SirSahOl/K2-Horizon-7B-chat-mlx-16bit

K2-Horizon-7B-mlx-16bit

16-bit MLX conversion of IFM/K2-Horizon-7B optimized for Apple Silicon native GPU inference.

Converted by: SirSahOl Source Model: IFM/K2-Horizon-7B Framework: MLX by Apple Quantization: 16-bit (Average 16.00 (unquantized bfloat16) bits per weight) Format: safetensors License: apache-2.0

Model Details

• —Architecture: K2HorizonForCausalLM
• —Parameters: 7B
• —Context Length: 524,288 tokens
• —Format: MLX (Apple Silicon native GPU format)
• —Quantization: 16-bit (Average 16.00 (unquantized bfloat16) bits per weight)
• —Active VRAM Footprint: ~15.2 GB (Minimum recommended: 24 GB – 32 GB Unified Memory)

Quick Start

Installation

pip install mlx-lm

CLI Usage

# Chat interactively
mlx_lm.chat --model SirSahOl/K2-Horizon-7B-chat-mlx-16bit

# Generate text
mlx_lm.generate --model SirSahOl/K2-Horizon-7B-chat-mlx-16bit --prompt "Write a short poem about artificial intelligence."

Python API (with Chat Template)

from mlx_lm import load, generate

model, tokenizer = load("SirSahOl/K2-Horizon-7B-chat-mlx-16bit")

messages = [
    {"role": "user", "content": "Explain quantum superposition in simple terms."}
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

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

Performance Benchmarks

Apple Silicon Hardware Sizing Matrix

Estimated decoding throughput, time-to-first-token (TTFT), and active unified memory footprint across Apple Silicon tiers:

Estimates based on Apple Silicon unified memory bandwidth and active parameter footprint. Real-world speeds may vary with context length.

Multi-Quantization Comparison

Evaluate your hardware budget and choose the optimal precision:

Who Should Use This?

General guidance:

• —Use 4-bit if you want to run this model alongside IDEs, browsers, and background development tools.
• —Use 8-bit if you have 16GB+ unified memory and require superior reasoning and code accuracy.
• —Use 16-bit for research, benchmarking, evaluation, or high-end workstation deployments.

Other Quantization Variants

LM Studio & Local Inference Setup Guide

To prevent runaway loops and ensure correct conversational turn-taking, configure custom stop strings in your local inference runtime.

Custom Stop Strings Configuration

Ensure the following sequence tokens are configured as strict stop boundaries:

1. 1.<|im_start|>
2. 2.<|im_end|>
3. 3.<|endoftext|>

Option A: Automatic Preset (Recommended for LM Studio)

Create a custom prompt preset JSON file named ChatML.json in your LM Studio config directory:

• —macOS / Linux: ~/.lmstudio/config-presets/ChatML.json
• —Windows: %USERPROFILE%\.lmstudio\config-presets\ChatML.json

{
  "name": "ChatML",
  "inference_params": {
    "pre_prompt": "You are a helpful, respectful, and honest assistant.",
    "input_prefix": "<|im_start|>user\n",
    "input_suffix": "<|im_end|>\n",
    "pre_prompt_prefix": "<|im_start|>system\n",
    "pre_prompt_suffix": "<|im_end|>\n",
    "antiprompt": [
      "<|im_start|>",
      "<|im_end|>",
      "<|endoftext|>"
    ],
    "stopStrings": [
      "<|im_start|>",
      "<|im_end|>",
      "<|endoftext|>"
    ],
    "temperature": 0.7,
    "max_tokens": 2048
  }
}

Restart LM Studio, load SirSahOl/K2-Horizon-7B-chat-mlx-16bit, and select "ChatML" from the Prompt Template dropdown.

Option B: Manual LM Studio Configuration

1. 1.Load SirSahOl/K2-Horizon-7B-chat-mlx-16bit in LM Studio.
2. 2.In the right-hand Advanced Configuration / Inference Parameters panel:
3. 3.Stop Strings (antiprompt / stopStrings): Add <|im_start|>, <|im_end|>, <|endoftext|>
4. 4.System Prefix: <|im_start|>system\n
5. 5.System Suffix: <|im_end|>\n
6. 6.User Prefix: <|im_start|>user\n
7. 7.Assistant Suffix: <|im_end|>\n<|im_start|>assistant\n

Option C: Ollama Modelfile Setup

Create a Modelfile to run this model in Ollama:

FROM SirSahOl/K2-Horizon-7B-chat-mlx-16bit

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER temperature 0.7

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""

Create and run with Ollama:

ollama create k2-horizon-7b-chat-mlx-16bit -f Modelfile
ollama run k2-horizon-7b-chat-mlx-16bit

Conversion Details

Reproduction

To reproduce this conversion:

pip install mlx-lm==0.31.3
python3 -m mlx_lm.convert --hf-path /root/.cache/huggingface/hub/models--IFM--K2-Horizon-7B/snapshots/ff325e226270e05ea081a97fd0c9c62652472fe8 --mlx-path output/K2-Horizon-7B-mlx-16bit

Limitations & Known Issues

• —4-bit group-wise quantization introduces minor precision loss compared to unquantized weights; for deep mathematical derivations or precision-critical reasoning, test the 8-bit or 16-bit variants.
• —High context sequences (>32K tokens) require sufficient unified memory headroom; ensure unified memory is not overcommitted.
• —This is a weight-only MLX conversion designed specifically for Apple Silicon GPUs (M1/M2/M3/M4 series).

License

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

See the original model card for full license details.

Changelog

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