🚀 Updated July 2026 — Now with AgentWorld + Opus 4.8 Reasoning!
📦 Available Models
| Folder | Description | Size |
|---|---|---|
model_agent/ 🏆 | AgentWorld + Fable + Opus 4.8 (recommended) | 54 GB |
model_chat/ | Fable SFT + Opus 4.8 (chat focused) | 54 GB |
📥 GGUF Downloads
| File | Format | Size | Description |
|---|---|---|---|
GGUF/...AgentWorld-fable-opus4.8.Q4_K_M.gguf | Q4_K_M | 20 GB | Recommended |
GGUF/...AgentWorld-fable-opus4.8.f16.gguf | F16 | 65 GB | Full precision |
GGUF/...Fable-opus4.8-CHAT.Q4_K_M.gguf | Q4_K_M | 20 GB | Chat version |
GGUF/...Fable-opus4.8-CHAT.f16.gguf | F16 | 65 GB | Chat F16 |
🧬 LoRA Adapters
| Adapter | Description | Base |
|---|---|---|
adapter_qwen3.6_35b_opus4.8_sft/ 🔥 | Opus 4.8 reasoning SFT | Huihui 35B |
adapter_qwen3.6_35b_fable_sft/ | Fable SFT (perfect-v1) | Huihui 35B |
adapter/ | Original Fable SFT | Qwen 35B |
🚀 Usage
llama.cpp
./llama-cli -m GGUF/Qwen-35B-A3B-abliterated-AgentWorld-fable-opus4.8.Q4_K_M.gguf \
--temp 0.6 --top-k 25 --top-p 0.9 --min-p 0.1 --repeat-penalty 1.15
Python
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"hotdogs/qwen35b-a3b-fable-sft-abliterated", subfolder="model_agent"
)
📊 Training Pipeline
Qwen3.6-35B-A3B-Base (MoE, 256 experts)
│
├── Huihui abliterated (uncensored)
│
├── Fable SFT (perfect-v1, 3,376 rows)
│
├── Opus 4.8 SFT (6,956 rows, Claude reasoning) 🔥
│
└── AgentWorld (tool use, environment simulation) 🏆
Built with ❤️ by UKA - 18yo coder & cybersecurity expert
💖 Support / โปรดสนับสนุน
If you find this model useful, please consider supporting my work!
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Thank you for your support! 🙏✨
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Built with ❤️ by UKA — 18-year-old coder & cybersecurity expert