---
thumbnail: https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/jg2NWmCUfPyzizm2USjMt.jpeg
datasets:
- NewEden/Orion-LIT
- NewEden/Orion-Asstr-Stories-16K
- Mielikki/Erebus-87k
- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
- Nitral-AI/ARES-ShareGPT
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned-20k
- NewEden/Claude-Instruct-2.7K
- NewEden/Claude-Instruct-5K
base_model: Delta-Vector/Hamanasu-15B-Instruct
tags:
- phi
- roleplay
- finetune
- storywriting
- mlx
- mlx-my-repo
---

# aimeri/Hamanasu-15B-Instruct-6bit

The Model [aimeri/Hamanasu-15B-Instruct-6bit](https://huggingface.co/aimeri/Hamanasu-15B-Instruct-6bit) was converted to MLX format from [Delta-Vector/Hamanasu-15B-Instruct](https://huggingface.co/Delta-Vector/Hamanasu-15B-Instruct) using mlx-lm version **0.21.5**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("aimeri/Hamanasu-15B-Instruct-6bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```