README.md

---
library_name: transformers
pipeline_tag: text-generation
license: gemma
datasets:
- Crownelius/Opus-4.6-Reasoning-2100x-formatted
base_model:
- google/gemma-4-E4B-it
base_model_relation: adapter
tags:
- gemma
- peft
- lora
- reasoning
- puzzle-solving
- code
---

# Gemma 4 E4B Opus4.6 Reasoning

A PEFT LoRA adapter fine-tuned on top of `google/gemma-4-e4b-it` using the `Crownelius/Opus-4.6-Reasoning-2100x-formatted` dataset.

This adapter is optimized for:

- structured step-by-step reasoning
- logic puzzles
- planning and decomposition
- algorithm explanations
- conceptual problem solving
- code reasoning workflows

The strongest improvements are visible on:
- multi-step logic puzzles
- algorithm design explanations
- state-tracking tasks
- proof-style conceptual reasoning

The adapter shows strongest gains on deliberate decomposition, planning, and educational reasoning prompts.
---

## Base Model
- `google/gemma-4-e4b-it`

## Dataset
- `Crownelius/Opus-4.6-Reasoning-2100x-formatted`

## Training Setup
- PEFT LoRA fine-tuning
- 4-bit QLoRA loading
- 2 training epochs
- training max sequence length: 512 tokens
- gradient accumulation: 16
- trained on Google Colab T4

---

## Training Metrics
- training loss: 192.38
- validation loss: 11.95
- entropy: 3.91
- mean token accuracy: 0.0462
- train runtime: 5783 seconds
- train rows: 2010
- validation rows: 106

## Example Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-4-e4b-it",
    device_map="auto"
)

model = PeftModel.from_pretrained(
    base_model,
    "krishnamraja13/gemma-4-e4b-opus46-reasoning"
)

tokenizer = AutoTokenizer.from_pretrained(
    "krishnamraja13/gemma-4-e4b-opus46-reasoning"
)
```

## Requirements

Use a recent version of `transformers` with Gemma 4 support.

```bash
pip install -U transformers peft accelerate bitsandbytes
```

---

## Known Strengths
This adapter performs best on:

- logic riddles
- switch / state puzzles
- recursive explanation prompts
- dynamic programming intuition
- binary search reasoning
- linked list cycle detection explanations
- proof-style educational prompts
- intermediate reasoning scaffolds and invariant-based explanations
---

## Known Limitations
The adapter is stronger at:

- structured reasoning
- decomposition
- planning
- conceptual explanation

than strict symbolic algebra fidelity.

For exact equation solving, outputs may sometimes over-interpret terse symbolic prompts.

---

## License
This adapter is a derivative of Gemma 4 and follows the Gemma license terms.