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
pip install -U "mlx-lm>=0.31.2"
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:
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:
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 / Q4_K_M 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. 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