---
title: K2-Horizon-0.9B-chat-mlx-16bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/K2-Horizon-0.9B-chat-mlx-16bit"
md_url: "https://www.modelscope.cn/models/mlx-community/K2-Horizon-0.9B-chat-mlx-16bit.md"
repository: mlx-community/K2-Horizon-0.9B-chat-mlx-16bit
last_updated: 2026-10-14
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - k2_horizon
architectures:
  - K2HorizonForCausalLM
base_model:
  - IFM/K2-Horizon-0.9B
base_model_relation: finetune
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 60
stars: 0
tags:
  - mlx
  - safetensors
  - conversational
---

# K2-Horizon-0.9B-chat-mlx-16bit

> K2-Horizon-0.9B-chat-mlx-16bit - mlx-community 在 ModelScope 开源的模型。K2-Horizon-0.9B-mlx-16bit

mlx-community/K2-Horizon-0.9B-chat-mlx-16bit 是 ModelScope 魔搭社区上的text-generation模型，采用 apache-2.0 许可，基于 IFM/K2-Horizon-0.9B 构建。

- **Repository**: mlx-community/K2-Horizon-0.9B-chat-mlx-16bit
- **License**: apache-2.0
- **Tasks**: text-generation
- **Base model**: IFM/K2-Horizon-0.9B
- **Tags**: mlx, safetensors, conversational
- **Downloads**: 60
- **Stars**: 0
- **Last updated**: 2026-10-14

Source: https://www.modelscope.cn/models/mlx-community/K2-Horizon-0.9B-chat-mlx-16bit

---

# K2-Horizon-0.9B-mlx-16bit

> 16-bit MLX conversion of [IFM/K2-Horizon-0.9B](https://huggingface.co/IFM/K2-Horizon-0.9B) for Apple Silicon.

**Converted by**: [SirSahOl](https://huggingface.co/SirSahOl)
**Source model**: [IFM/K2-Horizon-0.9B](https://huggingface.co/IFM/K2-Horizon-0.9B)
**Framework**: [MLX](https://github.com/ml-explore/mlx) by Apple
**Quantization**: 16-bit
**Format**: safetensors
**License**: apache-2.0

---

## Quick Start

### Installation

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

### CLI Usage

```bash
# Chat interactively
mlx_lm.chat --model SirSahOl/K2-Horizon-0.9B-chat-mlx-16bit

# Generate text
mlx_lm.generate --model SirSahOl/K2-Horizon-0.9B-chat-mlx-16bit --prompt "Your prompt here"
```

### Python Usage

```python
from mlx_lm import load, generate

model, tokenizer = load("SirSahOl/K2-Horizon-0.9B-chat-mlx-16bit")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256)
print(response)
```

---

## Performance Benchmarks

| Metric | 8-bit | 16-bit | |--------|--------|--------|| **Tokens/sec** | 47.82 | 26.15 | | **TTFT** | 20.91 ms | 38.26 ms | | **Peak Memory** | 1291.0 MB | 247.6 MB | 
> Benchmarked on **Apple M1** with **8GB** unified memory.
> Average over 5 runs with 256 max tokens.

---

## Who Should Use This?

| Your Hardware | Recommended Quantization |
|---------------|--------------------------|
| M1/M2 (8GB) | **4-bit** — Best balance of quality and memory usage |
| M1/M2 Pro/Max (16-32GB) | **8-bit** — Higher quality with reasonable memory |
| M2/M3/M4 Ultra (64GB+) | **16-bit** — Full precision, no quality loss |

**General guidance:**
- Use **4-bit** if you want to run this model alongside other applications
- Use **8-bit** if you have the memory and want better quality
- Use **16-bit** for research, evaluation, or if memory isn't a concern

---

## Other Quantization Variants

| Variant | Link |
|---------|------|
| 8-bit | [SirSahOl/K2-Horizon-0.9B-chat-mlx-8bit](https://huggingface.co/SirSahOl/K2-Horizon-0.9B-chat-mlx-8bit) |
| 16-bit | [SirSahOl/K2-Horizon-0.9B-chat-mlx-16bit](https://huggingface.co/SirSahOl/K2-Horizon-0.9B-chat-mlx-16bit) |

---

## Conversion Details

| Property | Value |
|----------|-------|
| **Source Model** | [IFM/K2-Horizon-0.9B](https://huggingface.co/IFM/K2-Horizon-0.9B) |
| **Quantization** | 16-bit |
| **mlx-lm Version** | 0.31.3 |
| **Conversion Time** | 8.35s |
| **Output Size** | 2.0 GB |
| **Date** | 2026-09-11T16:56:05.686827+00:00 |

### Reproduction

To reproduce this conversion:

```bash
pip install mlx-lm==0.31.3
python3 -m mlx_lm.convert --hf-path IFM/K2-Horizon-0.9B --mlx-path output/K2-Horizon-0.9B-mlx-16bit
```

---

## Limitations & Known Issues

- Performance may degrade with very long contexts (>8K tokens) at lower quantization levels.
- This is a weight-only conversion; the model architecture and behavior are inherited from the source model.
- Quantization introduces a small quality loss compared to the original model. Lower bit counts = more loss.
- This model requires Apple Silicon (M1 or later) to run with MLX.

---

## License

This model conversion inherits the license of the source model: **apache-2.0**.

See the [original model card](https://huggingface.co/IFM/K2-Horizon-0.9B) for full license details.

---

## Changelog

| Version | Date | Changes |
|---------|------|---------|
| v1.0 | 2026-09-11 | Initial conversion |

---

*Converted with [MLX Foundry](https://github.com/SirSahOl/mlx-foundry) — a professional pipeline for converting models to Apple MLX format.*