Modelpublic

KyleHessling1/Qwopus-GLM-18B-Healed-MLX-4bit

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Model Card

Qwopus-GLM-18B-Healed — MLX 4-bit

Apple Silicon / MLX 4-bit quantization of the healed Qwopus-GLM-18B frankenmerge. Ready to run on Macs with the MLX framework via `mlx-lm`.

—Source (BF16): KyleHessling1/Qwopus-GLM-18B-Healed
—Q4_K_M GGUF (llama.cpp): KyleHessling1/Qwopus-GLM-18B-Merged-GGUF

Quickstart

bash

pip install -U "mlx-lm>=0.31.2"

python

from mlx_lm import load, generate

model, tokenizer = load("KyleHessling1/Qwopus-GLM-18B-Healed-MLX-4bit")
print(generate(model, tokenizer, prompt="The capital of France is", max_tokens=64))

Or from the CLI:

bash

python3 -m mlx_lm generate \
  --model KyleHessling1/Qwopus-GLM-18B-Healed-MLX-4bit \
  --prompt "Write a haiku about Apple Silicon." \
  --max-tokens 128

Runs comfortably on a 16–24 GB unified-memory Mac (M-series).

Quantization

Property	Value
Method	MLX affine quantization (`mlx_lm.convert -q`)
Bits / weight	4 (effective 4.502 after non-quantized layers)
Group size	64
Non-quant dtype	bfloat16
Output size	~8.4 GB (2 safetensor shards)
Quantizer version	`mlx-lm` 0.31.2 / `mlx` 0.31.1

Reproducible from the BF16 source with:

bash

python3 -m mlx_lm convert \
  --hf-path KyleHessling1/Qwopus-GLM-18B-Healed \
  --mlx-path ./Qwopus-GLM-18B-Healed-MLX-4bit \
  -q --q-bits 4 --q-group-size 64

Base Model

A 64-layer frankenmerge of two of Jackrong's Qwen3.5-9B finetunes, healed with a 1000-step QLoRA fine-tune:

—Layers 0–31: Jackrong/Qwopus3.5-9B-v3.5 (Opus reasoning distill)
—Layers 32–63: Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1 (GLM-5.1 reasoning distill)
—Heal training: 1000 steps QLoRA (rank 64) on Jackrong's training data to smooth the layer boundary

Architecture

Property	Value
Parameters	~18B
Layers	64 (32 + 32)
Hidden Size	4096
Attention Heads	16 (4 KV heads, GQA)
Attention Type	Hybrid (linear + full, every 4th layer)
Context Length	262,144 tokens
Source Precision	BF16

Capability Suite (from base model)

Beats Qwen 3.6-35B-A3B MoE on a 44-test capability suite at less than half the VRAM:

	Qwopus-GLM-18B (healed)	Qwen 3.6-35B MoE
Score	40/44 (90.9%)	38/44 (86.4%)
Tool Calling	6/6	6/6
Agentic	4/4	4/4
Programming	12/15	12/15

Frontend stress tests: 62/63 checks passed across 6 complex HTML/CSS/JS generation tasks with perfectly balanced braces/parens and zero garbled output.

Note: benchmarks were measured on the BF16 base / Q4KM GGUF. The MLX 4-bit weights are a separate quantization and have not been independently re-benchmarked — expect quality within normal 4-bit quantization variance.

Known Issues

—The tokenizer emits a Mistral-regex warning on load (inherited from the source repo). Benign for Qwen tokenization in practice.

Credits

All credit for the source models goes to [Jackrong](https://huggingface.co/Jackrong). The heal training used his published datasets. See the full merge documentation for the complete technical workflow.

MLX quantization by @KyleHessling1 using mlx-lm.

License

Apache 2.0 (inherited from source models)

Contact

Questions, issues, or cool projects? Reach out on X: @KyleHessling1