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Qwopus-GLM-18B-Healed
KyleHessling1/Qwopus-GLM-18B-Healed
Qwopus-GLM-18B-Healed is a text generation model from KyleHessling1. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
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.
All-time downloads
3.4K
Public
Parameters
16.6B
33.2 GB on disk
Likes
20
Public
Repo makeup
Click a slice to open those files.
.safetensors33.1 GB · 100%
Parameter types
How the weights are stored.
BF1616.6B · 100%
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Base models
Model card
From the Hugging Face model README
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 Q4_K_M 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)
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:
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. 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
- Task
- Text Generation
- Type
- qwen3_5
- License
- apache-2.0
- Languages
- en, zh, ko, ja, fr, de
- Created
- Apr 18, 2026
- Updated
- Apr 19, 2026