chore(model-gallery): ⬆️ update checksum (#5422)

⬆️ Checksum updates in gallery/index.yaml

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>

gallery/index.yaml

@@ -369,7 +369,7 @@
   files:
     - filename: mlabonne_Qwen3-14B-abliterated-Q4_K_M.gguf
       uri: huggingface://bartowski/mlabonne_Qwen3-14B-abliterated-GGUF/mlabonne_Qwen3-14B-abliterated-Q4_K_M.gguf
-      sha256: 225ab072da735ce8db35dcebaf24e905ee2457c180e501a0a7b7d1ef2694cba8
+      sha256: 3fe972a7c6e847ec791453b89a7333d369fbde329cbd4cc9a4f0598854db5d54
 - !!merge <<: *qwen3
   name: "mlabonne_qwen3-8b-abliterated"
   urls:
@@ -382,8 +382,8 @@
       model: mlabonne_Qwen3-8B-abliterated-Q4_K_M.gguf
   files:
     - filename: mlabonne_Qwen3-8B-abliterated-Q4_K_M.gguf
-      sha256: 605d17fa8d4b3227e4848c2198616e9f8fb7e22ecb38e841b40c56acc8a5312d
       uri: huggingface://bartowski/mlabonne_Qwen3-8B-abliterated-GGUF/mlabonne_Qwen3-8B-abliterated-Q4_K_M.gguf
+      sha256: 361557e69ad101ee22b1baf427283b7ddcf81bc7532b8cee8ac2c6b4d1b81ead
 - !!merge <<: *qwen3
   name: "mlabonne_qwen3-4b-abliterated"
   urls:
@@ -7447,17 +7447,7 @@
   urls:
     - https://huggingface.co/a-m-team/AM-Thinking-v1
     - https://huggingface.co/bartowski/a-m-team_AM-Thinking-v1-GGUF
-  description: |
-      AM-Thinking‑v1, a 32B dense language model focused on enhancing reasoning capabilities. Built on Qwen 2.5‑32B‑Base, AM-Thinking‑v1 shows strong performance on reasoning benchmarks, comparable to much larger MoE models like DeepSeek‑R1, Qwen3‑235B‑A22B, Seed1.5-Thinking, and larger dense model like Nemotron-Ultra-253B-v1.
-      benchmark
-      🧩 Why Another 32B Reasoning Model Matters?
-
-      Large Mixture‑of‑Experts (MoE) models such as DeepSeek‑R1 or Qwen3‑235B‑A22B dominate leaderboards—but they also demand clusters of high‑end GPUs. Many teams just need the best dense model that fits on a single card. AM‑Thinking‑v1 fills that gap while remaining fully based on open-source components:
-
-          Outperforms DeepSeek‑R1 on AIME’24/’25 & LiveCodeBench and approaches Qwen3‑235B‑A22B despite being 1/7‑th the parameter count.
-          Built on the publicly available Qwen 2.5‑32B‑Base, as well as the RL training queries.
-          Shows that with a well‑designed post‑training pipeline ( SFT + dual‑stage RL ) you can squeeze flagship‑level reasoning out of a 32 B dense model.
-          Deploys on one A100‑80 GB with deterministic latency—no MoE routing overhead.
+  description: "AM-Thinking‑v1, a 32B dense language model focused on enhancing reasoning capabilities. Built on Qwen 2.5‑32B‑Base, AM-Thinking‑v1 shows strong performance on reasoning benchmarks, comparable to much larger MoE models like DeepSeek‑R1, Qwen3‑235B‑A22B, Seed1.5-Thinking, and larger dense model like Nemotron-Ultra-253B-v1.\nbenchmark\n\U0001F9E9 Why Another 32B Reasoning Model Matters?\n\nLarge Mixture‑of‑Experts (MoE) models such as DeepSeek‑R1 or Qwen3‑235B‑A22B dominate leaderboards—but they also demand clusters of high‑end GPUs. Many teams just need the best dense model that fits on a single card. AM‑Thinking‑v1 fills that gap while remaining fully based on open-source components:\n\n    Outperforms DeepSeek‑R1 on AIME’24/’25 & LiveCodeBench and approaches Qwen3‑235B‑A22B despite being 1/7‑th the parameter count.\n    Built on the publicly available Qwen 2.5‑32B‑Base, as well as the RL training queries.\n    Shows that with a well‑designed post‑training pipeline ( SFT + dual‑stage RL ) you can squeeze flagship‑level reasoning out of a 32 B dense model.\n    Deploys on one A100‑80 GB with deterministic latency—no MoE routing overhead.\n"
   overrides:
     parameters:
       model: a-m-team_AM-Thinking-v1-Q4_K_M.gguf