---
base_model: google/gemma-2-2b
datasets:
- cognitivecomputations/Dolphin-2.9
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- mlabonne/FineTome-100k
- arcee/agent_data
- PawanKrd/math-gpt-4o-200k
- cognitivecomputations/SystemChat-2.0
license: gemma
tags:
- generated_from_trainer
- mlx
---

# cmcmaster/rheum-dolphin-2.9.4-gemma2-2b

The Model [cmcmaster/rheum-dolphin-2.9.4-gemma2-2b](https://huggingface.co/cmcmaster/rheum-dolphin-2.9.4-gemma2-2b) was converted to MLX format from [cognitivecomputations/dolphin-2.9.4-gemma2-2b](https://huggingface.co/cognitivecomputations/dolphin-2.9.4-gemma2-2b) using mlx-lm version **0.18.0**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("cmcmaster/rheum-dolphin-2.9.4-gemma2-2b")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```