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Added usage of: unsloth/Qwen3.8-27B-GGUF but it is slow on a 32GB laptop

README.md

@@ -101,6 +101,7 @@ license: apache-2.0
         *   [MoT AI: unsloth/gemma-4-26B-A4B-it-GGUF](https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF/tree/main) — efficient  
         *   [Dense AI: unsloth/gemma-4-e2b-it-GGUF](https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF/tree/main) — fast debug cycles  
         *   [Dense AI: unsloth/gemma-4-e4b-it-GGUF](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF/tree/main) — balanced  
+        *   [Dense AI (works with Linux RAM Swap Memory): unsloth/Qwen3.8-27B-GGUF](https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/tree/main) — slow on laptop
     2. Place them in the `ai/` folder inside the repo.  
     3. Then copy the GGUF ai name and paste it in the config.py under `_ALPHA_INTELLIGENCE_TO_LOAD` and choose `_MODEL_TYPE: "qwen" OR "gemma"`**  
 

config.py

@@ -147,9 +147,11 @@ METATRON_TO_LOAD: list = [
 # ── AI models recommended ────────────────────────────────────────────────────────
 """
 Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf
-gemma-4-E2B-it-UD-Q4_K_XL.gguf
-gemma-4-E4B-it-UD-Q4_K_XL.gguf
 gemma-4-26B-A4B-it-UD-Q6_K_XL.gguf
+gemma-4-E4B-it-UD-Q4_K_XL.gguf
+gemma-4-E2B-it-UD-Q4_K_XL.gguf
+# Dense 27b: Large on 32GB laptop. Less than 1token/sec
+Qwen3.8-27B-UD-Q6_K_M.gguf
 """
 # ── AI to load for each hemisphere ───────────────────────────────────────────────
 _ALPHA_INTELLIGENCE_TO_LOAD: dict = {