Modelpublic

KyleHessling1/Qwopus-GLM-18B-Healed

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Model Card

Qwopus-GLM-18B-Healed — Base Weights

Full BF16 safetensors for the healed Qwopus-GLM-18B frankenmerge. Use these to create your own quantizations (GGUF, AWQ, GPTQ, EXL2, etc.) or as a base for further fine-tuning.

For a ready-to-use Q4KM GGUF, see: KyleHessling1/Qwopus-GLM-18B-Merged-GGUF

What This Is

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

Model Details

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
Precision	BF16
Total Size	~31 GB (7 safetensor shards)

Performance

Beats Qwen 3.6-35B-A3B MoE on our 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
Q4_K_M Size	9.2 GB	22 GB

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

Quantizing

GGUF (llama.cpp)

bash

python3 convert_hf_to_gguf.py \
  KyleHessling1/Qwopus-GLM-18B-Healed \
  --outfile Qwopus-GLM-18B-healed-f16.gguf \
  --outtype bf16

llama-quantize \
  Qwopus-GLM-18B-healed-f16.gguf \
  Qwopus-GLM-18B-healed-Q4_K_M.gguf \
  Q4_K_M

AutoGPTQ / AWQ / EXL2

Load with standard HuggingFace transformers and quantize with your preferred tool:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "KyleHessling1/Qwopus-GLM-18B-Healed",
    torch_dtype="bfloat16",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("KyleHessling1/Qwopus-GLM-18B-Healed")

Files

model-00001-of-00007.safetensors  (5.0 GB)
model-00002-of-00007.safetensors  (5.0 GB)
model-00003-of-00007.safetensors  (5.0 GB)
model-00004-of-00007.safetensors  (5.0 GB)
model-00005-of-00007.safetensors  (5.0 GB)
model-00006-of-00007.safetensors  (5.0 GB)
model-00007-of-00007.safetensors  (1.1 GB)
model.safetensors.index.json
config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja

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.

License

Apache 2.0 (inherited from source models)

Contact

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