Mao Yang},
      year={2025},
      eprint={2505.21297},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.21297}, 
}

```