Mixed-precision 4-bit MLX quantization of IFM/K2-Horizon-3.7B, converted from revision 943ce4e. 6.5 bits/weight effective, 4.1 GB on disk. For Apple silicon.
K2-Horizon-3.7B is IFM's small dense K2-Horizon model: a 3.7B decoder-only model with a 512K (524,288-token) context window.
mlx-lm doesn't support the k2_horizon architecture yet. There's an open request: mlx-lm#1876. Until support lands, this repo ships the MLX model code (k2_horizon.py), which mlx-lm loads through the model_file entry in config.json. So the released mlx-lm works as-is:
pip install -U mlx-lm
Pass --trust-remote-code (or trust_remote_code=True). mlx-lm 0.31.3 loads the file without it, but newer versions require it. The file runs on your machine, so read it first if you like; its comments describe how it differs from IFM's PyTorch code.
Once K2-Horizon support lands in a released mlx-lm, this repo will be updated to use it. If something breaks after that, open a discussion here asking for a re-upload.
Mixed precision, chosen from measurements rather than a fixed rule (integer affine quantization, bf16 scales and biases):
4-bit, group size 32: the MLP weights (gate_proj, up_proj, down_proj). These hold most of the parameters.
8-bit, group size 64: everything else: attention, the embeddings and lm_head.
Plain 4-bit (everything 4-bit, group size 64) loses too much on this model: +21.5% WikiText-2 perplexity versus bf16. This recipe loses +4.4%. On all three dense K2-Horizon models, plain 4-bit lost 18-22%, far more than on the 36B MoE model, so the dense 4-bit repos use this mixed recipe.
Peak 4.3 GB for a short prompt; fits a 16 GB Mac. The KV cache adds about 144 KB per token in bf16 (18.0 GB at 128K tokens), so long contexts need more memory; --max-kv-size and KV-cache quantization (--kv-bits 8, where available) reduce it.
The MLX implementation was checked against IFM's PyTorch code (modeling_k2_horizon.py) in fp32 on the real weights of this model:
Layer by layer, all 36 layers match to a relative error of 4e-6 or better, and the next-token predictions agree at every position.
Token by token: the full model in fp32 greedily generated with the KV cache, and the PyTorch model picked the same token at every step (256/256 tokens across English, code, math and Chinese prompts). Cached and uncached outputs also agree at every step.
Smoke-tested after conversion with released mlx-lm 0.31.3: 17 * 23 → 391 and "capital of Australia" → Canberra, both ending normally. On a Mac Studio M4 Max 128GB: 109.6 tok/s generation, peak 4.3 GB (short prompt).
mlx_lm.generate --model mlx-community/K2-Horizon-3.7B-4bit --trust-remote-code --prompt "Explain mixture-of-experts in two sentences." --max-tokens 2048
from mlx_lm import load, generate
# Newer mlx-lm versions need trust_remote_code=True; on mlx-lm 0.31.3 use load("mlx-community/K2-Horizon-3.7B-4bit").
model, tokenizer = load("mlx-community/K2-Horizon-3.7B-4bit", trust_remote_code=True)
messages = [{"role": "user", "content": "Explain mixture-of-experts in two sentences."}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
print(generate(model, tokenizer, prompt, max_tokens=2048))
mlx_lm.chat and mlx_lm.server take the same --trust-remote-code flag.
The model thinks before it answers, inside <ifm|think> … </ifm|think>. Leave room for that in max_tokens. The effort level is set with reasoning_effort in the chat template: "high" (default), "medium" or "low", e.g. apply_chat_template(messages, add_generation_prompt=True, reasoning_effort="low").
Notes:
Server output: mlx-lm doesn't recognize the <ifm|think> tags yet, so mlx_lm.server returns the thinking text inside content, before </ifm|think>, rather than in a separate reasoning field. K2-Horizon's tool-call format isn't parsed yet either.
Chat template change: the original template raises an error when an earlier assistant message has no thinking field, which is what OpenAI-style clients send. The template here renders empty thinking for those messages instead. Nothing else was changed.
Apache-2.0, inherited from the base model. Refer to the original model card for architecture, benchmarks and intended use. All credit for the model belongs to IFM.
Files
10 files · 4.16 GBWeights 4.14 GB (safetensors). Sizes and sha256 from the signed manifest.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("mlx-community/K2-Horizon-3.7B-4bit", revision="3b453d026bd4f433c0bc9a532706f87488269e4d")
model = AutoModelForCausalLM.from_pretrained("mlx-community/K2-Horizon-3.7B-4bit", revision="3b453d026bd4f433c0bc9a532706f87488269e4d", device_map="auto")
mlx-community <huggingface.co/mlx-community>Newsgroups: alt.freeweights.textSubject: 
